دورية أكاديمية
أعرض في EDS

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

التفاصيل البيبلوغرافية
العنوان: GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.
المؤلفون: Zhao M; Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA., Kognole AA; SilcsBio LLC, Baltimore, Maryland, USA., Jo S; SilcsBio LLC, Baltimore, Maryland, USA., Tao A; SilcsBio LLC, Baltimore, Maryland, USA., Hazel A; Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA., MacKerell AD Jr; Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA.
المصدر: Journal of computational chemistry [J Comput Chem] 2023 Jul 30; Vol. 44 (20), pp. 1719-1732. Date of Electronic Publication: 2023 Apr 24.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: United States NLM ID: 9878362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-987X (Electronic) Linking ISSN: 01928651 NLM ISO Abbreviation: J Comput Chem Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: New York : Wiley,
مستخلص: The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μ ex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μ ex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μ ex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.
(© 2023 Wiley Periodicals LLC.)
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معلومات مُعتمدة: R35 GM131710 United States GM NIGMS NIH HHS; R35GM131710 United States NH NIH HHS
فهرسة مساهمة: Keywords: SILCS; chemical potential; co-solvent molecular dynamics; computer-aided drug design; enhanced solute sampling
تواريخ الأحداث: Date Created: 20230424 Date Completed: 20230620 Latest Revision: 20240921
رمز التحديث: 20240921
مُعرف محوري في PubMed: PMC10330275
DOI: 10.1002/jcc.27121
PMID: 37093676
قاعدة البيانات: MEDLINE
الوصف
تدمد:1096-987X
DOI:10.1002/jcc.27121