A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations

By Y. Duan, Chun Wu, S. Chowdhury, Mathew C. Lee, Guoming Xiong, Rong Yang, P. Cieplak, Ray Luo, Tai-Sung Lee, J. Caldwell and 2 more. Journal of Computational Chemistry, 2003

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Molecular mechanics models have been applied extensively to study the dynamics of proteins and nucleic acids. Here we report the development of a third‐generation point‐charge all‐atom force field for proteins. Following the earlier approach of Cornell et al., the charge set was obtained by fitting to the electrostatic potentials of dipeptides calculated using B3LYP/cc‐pVTZ//HF/6‐31G** quantum mechanical methods. The main‐chain torsion parameters were obtained by fitting to the energy profiles of Ace‐Ala‐Nme and Ace‐Gly‐Nme di‐peptides calculated using MP2/cc‐pVTZ//HF/6‐31G** quantum mechanical methods. All other parameters were taken from the existing AMBER data base. The major departure from previous force fields is that all quantum mechanical calculations were done in the condensed phase with continuum solvent models and an effective dielectric constant of ε = 4. We anticipate that this force field parameter set will address certain critical short comings of previous force fields in condensed‐phase simulations of proteins. Initial tests on peptides demonstrated a high‐degree of similarity between the calculated and the statistically measured Ramanchandran maps for both Ace‐Gly‐Nme and Ace‐Ala‐Nme di‐peptides. Some highlights of our results include (1) well‐preserved balance between the extended and helical region distributions, and (2) favorable type‐II poly‐proline helical region in agreement with recent experiments. Backward compatibility between the new and Cornell et al. charge sets, as judged by overall agreement between dipole moments, allows a smooth transition to the new force field in the area of ligand‐binding calculations. Test simulations on a large set of proteins are also discussed. © 2003 Wiley Periodicals, Inc. J Comput Chem 24: 1999–2012, 2003

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/42181a6b5dd141578a30268047ef56b557ddb0bd

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Citations in the corpus

Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways (2013, methodology): “Protein, water, and ions were parameterized with the AMBER03 force field(1) and lipids with the Berger united atom force field.”
ACPYPE - AnteChamber PYthon Parser interfacE (2012, neutral): “The MKTOP program [11] can define atom types and hence topological parameters, but it cannot derive partial charges and only recently became able to generate topologies for AMBER03 [12] force field (besides the OPLS/AA [13]).”
ProteinGCN: Protein model quality assessment using Graph Convolutional Networks (2020, methodology): “Various methods have been developed to tackle the scoring problem, ranging from energy functions derived from either general physical principles (like a number of popular molecular mechanics force ﬁelds CHARMM [18], AMBER [6], OPLS [13], GROMOS 3.2) and uses GCN to learn the atom embeddings.”
STRUM: structure-based prediction of protein stability changes upon single-point mutation (2016, background): “The second group of energy potential is physics-based from AMBER force field (Duan et al. , 2003).”
Changes in Protein Structural Motifs upon Post-Translational Modification in Kidney Cancer (2021, methodology): “Modeling was performed without considering the explicit water environment in the force field AMBER ff03 [28] at 300 K.”
Systematic Validation of Protein Force Fields against Experimental Data (2012, methodology): “…that our comparison set was comprised of the following eight protein force fields: Amber ff99SB-ILDN [12,14], Amber ff99SB*-ILDN [12–14] Amber ff03 [16], Amber ff03* [13,16], OPLS-AA [17], CHARMM22 [18], CHARMM22 with the CMAP correction ([18,19]; herein termed CHARMM27), and CHARMM22* [15,18,19].”
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, methodology): “r1 [84] and ff99SB [14] force fields to explain the isoform selectivity of the compounds [89].”