Evaluating the Performance of Water Models with Host–Guest Force Fields in Binding Enthalpy Calculations for Cucurbit[7]uril–Guest Systems

The influence of water models SPC, SPC/E, TIP3P, and TIP4P on ligand binding affinity is examined by calculating the binding free energyΔGbindof oseltamivir carboxylate (Tamiflu) to the wild type of glycoprotein neuraminidase from the pandemic A/H5N1 virus.ΔGbindis estimated by the Molecular Mechanic-Poisson Boltzmann Surface Area method and all-atom simulations with different combinations of these aqueous models and four force fields AMBER99SB, CHARMM27, GROMOS96 43a1, and OPLS-AA/L. It is shown that there is no correlation between the binding free energy and the water density in the binding pocket in CHARMM. However, for three remaining force fieldsΔGbinddecays with increase of water density. SPC/E provides the lowest binding free energy for any force field, while the water effect is the most pronounced in CHARMM. In agreement with the popular GROMACS recommendation, the binding score obtained by combinations of AMBER-TIP3P, OPLS-TIP4P, and GROMOS-SPC is the most relevant to the experiments. For wild-type neuraminidase we have found that SPC is more suitable for CHARMM than TIP3P recommended by GROMACS for studying ligand binding. However, our study for three of its mutants reveals that TIP3P is presumably the best choice for CHARMM.

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The accuracy of force fields on the simulation of intrinsically disordered proteins: A benchmark test on the human p53 tumor suppressor

Journal of Theoretical and Computational Chemistry ◽

10.1142/s021963362050011x ◽

2020 ◽

Vol 19 (04) ◽

pp. 2050011

Author(s):

Shangbo Ning ◽

Jun Liu ◽

Na Liu ◽

Dazhong Yan

Keyword(s):

Md Simulation ◽

Intrinsically Disordered Proteins ◽

Force Fields ◽

Chemical Shifts ◽

Disordered Proteins ◽

Experimental Measurements ◽

Intrinsically Disordered ◽

Structural Ensembles ◽

Water Models ◽

Structural Ensemble

Intrinsically disordered proteins (IDPs) are a class of proteins without stable three-dimensional structures under physiological conditions. IDPs exhibit high dynamic nature and could be described by structural ensembles. As one of the most widely used tools, molecular dynamics (MD) simulation could provide the atomic descriptions of the structural ensemble of IDPs. However, the accuracy of the MD simulation largely depends on the accuracy of the force field. In this paper, we compared the structural ensembles of the activation domain 1 (AD1) in p53 tumor suppressor obtained from the widely used force fields, AMBER99SB-ILDN, CHARMM27, CHARMM36m with different water models. The results show that CHARMM36m generates more extended conformations than other force fields, while CHARMM27 prefers to sample the [Formula: see text]-helical structure. Moreover, the chemical shifts obtained by CHARMM36m are the closest to the experimental measurements. These results indicate that the CHARMM36m force field performs best in characterizing the structure properties of p53 AD1. Water models are also critical to describe the structural ensemble of IDPs. TIP4P water model can obtain more extended conformations and produce more local helical conformations than the TIP3P model in our simulation. In addition, we also compare the chemical shifts predicted by different chemical shift predicting programs with experimental measurements, the results show that SHIFTX2 obtains the best performance in the chemical shifts prediction.

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Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz143 ◽

2019 ◽

Vol 21 (6) ◽

pp. 2112-2125

Author(s):

Süleyman Selim Çınaroğlu ◽

Emel Timuçin

Keyword(s):

