Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20072008 Program
See http://www.ima.umn.edu/20072008 for a full description of the 20072008 program on Mathematics of Molecular and Cellular Biology.
Opportunities at the IMA: There is still time to apply for one of the IMA General Membership, New Directions Professorship or Postdoctoral Fellowship positions in connection with the 20082009 thematic program: Mathematics and Chemistry. The deadline for applying for the postdoc positions is January 4, 2008 and the deadline for the New Directions Research Professorships is January 15, 2008. You can find the applications for these positions at our Applications site.
IMA is seeking a new associate director: The IMA is looking for a new associatae director to begin September 1, 2008.
New Directions Short Course: The IMA is currently accepting applications for the 2008 New Directions Short Course  Mathematical Neuroscience, June 16  27, 2008, taught by G. Bard Ermentrout and Jonathan E. Rubin.
Neuroscience is becoming increasingly quantitative and the need for theoreticians interested in collaborating with experimental neuroscientists is on the rise. The proposed short course will introduce the participants to basic concepts in cellular and systems neuroscience with an emphasis on the underlying equations and dynamics. Participation is by application only. Application deadline: April 1, 2008.
All Day  New Year's Day. The IMA is closed. 
11:15a12:15p  An experimental perspective on miscibility phase transitions and their biological applications  Benjamin Stottrup (Augsburg College)  Lind Hall 409  PS 
11:15a12:15p  The solution of the distance geometry problem for protein modeling  Zhijun Wu (Iowa State University)  Lind Hall 409  MMCB 
8:15a8:50a  Registration and coffee  EE/CS 3176  T1.1011.08  
8:50a9:00a  Welcome to the IMA  Douglas N. Arnold (University of Minnesota)  EE/CS 3180  T1.1011.08 
9:00a10:00a  Lecture 1: Fundamental forces and molecular architecture  Michael Levitt (Stanford University)  EE/CS 3180  T1.1011.08 
10:00a10:30a  Break  EE/CS 3176  T1.1011.08  
10:30a11:30a  The geometry of biomolecular solvation. Part 1: Hydrophobicity  Patrice Koehl (University of California)  EE/CS 3180  T1.1011.08 
11:30a1:30p  Lunch  T1.1011.08  
1:30p2:30p  Part 2: Electrostatics  Patrice Koehl (University of California)  EE/CS 3180  T1.1011.08 
2:30p3:00p  Break  EE/CS 3176  T1.1011.08  
3:00p4:00p  Lecture 2: Simulating molecular motion  Michael Levitt (Stanford University)  EE/CS 3180  T1.1011.08 
4:00p4:30p  Second chances  Patrice Koehl (University of California) Michael Levitt (Stanford University)  EE/CS 3180  T1.1011.08 
8:30a9:00a  Coffee  EE/CS 3176  T1.1011.08  
9:00a10:00a  Part 3: Protein shape descriptors  Patrice Koehl (University of California)  EE/CS 3180  T1.1011.08 
10:00a10:30a  Break  EE/CS 3176  T1.1011.08  
10:30a11:30a  Lecture 3: Simulating protein folding  Michael Levitt (Stanford University)  EE/CS 3180  T1.1011.08 
11:30a2:00p  Lunch  T1.1011.08  
2:00p2:30p  Geometric simulation of protein flexibility  Ileana Streinu (Smith College)  EE/CS 3180  T1.1011.08 
2:30p3:00p  Knot theory and proteins  Isabel K. Darcy (University of Iowa)  EE/CS 3180  T1.1011.08 
3:00p3:30p  Break  EE/CS 3176  T1.1011.08  
3:30p4:00p  An introduction to multigrid techniques  Bobby Philip (Los Alamos National Laboratory)  EE/CS 3180  T1.1011.08 
All Day  Morning Theme: Force fields & simulations Afternoon Theme: Design, predictions, and optimization  W1.1418.08  
8:15a9:00a  Registration and coffee  EE/CS 3176  W1.1418.08  
9:00a9:15a  Welcome to the IMA  Douglas N. Arnold (University of Minnesota)  EE/CS 3180  W1.1418.08 
9:15a9:45a  Assessing the performance of PoissonBoltzmann continuum solvation models  Nathan A. Baker (Washington University School of Medicine)  EE/CS 3180  W1.1418.08 
9:50a10:20a  Simple models for simulating replica exchange simulations of protein folding and binding  Ronald M. Levy (Rutgers University)  EE/CS 3180  W1.1418.08 
10:25a10:55a  Alphahelical topology and tertiary structure prediction of globular proteins  Christodoulos A. Floudas (Princeton University)  EE/CS 3180  W1.1418.08 
10:55a11:40a  Coffee  EE/CS 3176  W1.1418.08  
11:40a12:10p  Challenges in generation of conformational ensembles for peptides and small proteins  Carlos L. Simmerling (SUNY)  EE/CS 3180  W1.1418.08 
12:15p12:30p  What is a transition path?  Robert D. Skeel (Purdue University)  EE/CS 3180  W1.1418.08 
12:30p2:00a  Lunch  W1.1418.08  
2:00p2:30p  Engineering protein structure and function with theoretical protein design  Jeffery G. Saven (University of Pennsylvania)  EE/CS 3180  W1.1418.08 
2:35p2:50p  Modeling ensembles of transmembrane betabarrel proteins  Jérôme Waldispühl (Massachusetts Institute of Technology)  EE/CS 3180  W1.1418.08 
2:55p3:25p  Coffee  EE/CS 3176  W1.1418.08  
3:25p3:40p  Exact methods for simplified protein models  Rolf Backofen (AlbertLudwigsUniversität Freiburg)  EE/CS 3180  W1.1418.08 
3:40p4:10p  Discussion forum  EE/CS 3180  W1.1418.08  
4:10p4:25p  Group Photo  W1.1418.08  
4:30p6:00p  Reception and Poster Session  Lind Hall 400  W1.1418.08  
Topological analysis of DNAbinding protein complexes  Soojeong Kim (University of Iowa)  
Overall rotation due to internal motions in the Nbody dynamics of protein molecules  Florence J. Lin (University of Southern California)  
Simulating protein conformations by a geometric model  Antonio Mucherino (Seconda Università di Napoli)  
Protein folding by ZAM & FRODA  Sefika Banu Ozkan (Arizona State University)  
Computing conformational free energy by deactivated morphing  Sanghyun Park (Argonne National Laboratory)  
Minima Hopping within an allatom framework for biomolecular structure prediction  Shantanu Roy (Universität Basel)  
Investigation of the unfolding pathway of Cyt2Aa2 toxin  Anchanee Sangcharoen (Mahidol University )  
Configurationdependent diffusion can shift the kinetic transition state and barrier height of protein folding  Jin Wang (SUNY)  
Mathematical methods for implicit solvent models  Guowei Wei (Michigan State University)  
The Dynamic Nature of the Folded and Unfolded States of the Villin Headpiece Helical Subdomain: An ultrafast folding protein  Lauren Wickstrom (SUNY)  
A novel method for protein folding shape description  Jiaan Yang (MicrotechNano)  
Temperature dependence of Trpcage folding kinetics from replica exchange simulations  Sichun Yang (University of Chicago) 
All Day  Morning Theme: Systems modeling Afternoon Theme: Conformational exploration, routes, and searching  W1.1418.08  
8:30a9:00a  Coffee  EE/CS 3176  W1.1418.08  
9:00a9:30a  Simulation methods for stochastic chemical systems that arise from a random time change representation  David F Anderson (University of Wisconsin)  EE/CS 3180  W1.1418.08 
9:35a10:05a  Coarsegrained parameterizations of biomolecular systems  Peter R. Kramer (Rensselaer Polytechnic Institute)  EE/CS 3180  W1.1418.08 
10:10a10:40a  From chemical reaction systems to cellular states: A computational approach  Hong Qian (University of Washington)  EE/CS 3180  W1.1418.08 
10:40a11:10a  Coffee  EE/CS 3176  W1.1418.08  
11:10a11:40a  Computational experiments in coarsegraining atomistic simulations  Yannis G. Kevrekidis (Princeton University)  EE/CS 3180  W1.1418.08 
11:40a1:30p  Lunch  W1.1418.08  
1:30p2:00p  Using motion planning to study molecular motions  Nancy M. Amato (Texas A & M University)  EE/CS 3180  W1.1418.08 
2:05p2:35p  Geometrical methods for the efficient exploration of protein conformation space  Evangelos A. Coutsias (University of New Mexico)  EE/CS 3180  W1.1418.08 
2:40p3:10p  Network models for protein dynamics and allostery: Application to GroELGroES  Ivet Bahar (University of Pittsburgh)  EE/CS 3180  W1.1418.08 
3:10p3:40p  Coffee  EE/CS 3176  W1.1418.08  
3:40p4:10p  Structural control of motions?  Robert L. Jernigan (Iowa State University)  EE/CS 3180  W1.1418.08 
4:15p4:45p  The network of sequence flow between protein structures  Ron Elber (University of Texas)  EE/CS 3180  W1.1418.08 
4:50p5:20p  Discussion forum  EE/CS 3180  W1.1418.08 
All Day  Morning Theme: Nonequilibrium & single molecules Afternoon Theme: Nucleic acids & genomes  W1.1418.08  
8:30a9:00a  Coffee  EE/CS 3176  W1.1418.08  
9:00a9:30a  Exploring global energy landscape of lattice protein models via Monte Carlo methods  Samuel Kou (Harvard University)  EE/CS 3180  W1.1418.08 
9:35a10:05a  Mathematical models of folded and unfolded protein ensembles  Gregory S. Chirikjian (Johns Hopkins University)  EE/CS 3180  W1.1418.08 
10:10a10:25a  Current issues in understanding complex biological networks  Hans G. Othmer (University of Minnesota)  EE/CS 3180  W1.1418.08 
10:30a11:15a  Coffee  EE/CS 3176  W1.1418.08  
11:15a11:45a  Improving nonequilibrium free energy estimates by enhancing phase space overlap  Christopher Jarzynski (University of Maryland)  EE/CS 3180  W1.1418.08 
11:45a2:00a  Lunch  W1.1418.08  
2:00p2:30p  The electrostatic free energy landscape for nucleic acid folding  beyond the PoissonBoltzmann equation  ShiJie Chen (University of Missouri)  EE/CS 3180  W1.1418.08 
2:35p2:50p  Annotated tertiary interactions in RNA structures reveal new interactions, correlations in motifs and composite motifs  Christian E. Laing (New York University)  EE/CS 3180  W1.1418.08 
2:50p3:20p  Coffee  EE/CS 3176  W1.1418.08  
3:20p3:35p  Mapping evolutionary pathways of HIV1 drug resistance using conditional selection pressure  Christopher J. Lee (University of California)  EE/CS 3180  W1.1418.08 
3:40p4:10p  Discussion forum  EE/CS 3180  W1.1418.08 
All Day  Morning Theme: Protein folding & lowresolution modeling Afternoon Theme: Protein design and interactions  W1.1418.08  
8:30a9:00a  Coffee  EE/CS 3176  W1.1418.08  
9:00a9:30a  Free energies and kinetics of protein folding from coarse master equations  Gerhard Hummer (National Institutes of Health (NIH))  EE/CS 3180  W1.1418.08 
9:35a10:05a  Entropic and enthalpic barriers in cooperative protein folding  HueSun Chan (University of Toronto)  EE/CS 3180  W1.1418.08 
10:10a10:40a  The energy landscape for folding and molecular motors  José Nelson Onuchic (University of California, San Diego)  EE/CS 3180  W1.1418.08 
10:40a11:25a  Coffee  EE/CS 3176  W1.1418.08  
11:25a11:55a  Transition states in protein folding  Thomas Weikl (Max Planck Institute for Colloids and Interfaces)  EE/CS 3180  W1.1418.08 
12:00p12:15p  Probing the diversity of unfolding pathways by simulated thermal denaturation  Andrew J. Rader (Indiana UniversityPurdue University)  EE/CS 3180  W1.1418.08 
12:30p2:00a  Lunch  W1.1418.08  
2:00p2:30p  Structurebased maximal affinity model predicts smallmolecule druggability  Alan C. Cheng (Amgen Cambridge Research Center)  EE/CS 3180  W1.1418.08 
2:35p2:50p  Cluster optimization in protein docking  Julie C. Mitchell (University of Wisconsin)  EE/CS 3180  W1.1418.08 
2:50p3:20p  Coffee  EE/CS 3176  W1.1418.08  
3:20p3:50p  Multistage optimization for proteinprotein docking  Sandor Vajda (Boston University)  EE/CS 3180  W1.1418.08 
3:55p4:25p  Discussion forum  EE/CS 3180  W1.1418.08  
6:30p8:30p  Workshop Dinner  Caspian Bistro 2418 University Ave SE Minneapolis, MN 55414 6126231133 
W1.1418.08 
All Day  Theme: Big Simulations of atomically detailed models  W1.1418.08  
8:30a9:00a  Coffee  EE/CS 3176  W1.1418.08  
9:00a9:30a  The limitations of temperature replica exchange (TREMD) for protein folding  Jed W. Pitera (IBM Research Division)  EE/CS 3180  W1.1418.08 
9:35a10:05a  Simulations on BlueGene of a fast folding mutant of lambda(685)  William Swope (IBM)  EE/CS 3180  W1.1418.08 
10:05a10:50a  Coffee  EE/CS 3176  W1.1418.08  
10:50a11:20a  Simulations of peptide folding and dynamics  Krzysztof Kuczera (University of Kansas)  EE/CS 3180  W1.1418.08 
11:25a11:55a  Discussion forum  EE/CS 3180  W1.1418.08  
11:55a12:10p  Concluding remarks  Ken A. Dill (University of San Francisco) Sorin Istrail (Brown University) Michael Levitt (Stanford University)  EE/CS 3180  W1.1418.08 
2:30p3:20p  Protein folding physics and computational modeling  Ken A. Dill (University of San Francisco)  402 Walter Library  Compbio 
All Day  Martin Luther King holiday. The IMA is closed. 
