Literature DB >> 31714766

ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution.

Chuan Tian1,2, Koushik Kasavajhala1,2, Kellon A A Belfon1,2, Lauren Raguette1,2, He Huang1,2, Angela N Migues2, John Bickel1, Yuzhang Wang1,2, Jorge Pincay1, Qin Wu3, Carlos Simmerling1,2.   

Abstract

Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

Entities:  

Year:  2019        PMID: 31714766     DOI: 10.1021/acs.jctc.9b00591

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  138 in total

1.  Blinded prediction of protein-ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4.

Authors:  Junjie Zou; Chuan Tian; Carlos Simmerling
Journal:  J Comput Aided Mol Des       Date:  2019-09-25       Impact factor: 3.686

2.  Driving torsion scans with wavefront propagation.

Authors:  Yudong Qiu; Daniel G A Smith; Chaya D Stern; Mudong Feng; Hyesu Jang; Lee-Ping Wang
Journal:  J Chem Phys       Date:  2020-06-28       Impact factor: 3.488

3.  Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Authors:  Tai-Sung Lee; Bryce K Allen; Timothy J Giese; Zhenyu Guo; Pengfei Li; Charles Lin; T Dwight McGee; David A Pearlman; Brian K Radak; Yujun Tao; Hsu-Chun Tsai; Huafeng Xu; Woody Sherman; Darrin M York
Journal:  J Chem Inf Model       Date:  2020-09-16       Impact factor: 4.956

4.  Computing Absolute Free Energy with Deep Generative Models.

Authors:  Xinqiang Ding; Bin Zhang
Journal:  J Phys Chem B       Date:  2020-11-03       Impact factor: 2.991

5.  A fast and high-quality charge model for the next generation general AMBER force field.

Authors:  Xibing He; Viet H Man; Wei Yang; Tai-Sung Lee; Junmei Wang
Journal:  J Chem Phys       Date:  2020-09-21       Impact factor: 3.488

6.  Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models.

Authors:  Arkajyoti Sengupta; Zhen Li; Lin Frank Song; Pengfei Li; Kenneth M Merz
Journal:  J Chem Inf Model       Date:  2021-02-04       Impact factor: 4.956

7.  Systematic Parametrization of Divalent Metal Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models.

Authors:  Zhen Li; Lin Frank Song; Pengfei Li; Kenneth M Merz
Journal:  J Chem Theory Comput       Date:  2020-06-29       Impact factor: 6.006

8.  SidechainNet: An all-atom protein structure dataset for machine learning.

Authors:  Jonathan Edward King; David Ryan Koes
Journal:  Proteins       Date:  2021-07-12

9.  Preparing and Analyzing Polarizable Molecular Dynamics Simulations with the Classical Drude Oscillator Model.

Authors:  Justin A Lemkul
Journal:  Methods Mol Biol       Date:  2021

10.  A physiologic rise in cytoplasmic calcium ion signal increases pannexin1 channel activity via a C-terminus phosphorylation by CaMKII.

Authors:  Ximena López; Nicolás Palacios-Prado; Juan Güiza; Rosalba Escamilla; Paola Fernández; José L Vega; Maximiliano Rojas; Valeria Marquez-Miranda; Eduardo Chamorro; Ana M Cárdenas; María Constanza Maldifassi; Agustín D Martínez; Yorley Duarte; Fernando D González-Nilo; Juan C Sáez
Journal:  Proc Natl Acad Sci U S A       Date:  2021-08-10       Impact factor: 11.205

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.