We have an exciting line-up of keynote speakers:

• Ronald M. Levy, Rutgers University
• James C. Sacchettini, Texas A&M University
• Shankar Subramaniam, University of California at San Diego

 

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Exploring Landscapes for Protein Folding and Binding using Replica Exchange and Network Models

 

Ronald M. Levy, Board of Governers Professor of Chemistry and Chemical Biology, Rutgers University

 

9:00am, Friday, March 27, 2009

 

Abstract Advances in computational biophysics depend on the development of accurate effective potentials and powerful sampling methods to traverse the rugged energy landscapes that govern protein folding and binding. I will review work in my lab over the last five years concerning the construction of all-atom effective potentials for proteins and multi-scale methods for simulating their folding and binding.

 

The Analytical Generalized Born plus Non-Polar (AGBNP) model is an analytical implicit solvent model with origins in solution physical chemistry that is suitable for modeling solvated peptides, proteins, and small molecule solutes [1]. It is based on an analytical pairwise descreening implementation of the continuum dielectric Generalized Born model and a novel non-polar hydration free energy estimator.  Since its introduction in 2004 AGBNP has been used to study a variety of problems in protein structural biology, including:  peptide folding [2], protein allostery [3,4], and in silico vaccine design [5]. I will describe the latest features of the AGBNP model which now includes the adoption of a molecular surface description of the solute volume, and the modeling of high-occupancy hydration sites.

 

Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of free-energy landscapes which define many challenging problems in computational biophysics, including protein folding and binding.  Although temperature RE 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. We have clarified some of the obstacles to obtaining converged thermodynamic information from RE simulations which I will describe [6,7].

 

Finally, I will present a new multi-scale approach to recover protein folding rates using the combined power of replica exchange, a kinetic network model and effective stochastic dynamics.  While RE simulations generate a large set of discrete states with the correct thermodynamics, kinetic information is lost due to the random exchange of temperatures.  I will discuss how we can recover the kinetics by using RE-generated discrete states as the nodes of a kinetic network.  One of the advantages of the kinetic network model is that the network can be built on a high-dimensional discretized state space, which could consist of multiple paths not consistent with a single reaction coordinate.

 

References

[1]  Gallicchio, E., and R.M. Levy.  AGBNP, an Analytic Implicit Solvent Model Suitable for Molecular Dynamics Simulations and High-Resolution Modeling.  J. Comp. Chem., 25, 479-499 (2004).

[2]  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, 6801-6806 (2005).

[3]  Ravindranathan, K.P., E. Gallicchio, and R.M. Levy.  Conformational Equilibria and Free Energy Profiles for the Allosteric Transition of the Ribose Binding Protein, J. Mol. Biol., 353, 196-210 (2005).

[4]  Ravindranathan, K.P., E. Gallicchio, R.A. Friesner, A.E. McDermott, and R.M. Levy.  Conformational Equilibrium of Cytochrome P450 BM-3 Complexed with N-Palmitoylglycine: A Replica Exchange Molecular Dynamics Study.  J. Am. Chem. Soc., 128, 3786-3791 (2006)

[5]  Lapelosa, M., E. Gallicchio,  G. Ferstandig-Arnold, R.M. Levy, and E. Arnold. In silico vaccine design based on molecular siulations of rhinovirus chimeras presenting HIV-1 gp41 epitopes, J. Mol. Biol., 385, 675-691(2009).

[6]  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, 15340-15345 (2007).

[7] 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 B., 112, 6083-6093 (2008)

 

Biography Ronald M. Levy is Board of Governors Professor of chemistry and chemical biology at Rutgers University. He received his Ph.D in biophysics from Harvard University in 1976 and worked as a postdoc with Martin Karplus at Harvard before joining the faculty at Rutgers University in 1980. His honors include Alfred P. Sloan, NIH Career Development, Japan Society for the Promotion of Science, and John Simon Gugenheim fellowship awards. He is a fellow of the AAAS. Ron has served on many NIH and NSF panels including NSF ITR panels on computational biology and NIH BISTI panels on bioinformatics. He served as a member of the Executive Committee of the American Chemical Society, Biophysical Chemistry Division 1997-2000. Ron currently serves as the director of the BioMaPS Institute for Quantitative Biology at Rutgers and is co-director of the Graduate Program in Computational Biology & Molecular Biophysics. The mission of BioMaPS is to promote research and education in biology at the interface with the mathematical and physical sciences and to stimulate interactions among scientists working on problems in biological physics, computational and systems biology. Ron’s research makes use of a combination of computer simulations, statistical mechanics methods and modeling to study the structure, function, folding, and dynamics of proteins in solution.