11:15a12:15p  Brownian dynamics simulations of polymer behavior in nanofluidic and microfluidic systems  Satish Kumar (University of Minnesota)  Lind Hall 409  PS 
11:15a12:15p  Analyzing the proteinprotein interaction network  Robert L. Jernigan (Iowa State University)  Lind Hall 409  MMCB 
11:15a12:15p  Syzygies of algebraic varieties  Milena Hering (University of Minnesota)  Lind Hall 409  PS 
11:15a12:15p  Combinatorial rigidity and the molecular conjecture  Brigitte Servatius (Worcester Polytechnic Institute)  Lind Hall 409  MMCB 
Event Legend: 

Compbio  Computational Biology Seminar 
MMCB  Mathematics of Molecular and Cellular Biology Seminar 
PS  IMA Postdoc Seminar 
T1.1011.08  Mathematics of Proteins 
W1.1418.08  Protein Folding 
Nancy M. Amato (Texas A & M University)  Using motion planning to study molecular motions 
Abstract: Protein motions, ranging from molecular flexibility to largescale conformational change, play an essential role in many biochemical processes. For example, some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. Despite the explosion in our knowledge of structural and functional data, our understanding of protein movement is still very limited because it is difficult to measure experimentally and computationally expensive to simulate. In this talk we describe a method we have developed for modeling protein motions that is based on probabilistic roadmap methods (PRM) for motion planning. Our technique yields an approximate map of a protein's potential energy landscape and can be used to generate transitional motions of a protein to the native state from unstructured conformations or between specified conformations. We also describe new analysis tools that enable us to extract kinetics information, such as folding rates or to identify and study the folding core. For example, we show how our mapbased tools for modeling and analyzing folding landscapes can capture subtle folding differences between protein G and its mutants, NuG1 and NuG2. More information regarding our work, including an archive of protein motions generated with our technique, are available from our protein folding server: http://parasol.tamu.edu/foldingserver/  
David F Anderson (University of Wisconsin)  Simulation methods for stochastic chemical systems that arise from a random time change representation 
Abstract: Chemical reaction systems with a low to moderate number of molecules are typically modeled as continuous time Markov chains. More explicitly, the state of the system is modeled as a vector giving the number of molecules of each species present with each reaction modeled as a possible transition for the state. The model for the kth reaction is determined by a vector of inputs specifying the number of molecules of each chemical species that are consumed in the reaction, a vector of outputs specifying the number of molecules of each species that are created in the reaction and a function of the state that gives the rate at which the reaction occurs. To understand how the probability distribution of the system changes in time one could attempt to solve the Chemical Master Equation (CME), however this is typically an extremely difficult task. Therefore, simulation methods such as the Stochastic Simulation Algorithm (Gillespie Algorithm) and tauleaping have been developed so as to approximate the probability distribution of the system via Monte Carlo methods. I will demonstrate how using a random time change representation for these models leads naturally to simulation methods that achieve greater efficiency and stability than existing methods.  
Rolf Backofen (AlbertLudwigsUniversität Freiburg)  Exact methods for simplified protein models 
Abstract: Due to the inherent complexity of the associated problems, investigations of the basic principles of protein folding and evolution are usually restricted to simplified protein models. Our group has developed methods and programs for exact and complete solving of problems typical for studies using HPlike 3D lattice protein models. Addressed tasks are the prediction of globally optimal and listing of suboptimal structures, sequence design, neutral network exploration, and degeneracy computation. The used methods are based on fast and nonheuristic techniques (constraint programming) instead of following stochastic approaches, which are not capable of answering many fundamental questions. Thus, we are able to find optimal structure for HPsequences of length greater than 200, including a proof of optimality. We have used these methods to find unique folding sequences, to investigate neutral nets and to design lowdegenerated sequences for given structures.  
Ivet Bahar (University of Pittsburgh)  Network models for protein dynamics and allostery: Application to GroELGroES 
Abstract: Two groups of studies recently proved to provide insights into such intrinsic, structureinduced effects: elastic network models that permit us to visualize the cooperative changes in conformation that are most readily accessible near native state conditions, and informationtheoretic approaches that elucidate the most efficient pathways of signal transmission favored by the overall architecture. Using a combination of these two approaches, we highlight, by way of application to the bacterial chaperonin complex GroELGroES, how the most cooperative modes of motion play a role in mediating the propagation of allosteric signals. A functional coupling between the global dynamics sampled under equilibrium conditions and the signal transduction pathways inherently favored by network topology appears to control allosteric effects.  
Nathan A. Baker (Washington University School of Medicine)  Assessing the performance of PoissonBoltzmann continuum solvation models 
Abstract: Continuum electrostatics methods have become increasingly popular due to their ability to provide approximate descriptions of solvation energies and forces without expensive sampling required by explicit solvent models. In particular, the PoissonBoltzmann equation (PBE) provides electrostatic potentials, solvation energies, and forces by modeling the solvent as a featureless, dielectric material, and the mobile ions as a continuous distribution of charge. In this talk, I will provide a review of PBEbased and new apolar continuum solvation methods as well as approaches for assessing their performance by comparison with explicit solvent simulations. In particular, I will focus on the ability of these continuum solvent models to describe solvation forces on proteins and nucleic acids and will comment on strengths and weaknesses of these implicit solvent approaches.  
HueSun Chan (University of Toronto)  Entropic and enthalpic barriers in cooperative protein folding 
Abstract: Many small singledomain proteins undergo cooperative, switchlike folding/unfolding transitions with very low populations of intermediate, i.e., partially folded, conformations. The phenomenon of cooperative folding is not readily accounted for by common notions about driving forces for folding. I will discuss how common protein chain models with pairwise additive interactions are insufficient to account for the folding cooperativity of natural proteins, and how models with nonadditive localnonlocal coupling may rationalize cooperative folding rates that are well correlated with native topology. The traditional formulation of folding transition states entails a macroscopic folding free energy barrier with both enthalpic and entropic components. I will explore the microscopic origins of these thermodynamic signatures in terms of conformational entropy as well as desolvation (dewetting) effects. Notably, the existence of significant enthalpic folding barriers raises fundamental questions about the validity of the funnel picture of protein folding, because such enthalpic barriers appear to imply that there are substantial uphill moves along a microscopic folding trajectory. Using results from extensive atomic simulations, I will show how the paradox can be resolved by a dramatic entropyenthalpy compensation at the ratelimiting step of folding. In this perspective, the height of the enthalpic barrier is seen as related to the degree of cooperativity of the folding process.  
ShiJie Chen (University of Missouri)  The electrostatic free energy landscape for nucleic acid folding  beyond the PoissonBoltzmann equation 
Abstract: Multivalent ions (Mg2+) in RNA tertiary structure folding can be strongly correlated and thus cannot be treated by meanfield theories such as the PoissonBoltzmann equation. We recently developed a statistical mechanical model (TBI) to account for ion correlation by considering ensemble of discrete ion distributions. Experimental tests show that the TBI model gives improved predictions for nucleic folding folding stability over the PoissonBoltzmann equation, which generally underestimates the (multivalent) iondependent folding stability due to ignoring the ion correlation. Using the TBI theory, we investigate the folding energy landscape for a simple system with looptethered short DNA helices and find that Na+ and Mg2+ play contrasting roles in helix–helix assembly. High [Na+] (>0.3 M) causes a reduced helix–helix electrostatic repulsion and a subsequent disordered packing of helices, while Mg2+ of concentration > 1 mM is predicted to induce a more compact and ordered helix–helix packing. Mg2+ is much more efficient in causing nucleic acid compaction and is predicted to induce a collapse transition around 1mM of [Mg2+].  
