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Title: ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution

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 solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Here, 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 alsomore » 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 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.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [3];  [1];  [3]; ORCiD logo [4];  [1]
  1. Stony Brook Univ., Stony Brook, NY (United States). Dept. of Chemistry and Laufer Center for Physical and Quantitative Biology
  2. Stony Brook Univ., Stony Brook, NY (United States). Laufer Center for Physical and Quantitative Biology
  3. Stony Brook Univ., Stony Brook, NY (United States). Dept. of Chemistry
  4. Brookhaven National Lab. (BNL), Upton, NY (United States). Center for Functional Nanomaterials
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1574918
Report Number(s):
BNL-212345-2019-JAAM
Journal ID: ISSN 1549-9618; TRN: US2001253
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 16; Journal Issue: 1; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
77 NANOSCIENCE AND NANOTECHNOLOGY

Citation Formats

Tian, Chuan, Kasavajhala, Koushik, Belfon, Kellon A. A., Raguette, Lauren, Huang, He, Migues, Angela N., Bickel, John, Wang, Yuzhang, Pincay, Jorge, Wu, Qin, and Simmerling, Carlos. ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. United States: N. p., 2019. Web. doi:10.1021/acs.jctc.9b00591.
Tian, Chuan, Kasavajhala, Koushik, Belfon, Kellon A. A., Raguette, Lauren, Huang, He, Migues, Angela N., Bickel, John, Wang, Yuzhang, Pincay, Jorge, Wu, Qin, & Simmerling, Carlos. ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. United States. https://doi.org/10.1021/acs.jctc.9b00591
Tian, Chuan, Kasavajhala, Koushik, Belfon, Kellon A. A., Raguette, Lauren, Huang, He, Migues, Angela N., Bickel, John, Wang, Yuzhang, Pincay, Jorge, Wu, Qin, and Simmerling, Carlos. Tue . "ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution". United States. https://doi.org/10.1021/acs.jctc.9b00591. https://www.osti.gov/servlets/purl/1574918.
@article{osti_1574918,
title = {ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution},
author = {Tian, Chuan and Kasavajhala, Koushik and Belfon, Kellon A. A. and Raguette, Lauren and Huang, He and Migues, Angela N. and Bickel, John and Wang, Yuzhang and Pincay, Jorge and Wu, Qin and Simmerling, Carlos},
abstractNote = {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 solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Here, 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 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.},
doi = {10.1021/acs.jctc.9b00591},
journal = {Journal of Chemical Theory and Computation},
number = 1,
volume = 16,
place = {United States},
year = {2019},
month = {11}
}

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Works referencing / citing this record:

Blinded prediction of protein–ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4
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Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts
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