VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein–Ligand Binding Free Energy Calculations

Wang, Ercheng; Fu, Weitao; Jiang, Dejun; Sun, Huiyong; Wang, Junmei; Zhang, Xujun; Weng, Gaoqi; Liu, Hui; Tao, Peng; Hou, Tingjun

doi:10.1021/acs.jcim.1c00091

Cited by 34 publications

(20 citation statements)

References 66 publications

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“…Pharmacokinetic and toxicological properties provide crucial information on the functioning of drug molecules inside the human body [ 38 ]. In this research article, the apigenin-7- O -glucoside alpha-amylase and alpha-glucosidase complexes’ pharmacokinetic and toxicological properties were calculated from ADMET analysis using SwissADME and QikProp.…”

Section: Discussionmentioning

confidence: 99%

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes

et al. 2022

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Piper betle L. is widely distributed and commonly used medicinally important herb. It can also be used as a medication for type 2 diabetes patients. In this study, compounds of P. betle were screened to investigate the inhibitory action of alpha-amylase and alpha-glucosidase against type 2 diabetes through molecular docking, molecular dynamics simulation, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis. The molecule apigenin-7-O-glucoside showed the highest binding affinity among 123 (one hundred twenty-three) tested compounds. This compound simultaneously bound with the two-target proteins alpha-amylase and alpha-glucosidase, with high molecular mechanics-generalized born surface area (MM/GBSA) values (ΔG Bind = −45.02 kcal mol−1 for alpha-amylase and −38.288 for alpha-glucosidase) compared with control inhibitor acarbose, which had binding affinities of −36.796 kcal mol−1 for alpha-amylase and −29.622 kcal mol−1 for alpha-glucosidase. The apigenin-7-O-glucoside was revealed to be the most stable molecule with the highest binding free energy through molecular dynamics simulation, indicating that it could compete with the inhibitors’ native ligand. Based on ADMET analysis, this phytochemical exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities and had no significant side effects, making them prospective drug candidates for type 2 diabetes. Additional in vitro, in vivo, and clinical investigations are needed to determine the precise efficacy of drugs.

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Section: Discussionmentioning

confidence: 99%

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes

et al. 2022

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“…How to choose an appropriate dielectric constant is usually a vital issue in the end-point binding free energy calculations since it may significantly affect the prediction accuracy. Notably, different types of systems may exhibit different tendencies to the employed dielectric constant [38,[50][51][52][55][56][57][58][59][60][61][62], such as a higher dielectric constant (i.e. ε in = 4) may be a good choice for the protein-ligand systems [51,52,[63][64][65], while a medium dielectric constant (i.e.…”

Section: Impact Of the Dielectric Constantmentioning

confidence: 99%

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Wang

et al. 2022

J Cheminform

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Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract

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“…Scoring functions (SFs) however have much lower computational costs and have found wide application in the prediction of binding affinity in protein–ligand complexes . Classical SFs can be force field-based, , empirical, − or knowledge-based. , Force field-based SFs are usually based on calculated energies, while empirical SFs are based on a hypothetical equation with linear regression (LR) parameters. In knowledge-based SFs, the energy terms are derived from the statistics of protein–ligand interactions.…”

Section: Introductionmentioning

confidence: 99%

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking

Dong

Wang

2022

ACS Omega

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Prediction of protein–ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein–ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein–ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein–ligand complexes.

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VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein–Ligand Binding Free Energy Calculations

Cited by 34 publications

References 66 publications

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking

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