Alan C. Cheng (Amgen Cambridge Research Center)  Structurebased maximal affinity model predicts smallmolecule druggability 
Abstract: Lead generation is a major hurdle in smallmolecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for druglike properties. It would be valuable to identify these lessdruggable targets before incurring substantial expenditure and effort. We discovered that a modelbased approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a druglike molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using highthroughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tacking difficult targets. I will also discuss our approach to calculating protein curvature and some potential computational approaches for difficult targets.  
Gregory S. Chirikjian (Johns Hopkins University)  Mathematical models of folded and unfolded protein ensembles 
Abstract: This talk (and a related poster) describes Liegrouptheoretic techniques that can be applied in the analysis and modeling of protein conformations. Three topics are covered: (1) Conformational transitions between two known end states; (2) proper normalization of helixhelix crossing angle data in the PDB; (3) models of the conformational entropy of the ensemble of unfolded polypeptide conformations. Using the concept of convolution on the group of rigidbody motions, the probability density of position and orientation of the distal end of a polypeptide chain is obtained by convolving the distributions for shorter segments that make up the chain. This methodology can also be used in the analysis of loop entropy in folded proteins as well as the ensemble of unfolded conformations.  
Evangelos A. Coutsias (University of New Mexico)  Geometrical methods for the efficient exploration of protein conformation space 
Abstract: The geometrical problem of protein folding, especially in its later stages, is composed of two types of freedom, the full torsional flexibility of loops connecting nearly rigid structural pieces (helices, betasheets etc), and the relative placing of such pieces. We present a method for sampling the feasible conformations of protein loops, based on Triaxial Loop Closure (TLC), a simple and highly efficient inverse kinematic (IK) method for solving the loop closure problem. TLC is easily extended to incorporate additional (i.e. position, orientation) constraints, or more general geometrical conditions. Due to its relative simplicity TLC compares favorably to more general IK robotics algorithms, both in robustness and in speed. We consider two applications: (i) An algorithm for the rapid sampling of the conformations of protein loops including three or more residues which uses quasirandom Sobol sampling of the Ramachandran regions. Ideas akin to Delauney triangulation may be employed to ensure sampling loop shapespace at a desired density. (ii) An efficient method for the sequential assembly of helical proteins via maximal hydrophobic packing. The geometrical problem of considering all possible mutual arrangements of a system of helices that are compatible with closing the corresponding loops is already too large to sample directly. We introduced a measure of hydrophobic packing by seeking to minimize the radius of gyration of the hydrophobic residues. Thus, we sequentially assemble the helices, by sampling relative orientations of pairs of them that bring specified hydrophobic residues in proximity. For the best candidates, in terms of energy and hydrophobig radius of gyration, the loops are closed using the algorithm in (i) and another helix is added to the assembly, always seeking to maximizing hydrophobic contact. We tested this iterative assembly method on 26 helical proteins each containing up to 5 helices. The method heavily samples nativelike conformations. The average RMSDtonative of the best conformations for the 18 helix bundle proteins that have 2 or 3 helices is less than 2 Angstroms with slightly worse errors for proteins containing more helices.  
Isabel K. Darcy (University of Iowa)  Knot theory and proteins 
Abstract: Some proteins contain locally knotted structures. Many algorithms have been developed in order to detect local knotting in protein conformations. In some cases these algorithms are used to rule out computationally generated structures containing local knots as knotted proteins are rare. However, there are several types of proteins which contain local knots. I will give an overview of knotted proteins, the various methods used to define a local knot in a protein, and their potential significance.  
Ken A. Dill (University of San Francisco)  Protein folding physics and computational modeling 
Abstract: Protein molecules are linear polymer chains that fold up into particular 3dimensional native structures to perform the functions of the cell. We are interested in: (a) the folding code — how the 1dimensional monomer sequence encodes the 3dimensional fold, (b) the folding problem — how the protein searches and finds it's native structure so quickly, and (c) a folding algorithm — a computational strategy for predicting the native structure from the amino acid sequence.  
Ron Elber (University of Texas)  The network of sequence flow between protein structures 
Abstract: Sequencestructure relationships in proteins are highly asymmetric since many sequences fold into relatively few structures. What is the number of sequences that fold into a particular protein structure? Is it possible to switch between stable protein folds by point mutations? To address these questions we compute a directed graph of sequences and structures of proteins, which is based on experimentally determined protein shapes. Two thousand and sixty experimental structures from the Protein Data Bank were considered, providing a good coverage of fold families. The graph is computed using an energy function that measures stability of a sequence in a fold. A node in the graph is an experimental structure (and the computationally matching sequences). A directed and weighted edge between nodes A and B is the number of sequences of A that switch to B because the energy of B is lower. The directed graph is highly connected at native energies with ³sinks² that attract many sequences from other folds. The sinks are rich in beta sheets. The indegrees of a particular protein shape correlates with the number of sequences that matches this shape in empirically determined genomes. Properties of strongly connected components of the graph are correlated with protein length and secondary structure. Joint work with Leonid Meyerguz and Jon Kleinberg  
Christodoulos A. Floudas (Princeton University)  Alphahelical topology and tertiary structure prediction of globular proteins 
Abstract: Joint work with S. R. McAllister. The protein folding question has developed over the past four decades as one of the most challenging and potentially rewarding problems in computational biology. Three general classes of algorithms have emerged, based on the techniques of comparative modeling, fold recognition, and first principles methods. For a detailed summary of protein structure prediction methods, the reader is directed to two recent reviews [1,2]. Within the field of protein structure prediction, the packing of alphahelices has been one of the more difficult problems. The use of distance constraints and topology predictions can be highly useful for reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy. We present a novel first principles framework to predict the structure of alphahelical proteins. Given the location of the alphahelical regions, a mixedinteger linear optimization model maximizes the interhelical residue contact probabilities to generate distance restraints between alphahelices [3]. Two levels of this formulation allow the prediction of both ``primary'' contacts between a helical pair as well as the prediction of ``wheel'' contacts, one helical turn beyond the primary contacts. These predictions are subject to a number of mathematical constraints to disallow sets of contacts that cannot be achieved by a folded protein. The interhelical contact prediction for alphahelical proteins was evaluated on 26 proteins, where it identified an average contact distance below 11.0 Angstroms for the entire set. A related optimizationbased approach is proposed for the prediction of alphahelical contacts in mixed alpha/beta proteins [4]. This contact prediction is based on the maximization of the number and hydrophobicity of hydrophobic interactions. The allowable sets of contacts is restricted based on knowledge or prediction of the betasheet topology and a number of distance geometry rules and constraints. The interhelical contact prediction for alpha/beta proteins was evaluated on 12 test proteins, where it identified an average contact distance below 11.0 Angstroms for 11 of these proteins. The distance restraints from the interhelical contacts are then used to restrict the feasible space of the protein during the prediction of the tertiary structure using a hybrid optimization algorithm [5,6]. This tertiary structure prediction approach combines torsion angle dynamics and rotamer optimization with a deterministic global optimization technique (alphaBB) and a stochastic optimization technique (conformational space annealing) to minimize a detailed atomisticlevel energy function. The tertiary structure prediction results are promising and are highlighted by the exciting, nearnative blind prediction of a de novo designed 4helix bundle protein. [1] Floudas CA, Fung HK, McAllister SR, Monningmann M, and Rajgaria R. Advances in Protein Structure Prediction and De Novo Protein Design: A Review. Chem Eng Sci. 2006;61: 966988. [2] Floudas CA. Computational Methods in Protein Structure Prediction. Biotechnol Bioeng. 2007;97:207213. [3] McAllister SR, Mickus BE, Klepeis JL, and Floudas CA. A Novel Approach for AlphaHelical Topology Prediction in Globular Proteins: Generation of Interhelical Restraints. Prot Struct Funct Bioinf. 2006;65:930952. [4] McAllister SR and Floudas CA. Alphahelical Residue Contact Prediction in Mixed Alpha/Beta Proteins Using MixedInteger Linear Programming. In preparation, 2007. [5] Klepeis JL and Floudas CA. ASTROFOLD: A Combinatorial and Global Optimization Framework for Ab Initio Prediction of Threedimensional Structures of Proteins from the Amino Acid Sequence. Biophys J, 2003;85:21192146. [6] McAllister SR and Floudas CA. An Improved Hybrid Global Optimization Method for Protein Tertiary Structure Prediction. In preparation, 2007.  
Milena Hering (University of Minnesota)  Syzygies of algebraic varieties 
Abstract: The embedding of a projective variety in projective space can be described as the common zero locus of homogeneous polynomials in a polynomial ring. I will introduce Green's property $N_p$ and the Koszul property for embeddings of projective varieties, and present some results for toric varieties, as well as for the GIT quotient of points in $P^1$ modulo the $SL(2,C)$ action.  
Gerhard Hummer (National Institutes of Health (NIH))  Free energies and kinetics of protein folding from coarse master equations 
Abstract: Coarse master equations and diffusion models provide powerful tools to study the equilibrium and nonequilibrium properties of molecular systems. Maximum likelihood and Bayesian approaches have been used successfully to construct such models from the observed dynamics projected onto discrete and continuous lowdimensional subspaces. By using a Green'sfunction based formalism, issues arising from fast nonMarkovian dynamics can be circumvented. The general formalism for the construction of coarse master equations and diffusion models will be illustrated with applications to peptide and protein folding.  
Christopher Jarzynski (University of Maryland)  Improving nonequilibrium free energy estimates by enhancing phase space overlap 
Abstract: While equilibrium free energy differences can be obtained from simulations of nonequilibrium systems, such estimates are often hampered by convergence difficulties similar to those that plague traditional perturbation methods. In the nonequilibrium context, dissipation is the culprit behind poor convergence. I will discuss a general strategy for addressing these difficulties, in which dissipation is reduced by adding nonphysical terms to the equations of motion. When these terms are chosen appropriately, the efficiency of the free energy estimate can improve dramatically. After sketching the general features of this strategy, I will illustrate its application using specific examples, and will discuss its relation to other strategies that are similar in spirit.  
Robert L. Jernigan (Iowa State University)  Structural control of motions? 
Abstract: Are protein motions limited because of a higher level of cooperativity than indicated by usual potentials? Recently derived fourbody coarsegrained potentials show improved performance in threading over pairwise potentials. Their apparently increased extent of cooperativity is consistent with the high level of control of motions manifested in the elastic network model computations. The elastic network models are providing strong evidence that proteins control their functional motions through their most important slowest domain motions. A major strength of these models appears to be their ability to represent the structures as highly cohesive rubbery materials, and much evidence supporting them has now accumulated. Such models exhibit strong control over their motions, arising principally from the shape, sometimes even including control of the motions of surface loops by domain motions and the motion of reactive atoms at enzyme active sites. These highly coordinated atom motions may be relevant to enzyme mechanisms. There is accumulating evidence that the behavior of protein machines can be understood with these models, and the important large domain motions can be obtained readily. For the ribosome, the results clearly indicate that its motions relate strongly to many aspects of its function. Already we have seen that the large ribosomal ratchet motion simultaneously causes the tRNAs and mRNA to translate in the processing direction. The control of the mRNA at the anticodon binding site is extremely strong, to ensure fidelity of copying, with the mRNA being moved translationally as a fully rigid body, with no internal motions.  
