Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo

被引:22
|
作者
Burley, Kalistyn H. [1 ]
Gill, Samuel C. [2 ]
Lim, Nathan M. [1 ]
Mobley, David L. [1 ,2 ]
机构
[1] Univ Calif Irvine, Dept Pharmaceut Sci, Irvine, CA 92697 USA
[2] Univ Calif Irvine, Dept Chem, Irvine, CA 92617 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
BINDING FREE-ENERGIES; ALANINE DIPEPTIDE; LIGAND-BINDING; T4; LYSOZYME; MOLECULAR-DYNAMICS; CONFORMATIONAL-ANALYSIS; NONPOLAR CAVITY; SIMULATIONS; PREFERENCES; SPECIFICITY;
D O I
10.1021/acs.jctc.8b01018
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Molecular simulations are a valuable tool for studying bomolecular motions and thermodynamics. However, such motions can be slow compared to simulation time scales, yet critical Specifically, adequate sampling of side chain motions in protein binding pockets is crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The time scale of side chain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed nonequilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of side chain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target side chain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine side chain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances side chain sampling, especially in systems where the torsional barrier to rotation is high (>= 10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment. Overall, this may provide a promising strategy to selectively improve side chain sampling in molecular simulations.
引用
收藏
页码:1848 / 1862
页数:15
相关论文
共 50 条
  • [21] MCCE2: Improving Protein pKa Calculations with Extensive Side Chain Rotamer Sampling
    Song, Yifan
    Mao, Junjun
    Gunner, M. R.
    JOURNAL OF COMPUTATIONAL CHEMISTRY, 2009, 30 (14) : 2231 - 2247
  • [22] Efficient Underground Formation Characterization Using Transitional Markov Chain Monte Carlo Sampling
    Lu, Han
    Chen, Jiefu
    Wu, Xuqing
    Fu, Xin
    Khalil, Mohammad
    Safta, Cosmin
    Huang, Yueqin
    2020 IEEE USNC-CNC-URSI NORTH AMERICAN RADIO SCIENCE MEETING (JOINT WITH AP-S SYMPOSIUM), 2020, : 131 - 132
  • [23] ENHANCED MIXTURE POPULATION MONTE CARLO VIA STOCHASTIC OPTIMIZATION AND MARKOV CHAIN MONTE CARLO SAMPLING
    El-Laham, Yousef
    Djuric, Petar M.
    Bugallo, Monica F.
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 5475 - 5479
  • [24] Sampling from complicated and unknown distributions Monte Carlo and Markov Chain Monte Carlo methods for redistricting
    Cho, Wendy K. Tam
    Liu, Yan Y.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 506 : 170 - 178
  • [25] Markov Chain Monte Carlo posterior sampling with the Hamiltonian method
    Hanson, KM
    MEDICAL IMAGING: 2001: IMAGE PROCESSING, PTS 1-3, 2001, 4322 : 456 - 467
  • [26] Respondent-driven sampling as Markov chain Monte Carlo
    Goel, Sharad
    Salganik, Matthew J.
    STATISTICS IN MEDICINE, 2009, 28 (17) : 2202 - 2229
  • [27] Markov chain Monte Carlo on optimal adaptive sampling selections
    Chao, CT
    ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2003, 10 (01) : 129 - 151
  • [28] ON THE OPTIMAL TRANSITION MATRIX FOR MARKOV CHAIN MONTE CARLO SAMPLING
    Chen, Ting-Li
    Chen, Wei-Kuo
    Hwang, Chii-Ruey
    Pai, Hui-Ming
    SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2012, 50 (05) : 2743 - 2762
  • [29] A simple introduction to Markov Chain Monte-Carlo sampling
    van Ravenzwaaij, Don
    Cassey, Pete
    Brown, Scott D.
    PSYCHONOMIC BULLETIN & REVIEW, 2018, 25 (01) : 143 - 154
  • [30] Accelerating Markov Chain Monte Carlo sampling with diffusion models ☆
    Hunt-Smith, N. T.
    Melnitchouk, W.
    Ringer, F.
    Sato, N.
    Thomas, A. W.
    White, M. J.
    COMPUTER PHYSICS COMMUNICATIONS, 2024, 296