Literature DB >> 21391583

Development of polarizable models for molecular mechanical calculations II: induced dipole models significantly improve accuracy of intermolecular interaction energies.

Junmei Wang1, Piotr Cieplak, Jie Li, Jun Wang, Qin Cai, MengJuei Hsieh, Hongxing Lei, Ray Luo, Yong Duan.   

Abstract

In the companion paper, we presented a set of induced dipole interaction models using four types of screening functions, which include the Applequist (no screening), the Thole linear, the Thole exponential model, and the Thole Tinker-like (another form of exponential screening function) functions. In this work, we evaluate the performance of polarizability models using a large set of amino acid analog pairs in conformations that are frequently observed in protein structures as a benchmark. For each amino acid pair, we calculated quantum mechanical interaction energies at the MP2/aug-cc-pVTZ//MP2/6-311++G(d,p) level with the basis set superposition error (BSSE) correction and compared them with molecular mechanics results. Encouragingly, all polarizable models significantly outperform the additive F94 and F03 models (mimicking AMBER ff94/ff99 and ff03 force fields, respectively) in reproducing the BSSE-corrected quantum mechanical interaction energies. In particular, the root-mean-square errors (RMSEs) for three Thole models in Set A (where the 1-2 and 1-3 interactions are turned off and all 1-4 interactions are included) are 1.456, 1.417, and 1.406 kcal/mol for model AL (Thole Linear), model AE (Thole exponential), and model AT (Thole Tinker-like), respectively. In contrast, the RMSEs are 3.729 and 3.433 kcal/mol for F94 and F03 models, respectively. A similar trend was observed for the average unsigned errors (AUEs), which are 1.057, 1.025, 1.011, 2.219, and 2.070 kcal/mol for AL, AE, AT, F94/ff99, and F03, respectively. Analyses based on the trend line slopes indicate that the two fixed charge models substantially underestimate the relative strengths of noncharge-charge interactions by 24 (F03) and 35% (F94), respectively, whereas the four polarizable models overestimate the relative strengths by 5 (AT), 3 (AL, AE), and 13% (AA), respectively. Agreement was further improved by adjusting the van der Waals parameters. Judging from the notably improved accuracy in comparison with the fixed charge models, the polarizable models are expected to form the foundation for the development of high quality polarizable force fields for protein and nucleic acid simulations.

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Year:  2011        PMID: 21391583      PMCID: PMC3082585          DOI: 10.1021/jp1121382

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  21 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  Force fields for protein simulations.

Authors:  Jay W Ponder; David A Case
Journal:  Adv Protein Chem       Date:  2003

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

Authors:  Yong Duan; Chun Wu; Shibasish Chowdhury; Mathew C Lee; Guoming Xiong; Wei Zhang; Rong Yang; Piotr Cieplak; Ray Luo; Taisung Lee; James Caldwell; Junmei Wang; Peter Kollman
Journal:  J Comput Chem       Date:  2003-12       Impact factor: 3.376

4.  CHARMM fluctuating charge force field for proteins: II protein/solvent properties from molecular dynamics simulations using a nonadditive electrostatic model.

Authors:  Sandeep Patel; Alexander D Mackerell; Charles L Brooks
Journal:  J Comput Chem       Date:  2004-09       Impact factor: 3.376

5.  Simulation of Ca2+ and Mg2+ solvation using polarizable atomic multipole potential.

Authors:  Dian Jiao; Christopher King; Alan Grossfield; Thomas A Darden; Pengyu Ren
Journal:  J Phys Chem B       Date:  2006-09-21       Impact factor: 2.991

6.  Interaction energies for the purine inhibitor roscovitine with cyclin-dependent kinase 2: correlated ab initio quantum-chemical, DFT and empirical calculations.

Authors:  Petr Dobes; Michal Otyepka; Miroslav Strnad; Pavel Hobza
Journal:  Chemistry       Date:  2006-05-24       Impact factor: 5.236

7.  Development of polarizable models for molecular mechanical calculations I: parameterization of atomic polarizability.

Authors:  Junmei Wang; Piotr Cieplak; Jie Li; Tingjun Hou; Ray Luo; Yong Duan
Journal:  J Phys Chem B       Date:  2011-03-10       Impact factor: 2.991

8.  Development of a polarizable intermolecular potential function (PIPF) for liquid amides and alkanes.

Authors:  Wangshen Xie; Jingzhi Pu; Alexander D Mackerell; Jiali Gao
Journal:  J Chem Theory Comput       Date:  2007       Impact factor: 6.006

9.  Polarizable force field development and molecular dynamics simulations of ionic liquids.

Authors:  Oleg Borodin
Journal:  J Phys Chem B       Date:  2009-08-20       Impact factor: 2.991

10.  Strike a balance: optimization of backbone torsion parameters of AMBER polarizable force field for simulations of proteins and peptides.

Authors:  Zhi-Xiang Wang; Wei Zhang; Chun Wu; Hongxing Lei; Piotr Cieplak; Yong Duan
Journal:  J Comput Chem       Date:  2006-04-30       Impact factor: 3.376

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  41 in total

1.  Application of molecular dynamics simulations in molecular property prediction II: diffusion coefficient.

Authors:  Junmei Wang; Tingjun Hou
Journal:  J Comput Chem       Date:  2011-09-22       Impact factor: 3.376

Review 2.  Classical electrostatics for biomolecular simulations.

Authors:  G Andrés Cisneros; Mikko Karttunen; Pengyu Ren; Celeste Sagui
Journal:  Chem Rev       Date:  2013-08-27       Impact factor: 60.622

3.  Polarizable molecular dynamics simulations of ionic liquids: Influence of temperature control.

Authors:  Esther Heid; Stefan Boresch; Christian Schröder
Journal:  J Chem Phys       Date:  2020-03-07       Impact factor: 3.488

Review 4.  Molecular modeling of nucleic acid structure: energy and sampling.

Authors:  T E Cheatham; B R Brooks; P A Kollman
Journal:  Curr Protoc Nucleic Acid Chem       Date:  2001-05

Review 5.  Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics.

Authors:  A T Hagler
Journal:  J Comput Aided Mol Des       Date:  2018-11-30       Impact factor: 3.686

6.  Recent Force Field Strategies for Intrinsically Disordered Proteins.

Authors:  Junxi Mu; Hao Liu; Jian Zhang; Ray Luo; Hai-Feng Chen
Journal:  J Chem Inf Model       Date:  2021-02-16       Impact factor: 4.956

7.  Numerical interpretation of molecular surface field in dielectric modeling of solvation.

Authors:  Changhao Wang; Li Xiao; Ray Luo
Journal:  J Comput Chem       Date:  2017-03-20       Impact factor: 3.376

Review 8.  Biomolecular electrostatics and solvation: a computational perspective.

Authors:  Pengyu Ren; Jaehun Chun; Dennis G Thomas; Michael J Schnieders; Marcelo Marucho; Jiajing Zhang; Nathan A Baker
Journal:  Q Rev Biophys       Date:  2012-11       Impact factor: 5.318

9.  Emerging topics in structure-based virtual screening.

Authors:  Giulio Rastelli
Journal:  Pharm Res       Date:  2013-03-07       Impact factor: 4.200

10.  A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins.

Authors:  Li Xiao; Jianxiong Diao; D'Artagnan Greene; Junmei Wang; Ray Luo
Journal:  J Chem Theory Comput       Date:  2017-06-14       Impact factor: 6.006

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