Robert L. Jernigan (Iowa State University)  Analyzing the proteinprotein interaction network 
Abstract: The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins. In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the proteinprotein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new proteinprotein interactions that indeed have been reported recently. Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions. Some other applications of this type of analysis for the identification of important variable in a simulation will be considered.  
Yannis G. Kevrekidis (Princeton University)  Computational experiments in coarsegraining atomistic simulations 
Abstract: I will present and discuss a number of computational experiments associated with the coarsegraining of atomistic/agentbased simulations. In particular, I will discuss coarse reverse integration, as well as the use of diffusion maps (a manifoldlearning technique) to suggest the selection of certain coarsegrained observables ("reduction coordinates") for the atomistic simulations. The illustrations will come from molecular dynamics, Monte Carlo as well as agentbased models.  
Soojeong Kim (University of Iowa)  Topological analysis of DNAbinding protein complexes 
Abstract: Difference topology is a methodology to derive the number of DNA crossings trapped in an unknown protein complex. By this method, Pathania, Jayaram, and Harshey revealed the topological structure within the Mu protein complex which consisted of three DNA segments containing five nodes [1]. In their experiments, they used a member of the sitespecific recombinases which is known as Cre. Cre mediates DNA exchange by rearranging target sites of the DNA segments. During this DNA recombination, there are no extra DNA crossings introduced. The initial DNA conformation is unknotted. After Cre recombination, the products are knots or catenanes. Recently, Darcy, Luecke, and Vazquez analyzed these experimental results and proved that the fivenoded conformation is the only biologically reasonable structure of the Mu protein DNA complex [2]. We address the possibility of protein complexes that binds four DNA segments. By the useful property of Cre, we can make the assumption that after Cre recombination, the topology of a DNAprotein complex would be a knot or catenane. The latest results of the topological tangle model for this case and very basic biological and mathematical backgrounds will be discussed. Reference: [1] S. Pathania, M. Jayaram, and R. Harshey, Path of DNA within the Mu transpososome: Transposase interaction bridging two Mu ends and the enhancer trap five DNA supercoils, Cell 109 (2002), 425436. [2] I. K. Darcy, J. Luecke, and M. Vazquez, A tangle analysis of the Mu transpososome protein complex which binds three DNA segments, Preprint.  
Patrice Koehl (University of California)  The geometry of biomolecular solvation. Part 1: Hydrophobicity 
Abstract: The molecular basis of life rests on the activity of biological macromolecules, mostly nucleic acids and proteins. A perhaps surprising finding that crystallized over the last handful of decades is that geometric reasoning plays a major role in our attempt to understand these activities. In my presentations, I will explore the connection between the biological activities of proteins and geometry, using a representation of molecules as a union of balls. I will cover three topics: (1) the geometry of biomolecular solvation, (2) understanding electrostatics using implicit solvent models, and (3), designing protein shape descriptors. Part 1: Hydrophobicity. The structure of a biomolecule is greatly influenced by its environment in the cell, which mainly consists of water. Explicit representation of the solvent that includes individual water molecules are costly and cumbersome. It is therefore highly desirable to develop implicit solvent models that are nevertheless accurate. In such models, hydrophobicity is expressed as a weighted sum of atomic accessible surface areas. I will show how these surface areas can be computed from the dual complex, a filtering of the weighted Delaunay triangulation of the centers of the atoms.  
Patrice Koehl (University of California)  Part 2: Electrostatics 
Abstract: Electrostatics plays an important role in stabilizing a molecule. In an implicit solvent model, the electric field generated by a molecule is obtained as a solution of the PoissonBoltzmann equation, a second order elliptic equation to be solved over the whole space within and around the molecule. Analytical solutions of this equation are not available for large molecules. Numerical solutions are usually obtained using finite element methods on regular meshes. These meshes however are not adequate to represent accurately the surface of the molecule that serves as interface between the interior of the molecule and the solvent. I will discuss the application of tetrahedral meshes for solving the PoissonBoltzmann equation, based on the meshing of the skin surface of the molecule. The skin surface is a smooth, differentiable surface of the molecule.  
Patrice Koehl (University of California)  Part 3: Protein shape descriptors 
Abstract: As the number of proteins for which a high resolution structure is known grow, it is important to classify them. A classification of protein structure would be useful for example to derive structural signatures for the protein functions. Classification requires a measure of protein structure similarity: while there are tools available to align and superpose protein structures, these tools are usually slow and not practical for large scale comparisons. In this talk, I will discuss the development of protein shape descriptors that allow fast detection of similarity between protein structures.  
Samuel Kou (Harvard University)  Exploring global energy landscape of lattice protein models via Monte Carlo methods 
Abstract: Efficient exploration of the configuration space of a protein is essential for its structure prediction. In this talk we will consider two recent Monte Carlo developments for such a task: (i) equienergy (EE) sampler and (ii) fragment regrowth via energyguided sequential sampling (FRESS). The EE sampler provides accurate estimation of the density of states of the energy landscape, which then allows detailed study of the thermodynamics of lattice protein folding. The FRESS algorithm provides an efficient means to sequentially simulate a protein structure. As an illustration we will consider 2D and 3D HP models. For the benchmark sequences, we not only found new lower energies for all the 3D sequences longer than 80 residues with little computing effort, but also were able to accurately estimate the density of states that characterizes the global energy landscape.  
Peter R. Kramer (Rensselaer Polytechnic Institute)  Coarsegrained parameterizations of biomolecular systems 
Abstract: I discuss two ongoing research programs concerning stochastic microphysical models in biology. First, in joint work with Grigorios Pavliotis (Imperial College) and Juan Latorre, we apply the methods of homogenization theory to compute transport properties for mathematical models (Brownian motors) of molecular motors in cells. The objective is to ascertain how the design properties of the motor affect its function. Secondly, in joint work with Shekhar Garde and Adnan Khan, we explore a simple stochastic model for the behavior of water molecules near a solute surface which has the potential for improving substantially upon Brownian dynamics models more conventionally used in engineering applications. We use exactly solvable mathematical models as a testbed for addressing some basic datadriven parameterization issues.  
Krzysztof Kuczera (University of Kansas)  Simulations of peptide folding and dynamics 
Abstract: We report on replicaexchange simulations of the folding of a 21residue alphahelical peptide in explicit solvent. Using eight replicas over a 280450 K temperature range, we were able to simulate both the folding equilibrium and the helix formation process. While the melting temperature was exaggerated by about 50 K, the folding enthalpy and entropy were in good agreement with experimental data. Simulations of the helix formation process showed a sequential mechanism, with helical structures formed within ca. 3 ns of simulation, and confirmed that the alphahelical state was a global free energy minimum of the peptide at low temperatures. We also present longterm conventional MD simulations of several simple peptide model systems for which we compare simulation results to experimental data, in order to test current peptide and water models.  
Satish Kumar (University of Minnesota)  Brownian dynamics simulations of polymer behavior in nanofluidic and microfluidic systems 
Abstract: Brownian Dynamics (BD) is a stochastic simulation method that can quantitatively describe the nonequilibrium behavior of long polymers (~1 micron contour length) over long time scales (~1 s). With the increasing use of nanofluidic and microfluidic devices for the handling of biopolymers such as DNA, BD has the potential to be a powerful design tool for the separation and transport processes carried out in these devices. As a coarsegrained simulation method, BD also serves as a natural bridge between atomistic and continuum modeling. In this talk, an introduction to the Brownian Dynamics simulation method will be given along with simulation results for some applications of current interest. The introduction will review basic molecular models for polymers (beadrod, beadspring) and the stochastic differential equations used to describe their dynamics. The applications will focus on polyelectrolyte adsorption and electrophoresis.  
Christian E. Laing (New York University)  Annotated tertiary interactions in RNA structures reveal new interactions, correlations in motifs and composite motifs 
Abstract: RNA tertiary motifs play an important role in RNA folding. To understand the complex organization of RNA tertiary interactions, we compiled a dataset containing 54 highresolution RNA crystal structures. Seven RNA tertiary motifs (coaxial helix, Aminor, ribose zipper, pseudoknot, kissing hairpin, tRNA Dloop:Tloop and tetralooptetraloop receptor) were searched by different computer programs. For the nonredundant RNA dataset, 601 RNA tertiary interactions were found. Most of these 3D interactions occur in the 16S and 23S rRNAs. Exhaustive search of these motifs revealed diversity of Aminor interactions, and other looploop receptor interactions similar to the tetralooptetraloop receptor. Correlation between motifs, such as pseudoknot or coaxial helix with Aminor, shows that they can form composite motifs. These findings may lead to tertiary structure constraints for RNA 3D prediction.  
Christopher J. Lee (University of California)  Mapping evolutionary pathways of HIV1 drug resistance using conditional selection pressure 
Abstract: Can genomics provide a new level of strategic intelligence about rapidly evolving pathogens? We have developed a new approach to measure the rates of all possible evolutionary pathways in a genome, using conditional Ka/Ks to estimate their “evolutionary velocity”, and have applied this to several datasets, including clinical sequencing of 50,000 HIV1 samples. Conditional Ka/Ks predicts the preferred order and relative rates of competing evolutionary pathways. We recently tested this approach using independent data generously provided by Shafer and coworkers (Stanford HIV Database), in which multiple samples collected at different times from each patient make it possible to track which mutations occurred first during this timecourse. Out of 35 such mutation pairs in protease and RT, conditional Ka/Ks correctly predicted the experimentally observed order in 28 cases (p=0.00025). Conditional Ka/Ks data reveal specific accessory mutations that greatly accelerate the evolution of multidrug resistance. Our analysis was highly reproducible in four independent datasets, and can decipher a pathogen’s evolutionary pathways to multidrug resistance even while such mutants are still rare. Analysis of samples from untreated patients shows that these rapid evolutionary pathways are specifically associated with drug treatment, and vanish in its absence.  
Michael Levitt (Stanford University)  Lecture 1: Fundamental forces and molecular architecture 
Abstract: Over the last three decades computer simulation have been able to bring atomic motion to structural biology. Such motion is not seen in experimental structural studies but is relatively easily studied by applying law of motions to models of the proteins and nucleic acids. By bringing molecular to life in this way, simulation complements experimental work making it is much easily to understand how proteins biological macromolecules function. After introducing molecular structure and the fundamental forces that stabilize it, we consider molecular motion and protein folding. Lecture 1 will introduce fundamental forces between atoms and consider how these forces give rise to the stable protein structures.  
Michael Levitt (Stanford University)  Lecture 2: Simulating molecular motion 
Abstract: Lecture 2 will describe the complementary methods of simulation: molecular dynamics and normal mode dynamic. We will show how they help understand the stability and the nature of protein motion.  
Michael Levitt (Stanford University)  Lecture 3: Simulating protein folding 
Abstract: Lecture 3 will consider the protein folding both in terms of predicting the most stable structure and simulating the actual folding pathways.  
