This proposal responds to PAR-11-028, Continued Development and Maintenance of Software: The goal of this program is to support the continued development, maintenance, testing and evaluation of existing software. Amber is a popular software package, licensed to over 800 academic and industry institutions, for simulating the structural, thermodynamic and kinetic properties of molecular systems. There are 885 citations to the popular Amber ff99SB force field, developed at Stony Brook by PI Carlos Simmerling. The Simmerling group is one of the six academic groups responsible for Amber maintenance and development. Computational Molecular Dynamics simulations using Amber and other software packages have become essential counterparts to experimental research for understanding the mechanisms of biomolecules, and for discovering drugs to inhibit them. The popular virtual screening program called DOCK interfaces directly with Amber. One goal of the software improvements proposed here is to reduce the time it takes to develop new drugs. We propose here new developments for Amber, addressing the most pressing needs of the field.
Aim 1, Solvation: We will incorporate improved solvation models: (a) the SEA semi-explicit water model and (b) a new Generalized Born model.
Aim 2, Sampling: We will add fast and targeted sampling methods: (a) variants of the general tools Hamiltonian exchange and thermal Replica Exchange Molecular Dynamics, (b) the new Modeling with Limited Data method, which samples conformations subject to sparse and noisy data;(c) the very fast Kinetic-Loop-Closure and Constrained-Loop-Closure methods for sampling loop conformations, and (d) the Confine and Transition method, which computes free- energy differences between two different conformations of a biomolecule. We will incorporate our recently developed algorithms into the Amber production code for distribution, and port the codes to the recently developed GPU version of Amber using CUDA. !

Public Health Relevance

Computer simulations of biomolecules such as proteins and nucleic acids has now become an essential companion to experiments for understanding biological mechanisms at the molecular level and for designing more effective drugs and therapies. We are proposing to incorporate advancements into software that is widely used in computer models of biomolecule properties. We plan to implement our improvements to be highly accessible to the scientific community by developing documentation, code, test cases and tutorials for the popular Amber suite of programs and continue to provide hands-on support at Amber user workshops.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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State University New York Stony Brook
Schools of Arts and Sciences
Stony Brook
United States
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Perez, Alberto; Morrone, Joseph A; Simmerling, Carlos et al. (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36:25-31
Perez, Alberto; Morrone, Joseph A; Brini, Emiliano et al. (2016) Blind protein structure prediction using accelerated free-energy simulations. Sci Adv 2:e1601274
Maier, James A; Martinez, Carmenza; Kasavajhala, Koushik et al. (2015) ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J Chem Theory Comput 11:3696-713
Perez, Alberto; MacCallum, Justin L; Coutsias, Evangelos A et al. (2015) Constraint methods that accelerate free-energy simulations of biomolecules. J Chem Phys 143:243143
Perez, Alberto; MacCallum, Justin L; Brini, Emiliano et al. (2015) Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations. J Chem Theory Comput 11:4770-9
Nguyen, Hai; Pérez, Alberto; Bermeo, Sherry et al. (2015) Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins. J Chem Theory Comput 11:3714-28
Perez, Alberto; MacCallum, Justin L; Dill, Ken A (2015) Accelerating molecular simulations of proteins using Bayesian inference on weak information. Proc Natl Acad Sci U S A 112:11846-51
Nguyen, Hai; Maier, James; Huang, He et al. (2014) Folding simulations for proteins with diverse topologies are accessible in days with a physics-based force field and implicit solvent. J Am Chem Soc 136:13959-62