This proposal responds to PA-14-156, Extended Development, Hardening and Dissemination of Technologies in Biomedical Computing, Informatics and Big Data Science. The goal of this program is to support the continued development, maintenance, testing and evaluation of existing software, matching our proposal. Amber is a very popular software package, used by academic and industry institutions, for simulating the structural, thermodynamic and kinetic properties of molecular systems. The free version of Amber has been downloaded by over 10,000 unique users. There are 3,500 citations to the widely adopted Amber ff99SB protein force field, developed at Stony Brook by PI Carlos Simmerling. The Simmerling group is one of the six academic labs responsible for leading the Amber software maintenance and development effort. 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, providing improvements to this widely used program for drug discovery. Other widely used programs also interface with Amber. Productivity in our prior funding period was excellent, leading to articles published in PNAS (2), JACS (3), JCTC (4) and other high-impact journals, with others currently in review. Several new versions of Amber were released. We propose here new developments for Amber, addressing two of 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 fast but reasonably accurate Generalized Born implicit water 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 very fast Kinetic-Loop-Closure and Constrained-Loop-Closure methods for sampling loop conformations. Following our practice over the past 20 years, we will openly share our results, parameters and methods, and incorporate them into the Amber production code for distribution with fully documented source code, along with full documentation, test cases, and tutorials. New versions follow an annual release schedule.
Computer simulations of biomolecules such as proteins and nucleic acids are 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 develop and incorporate advancements into Amber, a software package that is widely used in computer models of biomolecules. We plan to implement our improvements to be highly accessible to the scientific community by developing documentation, code, test cases and tutorials.
| Li, Haoquan; Endutkin, Anton V; Bergonzo, Christina et al. (2017) DNA Deformation-Coupled Recognition of 8-Oxoguanine: Conformational Kinetic Gating in Human DNA Glycosylase. J Am Chem Soc 139:2682-2692 |
| Hauser, Kevin; He, Yiqing; Garcia-Diaz, Miguel et al. (2017) Characterization of Biomolecular Helices and Their Complementarity Using Geometric Analysis. J Chem Inf Model 57:864-874 |
| Perez, Alberto; Morrone, Joseph A; Dill, Ken A (2017) Accelerating physical simulations of proteins by leveraging external knowledge. Wiley Interdiscip Rev Comput Mol Sci 7: |
| 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 |
| Zou, Junjie; Song, Benben; Simmerling, Carlos et al. (2016) Experimental and Computational Analysis of Protein Stabilization by Gly-to-d-Ala Substitution: A Convolution of Native State and Unfolded State Effects. J Am Chem Soc 138:15682-15689 |
| Brini, Emiliano; Paranahewage, S Shanaka; Fennell, Christopher J et al. (2016) Adapting the semi-explicit assembly solvation model for estimating water-cyclohexane partitioning with the SAMPL5 molecules. J Comput Aided Mol Des 30:1067-1077 |
| Hauser, Kevin; Essuman, Bernard; He, Yiqing et al. (2016) A human transcription factor in search mode. Nucleic Acids Res 44:63-74 |
| 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 |
| 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 |
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