Ronald M. Levy (Rutgers University)  Simple models for simulating replica exchange simulations of protein folding and binding 
Abstract: Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of freeenergy landscapes which define many challenging problems in computational biophysics, including protein folding and binding. Although replica exchange is a parallel simulation technique whose implementation is relatively straightforward, kinetics and the approach to equilibrium in the replica exchange ensemble are complicated; there is much to learn about how to best employ RE to protein folding and binding problems. Protein folding rates often slow down as the temperature is raised above a critical value and this “antiArrhenius” behavior represents a challenge for RE. However, it is far from straightforward to systematically explore the impact of this on RE by brute force molecular simulations, since RE simulations of protein folding are very difficult to converge. In studies over the past two years using both atomistic and simplified models, we have clarified some of the obstacles to obtaining converged thermodynamic information from RE simulations. In my talk I will describe some simple continuous and discrete models we have constructed to explore the behavior of replica exchange sampling under a variety of conditions. References Andrec, M., A.K. Felts, E. Gallicchio, and R.M. Levy. Protein folding pathways from replica exchange simulations and a kinetic network model. Proceedings Natl. Acad. Sci. USA, 102, 68016806 (2005). K.P. Ravindranathan, E. Gallicchio, R.A. Friesner, A.E. McDermott, and R.M. Levy. Conformational equilibrium of a cytochrome P450 – substrate complex, a replica exchange MD study. J. Am. Chem. Soc., 128, 57865791 (2006) Zheng, W., M. Andrec, E. Gallicchio, and R.M. Levy. Simulating replica exchange simulations of protein folding with a kinetic network model. Proceedings Natl. Acad. Sci. USA, 104, 1534015345 (2007). Zheng, W., M. Andrec, E. Gallicchio, and R.M. Levy. Simple continuous and discrete models for simulating replica exchange simulations of protein folding. J. Phys. Chem., in press.  
Florence J. Lin (University of Southern California)  Overall rotation due to internal motions in the Nbody dynamics of protein molecules 
Abstract: For a protein molecule in vacuo, the net overall rotation due to flexibility is expressed in internal coordinates by using Eckart's decomposition of the total rotational angular momentum. Regardless of whether the total (rotational) angular momentum vanishes, the condition for zero overall rotation is zero orbital angular momentum. Previous approaches toward the elimination of overall rotation included (i) using normal modes, (ii) minimizing the rootmeansquared deviation (RMSD through finite rotations with respect to an initial configuration, and (iii) setting the total angular momentum to zero. These three approaches neglected the contribution of nonzero internal angular momentum. While this approach [1, 2] is motivated by results in geometric mechanics [3], the results agree with an experimental observation of a rotation of 20 degrees in triatomic photodissociation [4] and a computational observation of an overall rotation of 42 degrees in the dynamics of protein
molecules [5].
References: [1] F. J. Lin, Hamiltonian dynamics of atomdiatomic molecule complexes and collisions, Discrete and Continuous Dynamical Systems, Supplement 2007, 655 – 666 (2007). [2] F. J. Lin, Separation of overall rotation and internal motion in the Nbody dynamics of protein molecules, 2007a. [3] J. E. Marsden, R. Montgomery, and T. Ratiu, Reduction, symmetry, and phases in mechanics, Memoirs of the American Mathematical Society, Vol. 88, No. 436, American Mathematical Society, Providence, RI, 1990. [4] A. V. Demyanenko, V. Dribinski, H. Reisler, H. Meyer, and C. X. W. Qian, Quantumproduct statedependent anisotropies in photoinitiated unimolecular decomposition, Journal of Chemical Physics 111, 7383 – 7396 (1999). [5] Y. Zhou, M. Cook and M. Karplus, Protein motions at zerototal angular momentum: The importance of longrange correlations, Biophysical Journal 79, 2902 – 2908 (2000). 

Julie C. Mitchell (University of Wisconsin)  Cluster optimization in protein docking 
Abstract: Recent progress in obtaining docked protein complexes will be discussed. The combination of exhaustive search, clustering and localized global optimization can reliably find energy minima to highly nonconvex biomolecular energy functions. Using an energy function that adds desolvation and screened electrostatics to classical molecular mechanics potentials, the global minimum is found very near to the observed native state. This is demonstrated across a large number of benchmark examples.  
Antonio Mucherino (Seconda Università di Napoli)  Simulating protein conformations by a geometric model 
Abstract: Protein molecules are usually studied through mathematical models which consider the physicochemical forces among the atoms forming the molecule. Recently, however, the interest on the geometric properties of protein conformations has been growing, and models mainly based on such properties have been developed. In this work, a global optimization problem is formulated for simulating protein conformations, that is based on the socalled "tube model", in which a protein is modeled as a thickened tube [1,2]. The optimization problem is solved by the metaheuristic method Monkey Search [3], which is inspired by the behavior of a monkey climbing trees in its search for food. Computational experiences proved that conformations having the typical geometric properties of proteins can be generated and that some of them can be close to conformations that proteins in Nature actually have. [1] J.R. Banavar, A. Maritan, C. Micheletti and A. Trovato, "Geometry and Physics of Proteins", Proteins: Structure, Function, and Genetics 47 (3): 315–322, 2002 [2] G. Ceci, A. Mucherino, M. D’Apuzzo, D. di Serafino, S. Costantini, A. Facchiano, G. Colonna, "Computational Methods for Protein Fold Prediction: an AbInitio Topological Approach", Data Mining in Biomedicine, Springer Optimization and Its Applications, Panos Pardalos et al (Eds.), vol.7, 2007 [3] A. Mucherino and O. Seref, "Monkey Search: A Novel MetaHeuristic Search for Global Optimization", AIP Conference Proceedings 953, Data Mining, System Analysis and Optimization in Biomedicine, 162–173, 2007  
José Nelson Onuchic (University of California, San Diego)  The energy landscape for folding and molecular motors 
Abstract: Globally the energy landscape of a folding protein resembles a partially rough funnel. The local roughness of the funnel reflects transient trapping of the protein configurations in local free energy minima. The overall funnel shape of the landscape, superimposed on this roughness, arises because the interactions present in the native structure of natural proteins conflict with each other much less than expected if there were no constraints of evolutionary design to achieve reliable and relatively fast folding (minimal energetic frustration). A consequence of minimizing energetic frustration is that the topology of the native fold also plays a major role in the folding mechanism. Some folding motifs are easier to design than others suggesting the possibility that evolution not only selected sequences with sufficiently small energetic frustration but also selected more easily designable native structures. The overall structure of the onroute and offroute (traps) intermediates for the folding of more complex proteins is also strongly influenced by topology. In this context, folding of larger and more complex proteins will be discussed. Many cellular functions rely on interactions among proteins and between proteins and nucleic acids. The limited success of binding predictions may suggest that the physical and chemical principles of protein binding have to be revisited to correctly capture the essence of protein recognition. Going beyond folding, the power of reduced models to study the physics of protein assembly will be discussed. Since energetic frustration is sufficiently small, native topologybased models, which correspond to perfectly unfrustrated energy landscapes, have shown that binding mechanisms are robust and governed primarily by the protein’s native topology. These models impressively capture many of the binding characteristics found in experiments and highlight the fundamental role of flexibility in binding. Deciphering and quantifying the key ingredients for biological selfassembly is invaluable to reading out genomic sequences and understanding cellular interaction networks. Going even beyond binding, we will be discussing the energy landscape for the molecular motor kinesin.  
Hans G. Othmer (University of Minnesota)  Current issues in understanding complex biological networks 
Abstract: Biological networks that arise in signal transduction, metabolism, gene control or other cellular functions frequently involve many steps and many levels of control, but are remarkably reliable in producing the desired output in response to inputs. In this talk we will discuss general characteristics such as sensitivity and adaptation of networks, give several examples that illustrate how robustness is achieved, and discuss the mathematical techniques that can contribute to understanding complex networks.  
Sefika Banu Ozkan (Arizona State University)  Protein folding by ZAM & FRODA 
Abstract: Protein folding by ZAM & FRODA Protein folding problem’ stemming from Levinthal’s paradox (i.e. how proteins fold fast even though they have vast conformational space) has been answered by the zipping and assembly mechanism (ZA). According to ZA mechanism, an unfolded chain first explores locally favorable structures at multiple independent positions along the chain. Then, these local structures engage neighboring amino acids in the chain sequence to form additional contacts, growing individual local structures by zipping or assembling. Using ZA principle, an allatom structure prediction method has been developed, called zipping and assembly method (ZAM). ZAM has successfully predicted protein structures of small single domain proteins. It uses replica exchange molecular dynamics for conformational sampling which creates a bottleneck in the assembly stage. We modify ZAM assembly stage by introducing FRODA which is a Monte Carlo based geometric simulation. Since FRODA can explore the largeamplitude motions of larger systems so much faster than molecular dynamics, we can speed up the assembly stage and generate the complete enumeration of all topologies quickly. The results show that the native structure of proteins can be sampled during the FRODAassembly stage within a RMSD <3 Å.  
Sanghyun Park (Argonne National Laboratory)  Computing conformational free energy by deactivated morphing 
Abstract: "What is the free energy difference between two different conformations of a protein?" This simple question is apparently not so simple to answer. Despite the significant advances in free energy computations, there has been relatively little success in computing conformational free energies. To compute conformational free energy differences, we need a transformation path that connects different conformations. The free energy difference is the same no matter what path is taken, but not all paths are equally useful. Finding a path that allows an efficient computation of free energy is a crucial step. Tremendous recent efforts to find physical paths of conformational changes have motivated us to take a stab at using nonphysical paths. This poster introduces a method we call 'deactivated morphing' and presents applications to two test systems: alanine dipeptide (AlaD) and decaalanine (Ala10), both in explicit water. In this method, the internal interaction of a protein is completely turned off before a transformation is carried out along a nonphysical path.  
Bobby Philip (Los Alamos National Laboratory)  An introduction to multigrid techniques 
Abstract: The lecture will be a basic introduction to multigrid techniques. It will cover some background on stationary iterative methods. The two main components of linear multigrid algorithms: smoothing and coarsegrid correction will be introduced. A two grid algorithm will be introduced that then leads to the description of the multilevel Vand Wcycles. A brief description of algebraic multigrid methods will be followed by a description of the Full Approximation Scheme (FAS) for nonlinear problems. Time permitting, the generalization of these algorithms to handle grids with local refinement will also be outlined.  
Jed W. Pitera (IBM Research Division)  The limitations of temperature replica exchange (TREMD) for protein folding 
Abstract: The replica exchange/parallel tempering method and its variations offer the hope of improved sampling for many challenging problems in molecular simulation. Like all sampling methods, however, the effectiveness of replica exchange is highly dependent on the specific physical system being studied. We have carried out large scale temperature replica exchange (TREMD) simulations of peptides and proteins in explicit solvent and encountered a number of issues that limit the effectiveness of the method in sampling biomolecular conformations. In particular, our results suggest the need to either replace or augment the temperature variable with an alternative extended variable, such as Hamiltonian scaling.  
