The Seventh q-bio Summer School: Biomolecular Simulations

Description of Theme As system biology thrives to excel in providing cellular level behavior of complex biological systems, it has become imperative to integrate the molecular level events for better understanding at the system level. The objective of this theme is to provide training on computational methodologies to extract molecular level events at different resolutions. In addition we intend to provide a brief review on recent theoretical and computational methods and state-of the-art computing architectures. Finally, we will use examples from our own research to show that the real strength of these computational methodologies can only be materialized when combined with experimental studies. This section of the summer school is organized by "Gnana" S Gnanakaran. Please address all questions about this section of the summer school to its organizer.

Speakers

Lecturers

• "Gnana" S Gnanakaran, Los Alamos National Laboratory
• Christoph Junghans, Los Alamos National Laboratory
• Giovanni Bellesia, Los Alamos National Laboratory & University of California, Santa Barbara
• Roland Schulz Oakridge national Laboratory & University of Tennessee
• Anurag Sethi, Los Alamos National Laboratory & University of New Mexico
• Mike Crowley, national Renewable Energy Laboratory
• Anton Zilman, University of Toronto
• Bridget Wilson, University of new Mexico

Mentors

• "Gnana" S Gnanakaran, Los Alamos National Laboratory
• Christoph Junghans, Los Alamos National Laboratory
• Giovanni Bellesia, Los Alamos National Laboratory & University of California, Santa Barbara

Course Materials

Topics

• Introduction to Computational Structural Biology
• Molecular Modeling Approaches for biomolecular recognition
• Quantum mechanics
• Methods for enzyme catalysis
• Protein Dynamics using Molecular simulation methods
• Molecular communication over long distances using network theory approaches
• Coarse-graining approaches to access biologically relevant time and length scales
• Limitations: Dealing In vivo conditions, Sampling and force fields

Lectures & Tutorials

• Introduction to molecular dynamics simulation

• Applications to signaling proteins and quick introduction to free energy calculation methods

• Monte Carlo methods in structural biology

• Coarse-grained models for biomacromolecules

• Examples of coarse grain models (proteins + lipids + nucleic acids + carbohydrates)

• Enhanced sampling methods in classical molecular simulations

• Calculation of macroscropic observables and comparison with experiments

Tutorials:

• VMD

• NAMD

• Free energy calculation with NAMD

• Metropolis Monte Carlo and the 2D ising model - code writing from scratch (Java or Fortran 90 or C)

• A simple polymer model for proteins - implementation in NAMD (writing topology and parameter files - starting simulations)

• Membrane proteins (VMD + Gromacs)

Homework

• To be announced.

Software

It is HIGHLY RECOMMENDED for the students to have the following software installed and working on their laptops for the tutorial sessions in the afternoon.

• VMD [[1]]
• NAMD [[2]]
• Gromacs [[3]]
• C compiler
• CARMA [[4]]

Suggested Reading

The papers recommended for the spatio-temporal modeling journal club are listed below. The students will choose an article to present and discuss during the Journal club session.

• Equation of state calculations by fast computing machines. Metropolis et al. Journal of Chemical Physics 21(6), 1087-1092 (1953).

• Understanding modern molecular dynamics: techniques and applications M.E. Tuckerman and G.J. Martyna, Journal of Physical Chemistry B 104(2), 159-178 (2000)

• M. Levitt, A Simplified Representation of Protein Conformations for Rapid Simulation of Protein Folding. J. Mol. Biol. 104, 59-107 (1976)

• D.E. Shaw et al. Atomic-Level Characterization of the Structural Dynamics of Proteins Science 330(6002), 341-346 (2010)

• M.A. Fisher et al. De Novo Designed Proteins from a Library of Artificial Sequences Function in Escherichia Coli and Enable Cell Growth PLoS One 2011, 6(1): e15364

• Y.C. Kim and G. Hummer. Coarse-grained models for simulations of multi-protein complexes Application to ubiquitin binding. J Mol. Biol. 2008, 375:1416-1433

• H Zhou. Quantitative Relation between intermolecule and intramolecular binding to pro-rich peptides to SH3 domains. Biophys. J. 2006, 91: 3170-3181

• P.C. Whitford, et al., Conformational Transitions of Adenylate Kinase: Switching by Cracking. J. Mol. Biol., 2007, 366: 1661-1671.

• S Yang, N.K. Banavali, and B. Roux, Mapping the conformational transition in Src activation by cumulating the information from multiple molecular dynamics trajectories. PNAS, 2009, 106:3776-3781.

• M Karelson and V.S. Lobanov, and A. R. Katritzky Quantum-Chemical Descriptors in QSAR/QSPR Studies Chem. Rev., 1996, 96 (3), pp 1027–1044

• A. Cavalli, P. Carloni, and M. Recanatini, Target-Related Applications of First Principles Quantum Chemical Methods in Drug Design. Chem. Rev., 2006, 106 (9), pp 3497–3519

• M. Lundberg, T. Kawatsu, T. Vreven, M. J. Frisch, and K. Morokuma, Transition States in the Protein Environment -- ONIOM QM:MM Modeling of Isopenicillin N Synthesis,” J. Chem. Theory and Comput., 5 (2009) 222-34.

• K. Morokuma, D. G. Musaev, T. Vreven, H. Basch, M. Torrent and D. V. Khoroshun, "Model studies of the structure, reactivities, and reaction mechanisms of Metalloenzymes," IBM Journal of Research & Development 45(May/July 2001) 367.

• Stabilization and Structure Calculations for Noncovalent Interactions in Extended Molecular Systems Based on Wave Function and Density Functional Theories Kevin E. Riley, Michal Pitok, Petr Jureka, and Pavel Hobza Chem. Rev., 2010, 110 (9), pp 5023–5063

• Simulation Studies of Protein Folding/Unfolding Equilibrium under Polar and Nonpolar Confinement. Tian et al. J. Am. Chem. Soc. 2011, 133, 15157-15164

• Pathway and mechanism of drug binding to G-protein-coupled receptors. Dror et al. Proceedings of the National Academy of Sciences 2011, 108, 13118-13123

• Quantifying Intramolecular Binding in Multivalent Interactions: A Structure-Based Synergistic Study on Grb2-Sos1 Complex. PLoS Computational Biology 2011, 7, e1002192

• Simulation studies of protein-induced bilayer deformations, and lipid-induced protein tilting, on a mesoscopic model for lipid bilayers with embedded proteins. Venturoli et al. Biophys. J. 2005, 88, 1778-1798

• Lipids on the move: Simulations of membrane pores, domains, stalks and curves. Marrink et al. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2009, 1788, 149-168

• Computational techniques for efficient conformational sampling of proteins. Liwo et al. Current Opinion in Structural Biology 2008, 18, 134–139.

• Multiscale Modeling of Proteins. Tozzini et al. Acc. Chem. Res. 2010, 43, 220–230

• Sending Signals Dynamically. Smock and Gierarsch, Science, 2009, 324.

• Dynamic Control of signaling by modular adaptor proteins. Tony Pawson, Curr Opin Cell Biol, 2007, 19:112-116.

• Sequence periodicity and secondary structure propensity in model proteins. Bellesia et al. Protein Sci. 2010, 19, 141-154.

Group Projects

• To be announced.

Ongoing projects at Gnana's group