 

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Bio- and Chemi-informatics on the Study of Tuberculosis Drug Resistance

 

James C. Sacchettini, Professor of Biochemistry and Biophysics and of Chemistry, Wolfe-Welch Chair in Science, Texas A&M University

 

1:30pm, Thursday, March 26, 2009

 

Abstract A major roadblock in eradicating TB is drug resistance. Drug-resistance and multi-drug resistance is emerging throughout the world at an alarming rate. We have begun a whole genome sequencing program in the Sacchettini laboratory to study the genetic variations which lead to drug resistance, multi-drug resistance and extensively drug resistant bacteria (XDR). We have completed the whole genome sequences of over 50 TB drug resistant strains from around the world, including 15 XDR strains from KwaZulu-Natal South Africa, responsible for killing over 50 people in a recent XDR TB outbreak. We have also sequenced MDR and XDR isolates of the extremely virulent Beijing strains, recently discovered in several areas of the world. These studies have allowed us to define what mutations within the genome allow the bacteria to become drug resistant, and provide a framework for the development of new technologies to diagnose drug resistant TB.

 

Biography James C. Sacchettini received his undergraduate degree from St. Louis University in 1980 and his Ph.D. in Molecular Biology (Biochemistry) from Washington University School of Medicine (1984 – 1987). After postdoctoral studies at Washington University he joined the faculty of the Department of Biochemistry at Albert Einstein College of Medicine in 1990. Dr. Sacchettini moved to Texas A&M University in 1996 as a Professor in the Department of Biochemistry and Biophysics with a joint appointment in the Department of Chemistry. He is also the Wolfe-Welch Chair in Sciences at Texas A&M University, the Director of the Center for Structural Biology, the Director of the TB Structural Genomics Consortium and a member of the faculty of the Institute of Biosciences and Technology in Houston, TX.

 

Professor Sacchettini’s research has primarily focused on the structure-based design and synthesis of novel compounds which are being tested as drug candidates against tuberculosis and malaria. Tuberculosis and malaria are the two most deadly infectious diseases, worldwide. The Sacchettini lab uses protein crystallography to visualize proteins at their atomic resolution and computer programs to make designer inhibitors. The lab is also applying these techniques to Cancer and Alzheimer’s disease.

 

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Systems Biology: Mechanisms, Networks, Models and Phenotypes

 

Shankar Subramaniam, Professor of Bioengineering, Chemistry, and Biochemistry, Cellular and Molecular Medicine and Nano Engineering, University of California at San Diego

 

1:30pm, Friday, March 27, 2009

 

Abstract The ‘state’ of a cell is defined by its components – their concentrations and locations, the interactions between components – that are modulated in space and time, and the complex circuitry, that involves a large number of interacting networks. The state represents a snapshot of the dynamical processes – such as gene expression, cell cycle, transport of components, etc, that characterize the cell function. Advances in high-throughput genomic, metabolomic and proteomic technologies now allow the study of the cellular components and their interactions in a quantitative manner. These technologies are aiding in the development of predictive models by combining legacy knowledge and novel data. There are two paradigms in computational systems biology: (1) the iterative cycle of biochemical model – mathematical model – computational model, and (2) integration of novel data and legacy knowledge to develop context specific biochemical, mathematical and computational models.

 

This talk will review the challenges in developing such models through well-characterized exemplar problems in biology. Challenges in building biochemical models include (1) the complexity of proteomic states and interactions, (2) integration of diverse data to infer biochemical interactions, and (3) temporal state of biochemical models. Challenges in building mathematical models include (1) incorporating statistical/probabilistic information into analytical models, (2) utilizing qualitative constraints into mathematical models, and (3) incomplete knowledge and coarse-graining. Challenges in computational modeling are: (1) the absence of knowledge about model parameters such as rate constants, (2) local versus global concentrations of species and multiple length and time-scales, and (3) variation among different cell-types and sub-populational variability or variability among biological repeats.