Hong Qian (University of Washington)  From chemical reaction systems to cellular states: A computational approach 
Abstract: The task of molecular biology is to identify and define cellular states and functions in terms of molecular structures, dynamics, and chemical reactions. At macromolecular level, functional states and dynamics of individual proteins and enzymes are determined by Newton's Law and/or statistical thermodynamics. Computational approach thus follows molecular dynamic simulations and statistical thermodynamics. At cellular level, "the structures" of biochemical reaction systems, i.e., metabolic networks, genetic regulatory modules and signaling pathways, have been the central focus of current reserch. We introduce the chemical master equation (CME) as the theoretical foundation of their dynamics. Dynamic models based on the CME represent open chemical systems that follow nonequilibrium statistical thermodynamics  a key ingredient for living cell but not in usual macromolecular models. The CME supersedes the traditional deterministic models based on the law of mass action; it provides reaction kinetics and concentration (or copy number) fluctuations; and it allows a rigorous definition of "cellular state(s)" in terms of the concentrations and copy numbers of biomolecules in a biochemical reaction system in open chemical environment. We discuss this computational approach to cellular biochemistry, its relation to opensystem thermodynamics. In particular we outline its similarities to and distinctions from computational macromolecular dynamics.  
Andrew J. Rader (Indiana UniversityPurdue University)  Probing the diversity of unfolding pathways by simulated thermal denaturation 
Abstract: In many cases the native structures of proteins encode information about their folding pathways. The degree to which this is true may be related to the similarity of various structural solutions, i.e. multiple NMR structures or independently solved Xray structures. We explore both the robustness of the native state and its impact upon putative folding pathways for these structures by examining simulated thermal denaturation pathways for ensembles of “native” structures. Previous rigidity analysis of proteins using the FIRST software[1] has demonstrated that such simulated unfolding results correlate well with experimental hydrogendeuterium exchange data[2] and mutational results[3]. We introduce an unfolding rigidity profile to characterize unfolding pathways and indicate which protein residues are most likely to adopt nativelike conformations. This rigidity profile is also used to discriminate conformational substates of the protein native state which are the results of different folding pathways. [1] D. J. Jacobs, A.J. Rader, Leslie A. Kuhn, and M.F. Thorpe Protein Flexibility Prediction Using Graph Theory. Proteins, 44, 150165, 2001. [2] B.M. Hespenheide, A.J. Rader, M.F. Thorpe, and L.K. Kuhn J. Molec. Graph. & Model., 21, 195207, 2002. [3] A.J. Rader, G. Andersen, B. Isin, H.G. Khorana, I. Bahar and J. KleinSeetharaman Identification of Core Amino Acids Stabilizing Rhodopsin. Proc. Natl. Acad. Sci., 101, 72467251, 2004.  
Shantanu Roy (Universität Basel)  Minima Hopping within an allatom framework for biomolecular structure prediction 
Abstract: We consider the protein folding problem as a global optimization problem on the free energy surface of the allatom OPLS force field. Starting from amino acid sequences arranged in a linear chain configuration we use the Minima Hopping algorithm to find low energy configurations . The algorithm samples the potential energy surface following the BellEvansPolanyi principle. In this way our moves are completely unbiased but have nevertheless a strong tendency to lead into other low energy configurations. Some small peptides like polyalanine were studied in vacuo. For an Ac(Ala)nLysH+ in vacuum we obtained a helical conformation while other (Ala)nH+ systems are found to be not helical.  
Anchanee Sangcharoen (Mahidol University )  Investigation of the unfolding pathway of Cyt2Aa2 toxin 
Abstract: The expressed 29kDa Cyt2Aa2 protoxin is produced from Bacillus thuringiensis subsp. darmstadiensis during sporulation. This toxin is proteolytically processed into the 25kDa active form which is lethal to Dipteran (Stegomyia and Culex) larvae. It has been proposed that the mechanism of action required a conformational change during the interaction with lipid membrane. We have demonstrated previously that the toxin can adopt a stable intermediate state between the transitions from native to unfolded state. In this study, we aim to investigate the conformational state of each secondary structure elements of the toxin in intermediate state. The Φ values analysis was employed as a tool to reveal the conformation state on various amino acid positions of toxin. Nondisruptive mutant toxins were designed and constructed as conformational probes using sitedirected mutagenesis. The effect of mutation at different position is characterized in terms of protein expression level, solubility, mosquitolarvicidal toxicity and hemolytic activity compared to those of wild type. The results showed that an intermediate state of this toxin was found in the aggregation/oligomerization form. Transitional free energy and activation energy of these toxins were obtained and derived for the Φ values. The data revealed helices A, B and C of the intermediate are quite deviated from the native state while helix D is still maintained similar to native state conformation. The relocation of these helices was proposed to be related to the conformational changes and contributes to the functional mechanism of this toxin.  
Jeffery G. Saven (University of Pennsylvania)  Engineering protein structure and function with theoretical protein design 
Abstract: Protein design opens new ways to probe the determinants of folding, to facilitate the study of proteins, and to arrive at novel molecules, materials and nanostructures. Recent theoretical methods for identifying the properties of amino acid sequences consistent with a desired structure and function will be discussed. Such methods address the structural complexity of proteins and their many possible amino acid sequences. Several computationally designed proteinbased molecular systems will be presented that have been experimentally realized, including novel proteins tailored to accommodate nonbiological cofactors.  
Brigitte Servatius (Worcester Polytechnic Institute)  Combinatorial rigidity and the molecular conjecture 
Abstract: Graph theory has successfully been used by several authors to predict protein flexibility, in particular, combinatorial rigidity is an important tool. The most important new result in combinatorial rigidity is the characterization of global rigidity while one of the most intriguing open problems is called "the molecular conjecture". We will explain the state of the art in the progress toward the conjecture and the implications of recent progress in rigidity theory, including the concept of combinatorial allostery, toward understanding the behavior of molecules.  
Carlos L. Simmerling (SUNY)  Challenges in generation of conformational ensembles for peptides and small proteins 
Abstract: Useful insight into the folding behavior of proteins has been gained by studying the free energy landscapes of model peptides and very small proteins. These can be particularly useful in exploring the denatured state, which is difficult to characterize directly through experiments. Several key challenges remain in obtaining accurate and precise computational data for peptides in solution, including the accuracy of the biomolecular force field and solvent model along with difficulties in obtaining converged ensembles. In this talk these issues will be explored, including a comparison of the properties of several Amber parameter sets and the effects of simulation with different explicit and implicit water models. Methods that improve convergence will be discussed, such as modified replica exchange approaches that permit application to larger systems at reduced computational cost.  
Robert D. Skeel (Purdue University)  What is a transition path? 
Abstract: Calculating transition paths of conformation change is not amenable to computer solution unless the problem is defined precisely. Inspired by the work of others, we offer a precise definition of the problem without invoking unmotivated stochastic forces. A weakness of thisand otherapproaches is the need for the user to identify an appropriate set of collective variables. In principal at least, the quality of the result can be checked a posteriori by calculating committor values from dynamics trajectories. The approach we advocate leads to a nonstandard minimum free energy path that is more reasonable physically.  
Ileana Streinu (Smith College)  Geometric simulation of protein flexibility 
Abstract: Solved protein structures from PDB depict a static picture, but proteins are flexible. We are interested in understanding how they move near the native conformation, or between two given conformations, without resorting to heavyduty molecular dynamics techniques. Geometric simulations focus on motions of constrained structures behaving much like mechanical devices, without concern for certain forces (such as electrostatic or hydrophobic interactions). The idea is to isolate specific problems (pertaining to maintenance of geometric distance and angle constraints, or to collisions), and develop the mathematical and computational tools for addressing them efficiently. We will describe static flexibility analysis tools pioneered in the FIRST software, firstgeneration geometric simulation as done in FRODA, and recent methods aiming at speeding them up.  
William Swope (IBM)  Simulations on BlueGene of a fast folding mutant of lambda(685) 
Abstract: Using the BlueGene computer at IBM Research we have performed extensive simulations on a mutant construct of lambda repressor. The protein consists of 80 amino acids stuctured as a five helix bundle in the folded state. The mutant was designed in the Gruebele lab at UIUC to exhibit sub10 microsecond folding times in laser temperature jump experiments. Our simulations employed replica exchange and traditional molecular dynamics at a number of different temperatures. Although it is very difficult to thoroughly equilibrate and sample molecular systems of this size and complexity, our simulations clearly reveal very complex folding behavior. In particular, different structural elements exhibit different degrees of thermodynamic stability and melt at different temperatures. Some of the structural transitions appear to be relatively cooperative, whereas others are quite diffuse.  
Sandor Vajda (Boston University)  Multistage optimization for proteinprotein docking 
Abstract: We focus on the problem of determining the structure of complexes formed by the association of two proteins by searching for the global minimum of a function approximating the free energy of the complex. Solving this problem requires the combination of a number of different optimization methods. First we explore the conformational space by systematic global search based on the Fast Fourier Transform (FFT) correlation approach that evaluates the energies of billions of docked conformations on a grid. We show that the method can be efficiently used with pairwise interactions potentials that substantially improve the docking results. A new 5D FFT algorithm is also discussed. The 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. The conformations are refined by a new mediumrange optimization method that has been developed to locate the global minima within well defined regions of the conformational space. In each cluster, the energy of the complex is a very noisy funnellike function on the space of rigid body motions, the Euclidean group SE(3). The SemiDefinite programming based Underestimation (SDU) method constructs a convex quadratic underestimator to the energy funnel based on a sample of the local mimima of the energy function, and uses the quadratic underestimator to guide future sampling. We show that the parameterization of SE(3) has a significant impact on the effectiveness of SDU and introduce a parameterization that dramatically reduces the number of very costly energy function evaluations. We also discuss the application of the combined method to recent targets in CAPRI (Critical Assessment of Protein Interactions), the first communitywide docking experiment.  
Jérôme Waldispühl (Massachusetts Institute of Technology)  Modeling ensembles of transmembrane betabarrel proteins 
Abstract: Transmembrane betabarrel (TMB) proteins are embedded in the outer membrane of
Gramnegative bacteria, mitochondria, and chloroplasts. Despite their
importance, very few nonhomologous TMB structures have been determined by Xray
diffraction because of the experimental difficulty encountered in crystallizing
transmembrane proteins. We introduce the program partiFold to investigate the
folding landscape of TMBs. By computing the Boltzmann partition function,
partiFold estimates interstrand residue interaction probabilities, predicts
contacts and perresidue Xray crystal structure Bvalues, and samples
conformations from the Boltzmann low energy ensemble. This broad range of
predictive capabilities is achieved using a single, parameterizable grammatical
model to describe potential betabarrel supersecondary structures, combined with
a novel energy function of stacked amino acid pair statistical potentials.
