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

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.

[1]  A. Onufriev,et al.  Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies , 2017, J. Chem. Inf. Model..

[2]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[3]  Gregory D. Hawkins,et al.  Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium , 1996 .

[4]  Zhihai Liu,et al.  Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results , 2014, J. Chem. Inf. Model..

[5]  Christian Kramer,et al.  MM/GBSA Binding Energy Prediction on the PDBbind Data Set: Successes, Failures, and Directions for Further Improvement , 2013, J. Chem. Inf. Model..

[6]  Samuel Genheden,et al.  Comparison of end‐point continuum‐solvation methods for the calculation of protein–ligand binding free energies , 2012, Proteins.

[7]  Pedro Alexandrino Fernandes,et al.  Computational alanine scanning mutagenesis—An improved methodological approach , 2007, J. Comput. Chem..

[8]  D. Case,et al.  Generalized Born Implicit Solvent Models for Biomolecules. , 2019, Annual review of biophysics.

[9]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[10]  Emil Alexov,et al.  DelPhi Suite: New Developments and Review of Functionalities , 2019, J. Comput. Chem..

[11]  Cen Gao,et al.  Accounting for ligand conformational restriction in calculations of protein-ligand binding affinities. , 2010, Biophysical journal.

[12]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[13]  Vincent Le Guilloux,et al.  fpocket: online tools for protein ensemble pocket detection and tracking , 2010, Nucleic Acids Res..

[14]  Weitao Fu,et al.  Discovery a novel antifungal compound against Fusarium graminearum species-specifically through an integrated molecular modeling strategy. , 2020, Pest management science.

[15]  Lin Li,et al.  DelPhi: a comprehensive suite for DelPhi software and associated resources , 2012, BMC biophysics.

[16]  S. Brooks,et al.  Optimization Using Simulated Annealing , 1995 .

[17]  Tingjun Hou,et al.  Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking , 2011, J. Comput. Chem..

[18]  D. Case,et al.  Modification of the Generalized Born Model Suitable for Macromolecules , 2000 .

[19]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[20]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[21]  Jeremy C. Smith,et al.  Nonuniform charge scaling (NUCS): A practical approximation of solvent electrostatic screening in proteins , 2005, J. Comput. Chem..

[22]  Benoît Roux,et al.  Computations of Absolute Solvation Free Energies of Small Molecules Using Explicit and Implicit Solvent Model. , 2009, Journal of chemical theory and computation.

[23]  Wei Zhang,et al.  A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations , 2003, J. Comput. Chem..

[24]  Chris Eliasmith,et al.  Hyperopt: a Python library for model selection and hyperparameter optimization , 2015 .

[25]  Lina Baranauskienė,et al.  Binding affinity in drug design: experimental and computational techniques , 2019, Expert opinion on drug discovery.

[26]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[27]  Jonathan C. Fuller,et al.  Protein Binding Pocket Dynamics. , 2016, Accounts of chemical research.

[28]  Yan Li,et al.  Comparative Assessment of Scoring Functions: The CASF-2016 Update , 2018, J. Chem. Inf. Model..

[29]  Mohamed A. Khamis,et al.  Comparative assessment of machine-learning scoring functions on PDBbind 2013 , 2015, Eng. Appl. Artif. Intell..

[30]  Wei Yang,et al.  Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential. , 2012, Journal of chemical theory and computation.

[31]  Lars Olsen,et al.  Binding affinities in the SAMPL3 trypsin and host–guest blind tests estimated with the MM/PBSA and LIE methods , 2012, Journal of Computer-Aided Molecular Design.

[32]  Tai-Sung Lee,et al.  GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features , 2018, J. Chem. Inf. Model..

[33]  Dan Li,et al.  Novel androgen receptor antagonist identified by structure-based virtual screening, structural optimization, and biological evaluation. , 2020, European journal of medicinal chemistry.

[34]  Homme W Hellinga,et al.  An empirical model for electrostatic interactions in proteins incorporating multiple geometry‐dependent dielectric constants , 2003, Proteins.

[35]  Junmei Wang,et al.  End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. , 2019, Chemical reviews.

[36]  Lin Li,et al.  On the Dielectric “Constant” of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi , 2013, Journal of chemical theory and computation.

[37]  Yu Kang,et al.  Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities , 2020, J. Chem. Inf. Model..

[38]  Lauren Wickstrom,et al.  Secondary structure bias in generalized Born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation. , 2007, The journal of physical chemistry. B.

[39]  Xiao Liu,et al.  Accurate and Efficient Calculation of Protein-Protein Binding Free Energy-Interaction Entropy with Residue Type-Specific Dielectric Constants. , 2018, Journal of chemical information and modeling.

[40]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[41]  C. R. W. Guimarães.  A Direct Comparison of the MM-GB/SA Scoring Procedure and Free-Energy Perturbation Calculations Using Carbonic Anhydrase as a Test Case: Strengths and Pitfalls of Each Approach. , 2011, Journal of chemical theory and computation.

[42]  Vincent Le Guilloux,et al.  Fpocket: An open source platform for ligand pocket detection , 2009, BMC Bioinformatics.

[43]  Jie Li,et al.  PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..

[44]  D. Case,et al.  Exploring protein native states and large‐scale conformational changes with a modified generalized born model , 2004, Proteins.

[45]  S. Genheden,et al.  The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities , 2015, Expert opinion on drug discovery.

[46]  Ray Luo,et al.  Heterogeneous Dielectric Implicit Membrane Model for the Calculation of MMPBSA Binding Free Energies , 2019, J. Chem. Inf. Model..

[47]  Christopher I. Bayly,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation , 2002, J. Comput. Chem..

[48]  Scott P. Brown,et al.  Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes. , 2009, Journal of medicinal chemistry.

[49]  Ray Luo,et al.  Virtual screening using molecular simulations , 2011, Proteins.

[50]  Kai Zhu,et al.  Improved Methods for Side Chain and Loop Predictions via the Protein Local Optimization Program:  Variable Dielectric Model for Implicitly Improving the Treatment of Polarization Effects. , 2007, Journal of chemical theory and computation.

[51]  Julian Tirado-Rives,et al.  Improving MM-GB/SA Scoring through the Application of the Variable Dielectric Model. , 2011, Journal of chemical theory and computation.

[52]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[53]  Jignasa K. Savjani,et al.  Drug Solubility: Importance and Enhancement Techniques , 2012, ISRN pharmaceutics.

[54]  H. Edelsbrunner,et al.  Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design , 1998, Protein science : a publication of the Protein Society.

[55]  W. Sherman,et al.  Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field. , 2010, Journal of chemical theory and computation.

[56]  R. Friesner,et al.  The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling , 2011, Proteins.

[57]  Jie Li,et al.  Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set , 2014, J. Chem. Inf. Model..

[58]  Youyong Li,et al.  Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models. , 2013, The journal of physical chemistry. B.

[59]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .