CHARMM (Chemistry at Harvard Macromolecular Mechanics) has been a primary research tool for macromolecular simulations and modeling in biology for over two decades. During this period CHARMM development, and applications emerging from its use, have defined the field of biomolecular computation. This proposal is aimed at ensuring CHARMM will continue as the development platform for future generations of scientists by addressing components of the software and its attendant support infrastructure that represent bottlenecks in development, performance and maintenance. Specifically, restructuring the code will provide improvements to underlying computational kernels in CHARMM, which will significantly enhance single processor performance and improve parallel scaling. Emphasis will be given to improving parallelism for two target system sizes. For systems of 50-200K atoms, which represent many """"""""typical"""""""" applications, we will develop, adapt and deploy methods that yield good parallel efficiency (greater than 70%) for tightly coupled parallelism in molecular dynamics on """"""""department accessible"""""""" parallel platforms (~100 commodity processors with a high bandwidth interconnect). For large biological problems on large """"""""supercomputers"""""""", i.e. for systems approaching 1M atoms running on 0.5-2K processors, we will develop and implement new simulation kernels for CHARMM that exploit the large spatial dimensions of these systems and employ techniques of spatial decomposition and task-level parallelism. Finally, we will develop and improve code and algorithms allowing graphics processor based acceleration to be utilized for the family of CHARMM potentials and methods. The outcome of these efforts will be a program platform that will facilitate continued forefront research in macromolecular simulation and modeling and enable its continued development and maintenance for future generations of researchers.
CHARMM (Chemistry at Harvard Macromolecular Mechanics) has been a primary research tool for macromolecular simulations and modeling in biology for over two decades, assisting biomedical researchers in theoretical investigations of protein/nucleic acid-ligand interactions, enzyme mechanism, protein/nucleic acid folding, free energy and docking simulations for drug discovery and design, and a large range of other applications in protein, nucleic acid and membrane modeling. The nearly 14,000 citations to CHARMM attests to the pivotal role this software package has in the biomedical community.
The aims of this proposal are to continue to modernize, improve performance, and secure a solid pathway for continued development of the CHARMM package to ensure its ongoing availability for biomedical researchers.
|Heo, Lim; Feig, Michael (2018) PREFMD: a web server for protein structure refinement via molecular dynamics simulations. Bioinformatics 34:1063-1065|
|Nawrocki, Grzegorz; Karaboga, Alp; Sugita, Yuji et al. (2018) Effect of protein-protein interactions and solvent viscosity on the rotational diffusion of proteins in crowded environments. Phys Chem Chem Phys :|
|Hayes, Ryan L; Vilseck, Jonah Z; Brooks 3rd, Charles L (2018) Approaching protein design with multisite ? dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme. Protein Sci 27:1910-1922|
|Feig, Michael; Yu, Isseki; Wang, Po-Hung et al. (2017) Crowding in Cellular Environments at an Atomistic Level from Computer Simulations. J Phys Chem B 121:8009-8025|
|Nawrocki, Grzegorz; Wang, Po-Hung; Yu, Isseki et al. (2017) Slow-Down in Diffusion in Crowded Protein Solutions Correlates with Transient Cluster Formation. J Phys Chem B 121:11072-11084|
|Hsu, Pin-Chia; Bruininks, Bart M H; Jefferies, Damien et al. (2017) CHARMM-GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. J Comput Chem 38:2354-2363|
|Kim, Seonghoon; Lee, Jumin; Jo, Sunhwan et al. (2017) CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules. J Comput Chem 38:1879-1886|
|Arthur, Evan J; Brooks 3rd, Charles L (2016) Parallelization and improvements of the generalized born model with a simple sWitching function for modern graphics processors. J Comput Chem 37:927-39|
|Arthur, Evan J; Brooks 3rd, Charles L (2016) Efficient implementation of constant pH molecular dynamics on modern graphics processors. J Comput Chem 37:2171-80|
|Lee, Jumin; Cheng, Xi; Swails, Jason M et al. (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405-13|
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