PartiFold outperforms existing programs for interstrand residue contact
prediction on TMB proteins, offering both higher average predictive accuracy as
well as more consistent results. Moreover, the integration of these contact
probabilities inside a stochastic contact map can be used to infer a more
meaningful picture of the TMB folding landscape, which cannot be achieved with
other methods. Partifold's predictions of Bvalues are competitive with recent
methods specifically designed for this problem. Finally, we show that sampling
TMBs from the Boltzmann ensemble matches the Xray crystal structure better
than single structure prediction methods. A webserver running partiFold is
available at http://partiFold.csail.mit.edu/.
Joint work with: Charles W. O'Donnell, Srini Devadas, Peter Clote and Bonnie
Berger.
References: [1] J. Waldispühl*, C.W. O'Donnel*, S. Devadas, P. Clote and B. Berger, Modeling Ensembles of Transmembrane betabarrel Proteins, PROTEINS: Structure, Function and Bioinformatics, published online 14 Nov. 2007. doi:10.1002/prot.21788 (* authors equally contributed) [2]J. Waldispühl, B. Berger, P. Clote and J.M. Steyaert, Predicting Transmembrane betabarrels and Interstrand Residue Interactions from Sequence, PROTEINS: Structure, Function and Bioinformatics, vol. 65, issue 1, p.6174, 2006. doi:10.1002/prot.21046 

Jin Wang (SUNY)  Configurationdependent diffusion can shift the kinetic transition state and barrier height of protein folding 
Abstract: We show that diffusion can play an important role in proteinfolding kinetics. We explicitly calculate the diffusion coefficient of protein folding in a lattice model. We found that diffusion typically is configuration or reaction coordinatedependent. The diffusion coefficient is found to be decreasing with respect to the progression of folding toward the native state, which is caused by the collapse to a compact state constraining the configurational space for exploration. The configuration or positiondependent diffusion coefficient has a significant contribution to the kinetics in addition to the thermodynamic freeenergy barrier. It effectively changes (increases in this case) the kinetic barrier height as well as the position of the corresponding transition state and therefore modifies the folding kinetic rates as well as the kinetic routes. The resulting folding time, by considering both kinetic diffusion and the thermodynamic folding freeenergy profile, thus is slower than the estimation from the thermodynamic freeenergy barrier with constant diffusion but is consistent with the results from kinetic simulations. The configuration or coordinatedependent diffusion is especially important with respect to fast folding, when there is a small or no freeenergy barrier and kinetics is controlled by diffusion. Including the configurational dependence will challenge the transition state theory of protein folding. The classical transition state theory will have to be modified to be consistent. The more detailed folding mechanistic studies involving phi value analysis based on the classical transition state theory also will have to be modified quantitatively.  
Guowei Wei (Michigan State University)  Mathematical methods for implicit solvent models 
Abstract: Rigorous, quantitative, and atomic scale description of complex biological systems is a grand challenge. Explicit description of biomolecules and their aqueous environment, including solvent, cosolutes, and mobile ions, is prohibitively expensive although a variety of methods, including Ewald summations, Euler summations, periodic images and reaction field theory, have been developed in the past few decades. Therefore, multiscale analysis is an attractive and sometimes indispensable approach. Implicit solvent models which treat the solvent as a macroscopic continuum while admitting a microscopic atomic description for the biomolecule, are efficient multiscale approaches to complex, large scale biological systems. We summarize recent advances in mathematical methods for the PoissonBoltzmann (PB) equation based implicit solvent theory. These include the rigorous mathematical treatments of molecular surface interfaces, surface geometric singularities, charge singularities and associated force evaluation for molecular dynamics.  
Thomas Weikl (Max Planck Institute for Colloids and Interfaces)  Transition states in protein folding 
Abstract: Small singledomain proteins often exhibit only a single freeenergy
barrier, or transition state, between the denatured and the native state.
The folding kinetics of these proteins is usually explored via mutational
analysis. A central question is which structural information on the
transition state can be derived from the mutational data. To interpret these
data, we have developed models that are based (a) on the substructural
cooperativity of helices and hairpins, and (b) on splitting up
mutationinduced stability changes of a protein into components for its
substructures. We obtain a consistent structural interpretation of
mutational Phivalues by fitting few parameters that describe the degrees of
structure formation of helices and hairpins in the transition state. Our
models explain how mutations at a given site can lead to different
Phivalues, and capture nonclassical Phivalues smaller than 0 or larger
than 1, which have been difficult to interpret. Nonclassical Phivalues
simply arise, e.g., if mutations stabilize a helix or hairpin, but
destabilize its tertiary interactions.
References: [1] C. Merlo, K. A. Dill, and T. R. Weikl, PNAS 102, 10171 (2005). [2] T. R. Weikl and K. A. Dill, J. Mol. Biol. 365, 1578 (2007). [3] T. R. Weikl, Biophys. J., in press (2008). 

Lauren Wickstrom (SUNY)  The Dynamic Nature of the Folded and Unfolded States of the Villin Headpiece Helical Subdomain: An ultrafast folding protein 
Abstract: In order to understand protein folding, we need to understand both folded and unfolded state structure. One of the key systems for these studies is the 36 residue villin headpiece helical subdomain (HP36) because of its simple topology, small size and fast folding properties. Structures of HP36 have been determined using Xray crystallography and NMR spectroscopy, with the resulting structures exhibiting clear structural differences. We complement the existing data by using molecular dynamics simulations and experimental double mutant cycles to show that the xray structure is the better representation in solution at neutral conditions. Denatured state studies using fragment analysis coupled with relatively low resolution spectroscopic techniques show a small tendency to form locally stabilized structure. Using standard Replica Exchange Molecular Dynamics, our simulations show that the first helix contains the most nativelike helical structure of all three helices. Overall, our analysis shows how theoretical and experimental collaborative efforts can help aid in the understanding of the dynamic nature of the folding pathway.  
Zhijun Wu (Iowa State University)  The solution of the distance geometry problem for protein modeling 
Abstract: A wellknown problem in protein modeling is the determination of the structure of a protein with a given set of interatomic or interresidue distances obtained from either physical experiments or theoretical estimates. A general form of the problem is known as the distance geometry problem in mathematics, the graph embedding problem in computer science, and the multidimensional scaling problem in statistics. The problem has applications in many other scientific and engineering fields as well such as sensor network localization, image recognition, and protein classification. We describe the formulations and complexities of the problem in its various forms, and introduce a geometric buildup approach to the problem. Central to this approach is the idea that the coordinates of the atoms in a protein can be determined one atom at a time, with the distances from the determined atoms to the undetermined ones. The determination of each atom requires the solution of a small system of distance equations, which can usually be obtained in constant time. Therefore, in ideal cases, the coordinates of n atoms can be determined by a geometric buildup algorithm with O(n) distances in O(n) computing time instead of O(n2) distances in O(n2) computing time as required by a conventional singularvalue decomposition algorithm. We present the general algorithm and discuss the methods for controlling the propagation of the numerical errors in the buildup process, for determining rigid vs. unique structures, and for handling problems with inexact distances (distances with errors). We show the results from applying the algorithm to a set of model protein problems with varying degrees of availability and accuracy of the distances and justify the potential use of the algorithm in protein modeling practice.  
Jiaan Yang (MicrotechNano)  A novel method for protein folding shape description 
Abstract: A novel method has been developed to describe the protein folding shape structures. A set of 27 vectors is generated from an enclosed geometric space to describe the protein backbone folding shapes. This algorithm has mathematically reserved all possible folding shapes in space, and it is capable of making the complete assignment of folding shapes along the protein backbone without any gap. All possible types of folding can be uniquely described, including the regular protein secondary structures, irregular turn and loop structures and even rare possible folding structures. This method offers a simple onedimensional description for the complicated threedimensional folding structures which is able to align with the protein sequence for structural comparison. The results are compared with the protein data bank (PDB) and the structural assignments of other methods. This method has the ability to reveal the protein structural similarity and dissimilarity with the accurate and consistent meaning.  
Sichun Yang (University of Chicago)  Temperature dependence of Trpcage folding kinetics from replica exchange simulations 
Abstract: We examine the temperature dependence of the folding time in the Trpcage miniprotein, using allatom replica exchange molecular dynamics (REMD) simulations. This is done by using an "equation free" approach. The central idea is to use REMD simulations to generate appropriately initialized bursts of atomistic simulation trajectories to obtain the drift and diffusion coefficients of coarse variables of interest to capture quantitatively the coupling of fast with slow moving degrees of freedom. Then we employ a stochastic (Langevin) dynamic approach to follow the evolution of coarse variables and compute a distribution of folding times that is consistent with the drift and diffusion coefficients obtained from allatom simulations. We use physically motivated order parameters as coarse variables. We describe the distribution of folding times as a function of temperature for the Trpcage miniprotein. 