Prediction Accuracy ◽

Comprehensive Evaluation ◽

Force Fields ◽

Water Model ◽

Receptor Ligand ◽

Computational Costs ◽

Scoring Schemes ◽

Water Models ◽

The One ◽

Amber Force Fields

Abstract MM-PB/GBSA methods represent a higher-level scoring theory than docking. This study reports an extensive testing of different MM-GBSA scoring schemes on two bromodomain (BRD) datasets. The first set is composed of 24 BRPF1 complexes, and the second one is a nonredundant set constructed from the PDBbind and composed of 28 diverse BRD complexes. A variety of MM-GBSA schemes were analyzed to evaluate the performance of four protocols with different numbers of minimization and MD steps, 10 different force fields and three different water models. Results showed that neither additional MD steps nor unfixing the receptor atoms improved scoring or ranking power. On the contrary, our results underscore the advantage of fixing receptor atoms or limiting the number of MD steps not only for a reduction in the computational costs but also for boosting the prediction accuracy. Among Amber force fields tested, ff14SB and its derivatives rather than ff94 or polarized force fields provided the most accurate scoring and ranking results. The TIP3P water model yielded the highest scoring and ranking power compared to the others. Posing power was further evaluated for the BRPF1 set. A slightly better posing power for the protocol which uses both minimization and MD steps with a fixed receptor than the one which uses only minimization with a fully flexible receptor-ligand system was observed. Overall, this study provides insights into the usage of the MM-GBSA methods for screening of BRD inhibitors, substantiating the benefits of shorter protocols and latest force fields and maintaining the crystal waters for accuracy.

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Hydration Thermodynamic Properties of Amino Acid Analogues: A Systematic Comparison of Biomolecular Force Fields and Water Models

The Journal of Physical Chemistry B ◽

10.1021/jp0641029 ◽

2006 ◽

Vol 110 (35) ◽

pp. 17616-17626 ◽

Cited By ~ 219

Author(s):

Berk Hess ◽

Nico F. A. van der Vegt

Keyword(s):

Amino Acid ◽

Thermodynamic Properties ◽

Force Fields ◽

Systematic Comparison ◽

Amino Acid Analogues ◽

Water Models

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Testing the Limitations of MD-based Local Electric Fields Using the Vibrational Stark Effect in Solution: Penicillin G as a Test Case

10.26434/chemrxiv.13621421.v1 ◽

2021 ◽

Author(s):

Jacek Kozuch ◽

Samuel Schneider ◽

Chu Zheng ◽

Zhe Ji ◽

Richard T Bradshaw ◽

...

Keyword(s):

Electric Fields ◽

Active Sites ◽

Stark Effect ◽

Force Fields ◽

Direct Method ◽

Md Simulations ◽

Penicillin G ◽

Conformational Sampling ◽

Test Case ◽

Water Models

<div>Non-covalent interactions underlie nearly all molecular processes in the condensed phase from solvation to</div><div>catalysis. Their quantification within a physically consistent framework remains challenging. Experimental vibrational Stark effect (VSE)-based solvatochromism can be combined with molecular dynamics (MD) simulations to quantify the electrostatic forces in solute-solvent interactions for small rigid molecules and, by extension, when these solutes bind in enzyme active sites. While generalizing this approach towards more complex (bio)molecules, such as the conformationally flexible and charged penicillin G (PenG), we were surprised to observe inconsistencies in MD-based electric fields. Combining synthesis, VSE spectroscopy, and computational methods, we provide an intimate view on the origins of these discrepancies. We observe that the electrics fields are correlated to conformation-dependent effects of the flexible PenG side-chain, including both local solvation structure and solute conformational sampling in MD. Additionally, we identified that MD-based electric fields are consistently overestimated in 3-point water models in the vicinity of charged groups; this cannot be entirely ameliorated using polarizable force fields (AMOEBA) or advanced water models. This work demonstrates the value of the VSE as a direct method for experiment-guided refinements of MD force fields and establishes a general reductionist approach to calibrating vibrational probes for complex (bio)molecules.</div>

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The Dependence of Amyloid-β Dynamics on Protein Force Fields and Water Models

ChemPhysChem ◽

10.1002/cphc.201500415 ◽

2015 ◽

Vol 16 (15) ◽

pp. 3278-3289 ◽

Cited By ~ 69

Author(s):

Arun Kumar Somavarapu ◽

Kasper P. Kepp

Keyword(s):

Force Fields ◽

Amyloid Β ◽

Water Models ◽

Protein Force Fields

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