 

I will describe one model problem from my laboratory in more detail highlighting the above challenges. This will involve the total lipidomic response in macrophage cells to inflammatory stimulus by KDO2-lipid A from quantitative mass spectrometric measurements, transcriptional measurements and integrative data analysis. The analysis of lipidomic and concomitant transcriptomic measurements demonstrate early responses in fatty acid metabolism through increased eicosanoid cascade, later responses through increases in a large number of sphingolipids and a long term response in the biosynthesis of sterols. The analysis also demonstrates the mechanisms of lipid remodeling involving all classes of mammalian lipids, thus providing a systems-level view of lipid pathways in activated macrophages. Lipid remodeling in activated macrophages include acyl coA remodeling into phospholipids and mitochondria-driven remodeling to acetyl coA. The latter serves as a precursor for sterol biosynthesis. Pharmacological perturbations of the lipid cascade through the addition of statin drugs demonstrate both the expected sterol changes and the unexpected changes in other lipid classes. To the best of our knowledge this is most comprehensive network map of lipids in mammalian cells along with a dynamical perspective on changes associated with macrophage stimulation. The comprehensive lipidomic network, a dynamical network of lipid metabolism in activated macrophages, and the effects on this network of a pharmacological perturbation will also be presented in this lecture.

 

Funding Support: NIGMS, NIDDK, NHLBI, NSF and the State of California.

 

Biography Shankar Subramaniam is a Professor of Bioengineering, Chemistry and Biochemistry, Cellular and Molecular Medicine and Nano Engineering. He is currently the Chair of the Bioengineering Department at the University of California at San Diego.  He holds the inaugural Joan and Irwin Jacobs Endowed Chair in Bioengineering and Systems Biology. He was the Founding Director of the Bioinformatics Graduate Program at the University of California at San Diego.  He also has adjunct Professorships at the Salk Institute for Biological Studies and the San Diego Supercomputer Center. He is also a Guest Professor at the Center for Molecular Biology and Neuroscience at the University of Oslo in Norway and Professor at the Center for Cardiovascular Bioinformatics and Modeling at Johns Hopkins University. Prior to moving to UC San Diego, Dr. Subramaniam was a Professor of Biophysics, Biochemistry, Molecular and Integrative Physiology, Chemical Engineering and Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC).  He was the Director of the Bioinformatics and Computational Biology Program at the National Center for Supercomputing Applications and the Co-Director of the W.M. Keck Center for Comparative and Functional Genomics at UIUC. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and is a recipient of Smithsonian Foundation and Association of Laboratory Automation Awards and his research work is described below. In 2002 he received the Genome Technology All Star Award. In 2008 he was awarded the Faculty Excellence in Research Award at the University of California at San Diego. His research spans several areas of bioinformatics and systems biology.

 

Subramaniam has played a key role in raising national awareness for training and research in bioinformatics.  He served as a member of the National Institute for Health (NIH) Director’s Advisory Committee on Bioinformatics, which resulted in the BIOMEDICAL INFORMATION SCIENCE AND TECHNOLOGY INITIATIVE (BISTI) report.  The report recognized the dire need for trained professionals in Bioinformatics and recommended the launching of a strong NIH funding initiative.  Dr. Subramaniam served as the Chair of a NIH BISTI Study Section.  Dr. Subramaniam has also served on Bioinformatics and Biotechnology Advisory Councils for Virginia Tech, the University of Illinois at Chicago, and on the Scientific Advisory Board of several Biotech and Bioinformatics Companies.  Dr. Subramaniam has served as a member of the State of Illinois Governor’s initiative in Biotechnology and an advisor and reviewer of the State of North Carolina initiative in Biotechnology.  He is currently an overseas advisor for the Department of Biotechnology of the Government of India, and a member of a European Science Foundation Panel.

 

Research in Subramaniam laboratory spans several areas of bioinformatics and systems biology.  In bioinformatics he is involved in developing novel strategies for identifying protein interaction networks, intracellular localization of proteins and identification of functional networks in cells. In systems biology he is involved in deciphering mammalian cellular networks from high throughput and phenotypic data and in developing strategies for modeling cellular signaling networks. He collaborates with biomedical scientists towards understanding diseases associated with insulin resistance and inflammation. His laboratory is interested in mapping the circuitry of the macrophage cells.

 

He continues to be engaged in developing state-of-the-art infrastructure for bioinformatics. The Molecule Pages Database has been recognized as the most innovative informatics resource for signaling proteins and received the ALSIP award. The integration of highly innovative and complex computer science strategies with expert-driven curation has led to the Molecule Pages Database that provides comprehensive information on all known functional states of signaling molecules. The LipidMaps database serves as the first and only integrated resource for mammalian lipids along with their complementary gene and protein data. The microarray server, widely used by the research community combines sophisticated statistical analysis methods developed in the Subramaniam laboratory with biochemical annotations and pathways to provide biological insights into consequences of transcriptional changes in mammalian cells.

 

http://genome.ucsd.edu/

http://www.signaling-gateway.org/

http://www.lipidmaps.org

http://workbench.sdsc.edu

http://www.bioinformatics.ucsd.edu

 

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