Hoda AbdelAal Bettley  University of Manchester  1/13/2008  1/18/2008 
Nancy M. Amato  Texas A & M University  1/13/2008  1/18/2008 
David F Anderson  University of Wisconsin  1/13/2008  1/18/2008 
Douglas N. Arnold  University of Minnesota  7/15/2001  6/30/2008 
Donald G. Aronson  University of Minnesota  9/1/2007  8/31/2009 
Rolf Backofen  AlbertLudwigsUniversität Freiburg  1/13/2008  1/18/2008 
Ivet Bahar  University of Pittsburgh  1/13/2008  1/24/2008 
Nathan A. Baker  Washington University School of Medicine  1/13/2008  1/17/2008 
Daniel J. Bates  University of Minnesota  9/1/2006  8/31/2008 
John Baxter  University of Minnesota  8/1/2007  7/30/2009 
Yermal Sujeet Bhat  University of Minnesota  9/1/2006  8/31/2008 
Victor Bloomfield  University of Minnesota  1/15/2008  1/18/2008 
Jamie Blundell  University of Cambridge  1/9/2008  1/19/2008 
Khalid Boushaba  Iowa State University  1/16/2008  6/30/2008 
MariaCarme T. Calderer  University of Minnesota  1/10/2008  1/18/2008 
Hannah Callender  University of Minnesota  9/1/2007  8/31/2009 
Larry Carson  3M  1/10/2008  1/11/2008 
Alessandro Cembran  University of Minnesota  1/10/2008  1/18/2008 
HueSun Chan  University of Toronto  1/13/2008  1/18/2008 
ShiJie Chen  University of Missouri  1/13/2008  1/18/2008 
Alan C. Cheng  Amgen Cambridge Research Center  1/13/2008  1/18/2008 
Gregory S. Chirikjian  Johns Hopkins University  1/11/2008  1/18/2008 
Patrick L Coleman  3M  1/10/2008  1/11/2008 
Ludovica Cecilia CottaRamusino  University of Minnesota  10/1/2007  8/30/2009 
Evangelos A. Coutsias  University of New Mexico  1/13/2008  1/18/2008 
Lenore J. Cowen  Tufts University  1/13/2008  1/19/2008 
Isabel K. Darcy  University of Iowa  9/1/2007  1/19/2008 
Yuanan Diao  University of North Carolina  Charlotte  1/9/2008  1/18/2008 
Ken A. Dill  University of San Francisco  1/13/2008  1/23/2008 
Yang Ding  Boston College  1/9/2008  1/19/2008 
Olivier Dubois  University of Minnesota  9/3/2007  8/31/2009 
Oguz C. Durumeric  University of Iowa  1/9/2008  1/12/2008 
Ron Elber  University of Texas  1/13/2008  1/18/2008 
Claus Ernst  Western Kentucky University  1/12/2008  1/18/2008 
Elisenda Feliu  University of Barcelona  1/13/2008  1/19/2008 
Christodoulos A. Floudas  Princeton University  1/8/2008  1/20/2008 
Ece Cazibe Gaffarogullari  University of Minnesota  1/10/2008  1/11/2008 
Andrew Gillette  University of Texas  1/9/2008  1/12/2008 
Anant Godbole  East Tennessee State University  1/13/2008  1/18/2008 
Laura Rocio GonzalezRamirez  CINVESTAV  1/9/2008  1/18/2008 
Jason E. Gower  University of Minnesota  9/1/2006  8/31/2008 
Sergei Grudinin  INRIA RhoneAlpes Research Unit  1/13/2008  1/18/2008 
Esfandiar Haghverdi  Indiana University  1/2/2008  6/30/2008 
Omar Haq  Rutgers University  1/9/2008  1/18/2008 
Milena Hering  University of Minnesota  9/1/2006  8/31/2008 
Peter Hinow  University of Minnesota  9/1/2007  8/31/2009 
Kenneth Hinson  University of North Carolina  Charlotte  1/13/2008  1/19/2008 
Xia Hua  Massachusetts Institute of Technology  1/13/2008  1/18/2008 
Kimberly Jean Huerter  University of Iowa  1/9/2008  1/11/2008 
Gerhard Hummer  National Institutes of Health (NIH)  1/15/2008  1/18/2008 
Sorin Istrail  Brown University  1/14/2008  1/18/2008 
Filip Jagodzinski  University of Massachusetts  1/9/2008  1/18/2008 
Richard D. James  University of Minnesota  9/4/2007  6/30/2008 
Christopher Jarzynski  University of Maryland  1/13/2008  1/18/2008 
Robert L. Jernigan  Iowa State University  1/13/2008  1/24/2008 
Tiefeng Jiang  University of Minnesota  9/1/2007  6/30/2008 
Christopher Kauffman  University of Minnesota  1/14/2008  1/18/2008 
Yiannis Kaznessis  University of Minnesota  1/15/2008  1/18/2008 
Yannis G. Kevrekidis  Princeton University  1/13/2008  1/18/2008 
Abdul Khaliq  Middle Tennessee State University  1/9/2008  1/19/2008 
Soojeong Kim  University of Iowa  8/30/2007  1/20/2008 
Debra Knisley  East Tennessee State University  8/17/2007  6/1/2008 
Patrice Koehl  University of California  1/9/2008  1/12/2008 
Dmitry A. Kondrashov  University of Chicago  1/13/2008  1/18/2008 
Samuel Kou  Harvard University  1/13/2008  1/18/2008 
Dmytro Kozakov  Boston University  1/13/2008  1/19/2008 
Peter R. Kramer  Rensselaer Polytechnic Institute  1/8/2008  6/30/2008 
Krzysztof Kuczera  University of Kansas  1/13/2008  1/18/2008 
Satish Kumar  University of Minnesota  1/22/2008  1/22/2008 
Christian E. Laing  New York University  1/9/2008  1/18/2008 
Fumei Lam  Brown University  1/13/2008  1/18/2008 
Juan Latorre  Rensselaer Polytechnic Institute  1/10/2008  6/30/2008 
Audrey Lee  University of Massachusetts  1/9/2008  1/18/2008 
Chang Hyeong Lee  Worcester Polytechnic Institute  10/14/2007  1/4/2008 
Christopher J. Lee  University of California  1/10/2008  3/10/2008 
Michael Levitt  Stanford University  1/9/2008  1/18/2008 
Ronald M. Levy  Rutgers University  1/13/2008  1/18/2008 
Robert Michael Lewis  College of William and Mary  1/9/2008  1/19/2008 
Anton Leykin  University of Minnesota  8/16/2006  8/15/2008 
Timothy Lezon  University of Pittsburgh  1/13/2008  1/18/2008 
Florence J. Lin  University of Southern California  1/14/2008  1/17/2008 
Andy Lorenz  Boston College  1/13/2008  1/19/2008 
Roger Lui  Worcester Polytechnic Institute  9/1/2007  6/30/2008 
Laura Lurati  University of Minnesota  9/1/2006  8/31/2008 
Christopher Michael Maloney  Brown University  1/14/2008  1/18/2008 
Yi Mao  Michigan State University  1/13/2008  1/18/2008 
Ezra Miller  University of Minnesota  9/1/2007  6/30/2008 
Kenneth C. Millett  University of California  1/10/2008  2/9/2008 
Julie C. Mitchell  University of Wisconsin  1/13/2008  1/18/2008 
Alejandro Morales Valencia  University of Guadalajara  1/9/2008  1/19/2008 
Naoto Morikawa  GENOCRIPT  1/8/2008  1/12/2008 
Antonio Mucherino  Seconda Università di Napoli  1/7/2008  1/21/2008 
Chitra Narayanan  Rutgers University  1/13/2008  1/18/2008 
Junalyn NavarraMadsen  Texas Woman's University  1/9/2008  1/19/2008 
Timothy Newman  Arizona State University  9/1/2007  6/30/2008 
Duane Nykamp  University of Minnesota  9/1/2007  6/30/2008 
David Odde  University of Minnesota  1/9/2008  6/30/2008 
Charles W. O'Donnell  Massachusetts Institute of Technology  1/13/2008  1/19/2008 
José Nelson Onuchic  University of California, San Diego  1/15/2008  1/17/2008 
Hans G. Othmer  University of Minnesota  9/1/2007  6/30/2008 
Sefika Banu Ozkan  Arizona State University  1/13/2008  1/18/2008 
Sanghyun Park  Argonne National Laboratory  1/13/2008  1/18/2008 
Ioannis Paschalidis  Boston University  1/13/2008  1/18/2008 
Bobby Philip  Los Alamos National Laboratory  1/9/2008  1/18/2008 
Jed W. Pitera  IBM Research Division  1/16/2008  1/18/2008 
Candice Price  University of Iowa  1/9/2008  1/12/2008 
Andrea Pugliese  Università di Trento  1/14/2008  1/18/2008 
Hong Qian  University of Washington  1/13/2008  1/18/2008 
Terrance Quinn  Middle Tennessee State University  1/13/2008  1/18/2008 
Andrew J. Rader  Indiana UniversityPurdue University  1/14/2008  1/23/2008 
Subramanian Ramamoorthy  University of Edinburgh  1/13/2008  1/20/2008 
Rahul Ravindrudu  Iowa State University  1/9/2008  1/18/2008 
Eric Rawdon  University of St. Thomas  1/10/2008  6/30/2008 
Stephane Redon  INRIA RhôneAlpes  1/12/2008  1/18/2008 
Shantanu Roy  Universität Basel  1/11/2008  1/20/2008 
Anchanee Sangcharoen  Mahidol University  1/12/2008  1/18/2008 
Jeffery G. Saven  University of Pennsylvania  1/13/2008  1/18/2008 
Deena Schmidt  University of Minnesota  9/1/2007  8/31/2009 
Tamara SchmidtHegge  University of Minnesota  1/10/2008  1/17/2008 
Brigitte Servatius  Worcester Polytechnic Institute  1/10/2008  2/8/2008 
Chehrzad Shakiban  University of Minnesota  9/1/2006  8/31/2008 
Yang Shen  Boston University  1/13/2008  1/19/2008 
Carlos L. Simmerling  SUNY  1/13/2008  1/18/2008 
Zachariah Sinkala  Middle Tennessee State University  1/12/2008  1/18/2008 
Atilla Sit  Iowa State University  1/11/2008  1/11/2008 
Robert D. Skeel  Purdue University  1/13/2008  1/18/2008 
Daniel Smith  University of Pittsburgh  1/9/2008  1/13/2008 
Carlos Sosa  University of Minnesota  1/14/2008  1/18/2008 
Andrew Stein  University of Minnesota  9/1/2007  8/31/2009 
Benjamin Stottrup  Augsburg College  1/8/2008  1/8/2008 
Ileana Streinu  Smith College  1/9/2008  1/18/2008 
Kirk Sturtz  US Air Force Research Laboratory  1/9/2008  1/9/2008 
Vlad Sukhoy  Iowa State University  1/11/2008  1/11/2008 
Weitao Sun  New Mexico State University  1/9/2008  1/19/2008 
Vladimir Sverak  University of Minnesota  9/1/2007  6/30/2008 
William Swope  IBM  1/13/2008  1/18/2008 
Michael Tomasini  Rutgers University  1/10/2008  1/18/2008 
Alex Tropsha  University of North Carolina  1/7/2008  1/13/2008 
Erkan Tüzel  University of Minnesota  9/1/2007  8/31/2009 
George Vacek  Hewlett Packard  1/13/2008  1/18/2008 
Sandor Vajda  Boston University  1/13/2008  1/19/2008 
Jérôme Waldispühl  Massachusetts Institute of Technology  1/13/2008  1/18/2008 
Jin Wang  SUNY  1/13/2008  1/18/2008 
Zhian Wang  University of Minnesota  9/1/2007  8/31/2009 
Guowei Wei  Michigan State University  1/13/2008  1/18/2008 
Thomas Weikl  Max Planck Institute for Colloids and Interfaces  1/13/2008  1/21/2008 
Lauren Wickstrom  SUNY  1/13/2008  1/18/2008 
Sebastian Will  AlbertLudwigsUniversität Freiburg  1/13/2008  1/20/2008 
Steven Wojtkiewicz  University of Minnesota  1/10/2008  1/11/2008 
Di Wu  Western Kentucky University  1/9/2008  1/18/2008 
Zhijun Wu  Iowa State University  9/4/2007  6/1/2008 
Chuan Xue  University of Minnesota  1/10/2008  1/11/2008 
Jiaan Yang  MicrotechNano  1/13/2008  1/18/2008 
Sichun Yang  University of Chicago  1/13/2008  1/18/2008 
Yaxiang Yuan  Chinese Academy of Sciences  1/9/2008  1/25/2008 
Adam Zemla  Lawrence Livermore National Laboratory  1/13/2008  1/19/2008 
Hongchao Zhang  University of Minnesota  9/1/2006  8/31/2008 
Likun Zheng  University of Minnesota  1/10/2008  1/11/2008 
Carol L. Ecale Zhou  Lawrence Livermore National Laboratory  1/13/2008  1/19/2008 