Tunable, mixed-resolution modeling using library-based monte carlo and graphics processing units

Artem B. Mamonov, Steven Lettieri, Ying Ding, Jessica L. Sarver, Rohith Palli, Timothy F. Cunningham, Sunil Saxena, Daniel M. Zuckerman

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Building on our recently introduced library-based Monte Carlo (LBMC) approach, we describe a flexible protocol for mixed coarse-grained (CG)/all-atom (AA) simulation of proteins and ligands. In the present implementation of LBMC, protein side chain configurations are precalculated and stored in libraries, while bonded interactions along the backbone are treated explicitly. Because the AA side chain coordinates are maintained at minimal run-time cost, arbitrary sites and interaction terms can be turned on to create mixed-resolution models. For example, an AA region of interest such as a binding site can be coupled to a CG model for the rest of the protein. We have additionally developed a hybrid implementation of the generalized Born/surface area (GBSA) implicit solvent model suitable for mixed-resolution models, which in turn was ported to a graphics processing unit (GPU) for faster calculation. The new software was applied to study two systems: (i) the behavior of spin labels on the B1 domain of protein G (GB1) and (ii) docking of randomly initialized estradiol configurations to the ligand binding domain of the estrogen receptor (ERα). The performance of the GPU version of the code was also benchmarked in a number of additional systems.

Original languageEnglish (US)
Pages (from-to)2921-2929
Number of pages9
JournalJournal of Chemical Theory and Computation
Volume8
Issue number8
DOIs
StatePublished - Aug 14 2012

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ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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