CHARMM (Chemistry at Harvard Macromolecular Mechanics) has been a primary research tool for macromolecular simulation and modeling in biology for nearly three decades. During this period CHARMM development, applications emerging from its use have defined the field of biomolecular computation. This proposal is aimed at ensuring that 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, continued refactoring of the code will provide improvements and extensions to underlying computational kernels in CHARMM, which will significantly enhance single processor performance and improve parallel scaling. Additional developments will establish a more extensive and user friendly front end for complex simulation preparation using CHARMM-GUI (www.charmm-gui.org) and embracing the emerging big data as drivers of biological simulations approaching cellular level modeling. Emphasis will be given to improving parallelism and accelerated simulations by extending the new and successful DOMDEC kernels for scalable parallel applications, migrating more of CHARMM exceptional functionality to GPU-based accelerators through both DOMDEC and the CHARMM/OpenMM interfaces. Additionally, we will focus on the establishment of a scalable simulation architecture for systems approaching hundreds of millions of particles utilizing both detailed atomic as well as coarse-grained models. 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 simulation and modeling in biology for nearly three 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, carbohydrate and membrane modeling. The more than 25,000 citations to CHARMM attest to the pivotal role that this software package and force field has in the biomedical community. The aims of this proposal are to continue to modernize, improve performance, enable the utilization of large data as a key contributor to macromolecular modeling, maintain to a solid pathway for continued development of the CHARMM package to ensure its ongoing availability for biomedical researchers, and establish a more extensive and user friendly front-end for complex simulation preparation using CHARMM-GUI.
|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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