An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

[1]  Fedor N. Novikov,et al.  Lead Finder docking and virtual screening evaluation with Astex and DUD test sets , 2012, Journal of Computer-Aided Molecular Design.

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

[3]  Julian Tirado-Rives,et al.  Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. , 2006, Journal of medicinal chemistry.

[4]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[5]  Pietro Cozzini,et al.  Mapping the energetics of water-protein and water-ligand interactions with the "natural" HINT forcefield: predictive tools for characterizing the roles of water in biomolecules. , 2006, Journal of molecular biology.

[6]  Araz Jakalian,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method , 2000 .

[7]  A Lavecchia,et al.  Virtual screening strategies in drug discovery: a critical review. , 2013, Current medicinal chemistry.

[8]  Tingjun Hou,et al.  Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. , 2014, Physical chemistry chemical physics : PCCP.

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

[10]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[11]  D. Fabbro,et al.  Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. , 2003, Journal of medicinal chemistry.

[12]  Nisha S. Sipes,et al.  Rational design of small molecule inhibitors targeting RhoA subfamily Rho GTPases. , 2012, Chemistry & biology.

[13]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

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

[15]  Richard A. Friesner,et al.  Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide , 2012, Journal of Computer-Aided Molecular Design.

[16]  I. Enyedy,et al.  Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer screening. , 2001, Journal of medicinal chemistry.

[17]  Robert Abel,et al.  Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods. , 2014, Journal of chemical theory and computation.

[18]  A. Corsini,et al.  Role of Small GTPase Protein Rac1 in Cardiovascular Diseases: Development of New Selective Pharmacological Inhibitors , 2013, Journal of cardiovascular pharmacology.

[19]  Alfonso T. García-Sosa,et al.  Hydration Properties of Ligands and Drugs in Protein Binding Sites: Tightly-Bound, Bridging Water Molecules and Their Effects and Consequences on Molecular Design Strategies , 2013, J. Chem. Inf. Model..

[20]  C. Vlaar,et al.  Novel inhibitors of Rac1 in metastatic breast cancer. , 2010, Puerto Rico health sciences journal.

[21]  P. Kollman,et al.  Automatic atom type and bond type perception in molecular mechanical calculations. , 2006, Journal of molecular graphics & modelling.

[22]  N. Demitri,et al.  Investigation of 2‐Fold Disorder of Inhibitors and Relative Potency by Crystallizations of HIV‐1 Protease in Ritonavir and Saquinavir Mixtures , 2011 .

[23]  I. Enyedy,et al.  Structure-based approach for the discovery of bis-benzamidines as novel inhibitors of matriptase. , 2001, Journal of medicinal chemistry.

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

[25]  Sudipto Mukherjee,et al.  Evaluation of DOCK 6 as a pose generation and database enrichment tool , 2012, Journal of Computer-Aided Molecular Design.

[26]  O. Korb,et al.  NMR‐Guided Molecular Docking of a Protein–Peptide Complex Based on Ant Colony Optimization , 2010, ChemMedChem.

[27]  Thomas Stützle,et al.  PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design , 2006, ANTS Workshop.

[28]  Volkhard Helms,et al.  Adhesive water networks facilitate binding of protein interfaces. , 2011, Nature communications.

[29]  L. Cubano,et al.  Characterization of EHop-016, Novel Small Molecule Inhibitor of Rac GTPase* , 2012, The Journal of Biological Chemistry.

[30]  Irene T. Weber,et al.  Amprenavir complexes with HIV‐1 protease and its drug‐resistant mutants altering hydrophobic clusters , 2010, The FEBS journal.

[31]  Stefano Forli,et al.  A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. , 2012, Journal of medicinal chemistry.

[32]  Sihyun Ham,et al.  Dynamics of Hydration Water Plays a Key Role in Determining the Binding Thermodynamics of Protein Complexes , 2017, Scientific Reports.

[33]  Carlos Simmerling,et al.  Improved Generalized Born Solvent Model Parameters for Protein Simulations. , 2013, Journal of chemical theory and computation.

[34]  Andreas W. Götz,et al.  SPFP: Speed without compromise - A mixed precision model for GPU accelerated molecular dynamics simulations , 2013, Comput. Phys. Commun..

[35]  Thomas E. Exner,et al.  pKa based protonation states and microspecies for protein–ligand docking , 2010, J. Comput. Aided Mol. Des..

[36]  Yong-Liang Zhu,et al.  Including Explicit Water Molecules as Part of the Protein Structure in MM/PBSA Calculations , 2014, J. Chem. Inf. Model..

[37]  Michael M. Mysinger,et al.  Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.

[38]  Holger Gohlke,et al.  MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. , 2012, Journal of chemical theory and computation.

[39]  Ricardo L. Mancera,et al.  WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes , 2003, Journal of molecular modeling.

[40]  Matthew P. Repasky,et al.  WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking. , 2016, Journal of medicinal chemistry.

[41]  Xiaohua Zhang,et al.  Toward Fully Automated High Performance Computing Drug Discovery: A Massively Parallel Virtual Screening Pipeline for Docking and Molecular Mechanics/Generalized Born Surface Area Rescoring to Improve Enrichment , 2014, J. Chem. Inf. Model..

[42]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[43]  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.

[44]  Duncan Poole,et al.  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born , 2012, Journal of chemical theory and computation.

[45]  Alessandro Contini,et al.  Explicit Ligand Hydration Shells Improve the Correlation between MM-PB/GBSA Binding Energies and Experimental Activities. , 2013, Journal of chemical theory and computation.

[46]  Irene Maffucci and Alessandro Contini.  Tuning the Solvation Term in the MM-PBSA/GBSA Binding Affinity Predictions , 2015 .

[47]  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..

[48]  C. Zhan,et al.  Theoretical calculation of the binding free energies for pyruvate dehydrogenase E1 binding with ligands. , 2007, Bioorganic & medicinal chemistry letters.

[49]  Brian K. Shoichet,et al.  Structure-Based Discovery of a Novel, Noncovalent Inhibitor of AmpC β-Lactamase , 2002 .

[50]  Felice C Lightstone,et al.  Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap , 2013, Expert opinion on drug discovery.

[51]  D. Corrada,et al.  Structural and dynamic roles of permanent water molecules in ligand molecular recognition by chicken liver bile acid binding protein , 2008, Journal of molecular recognition : JMR.

[52]  Oliver Korb,et al.  Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test , 2012, Journal of Computer-Aided Molecular Design.

[53]  Samuel Genheden,et al.  Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach , 2014, Journal of Molecular Modeling.

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

[55]  Kerim Babaoglu,et al.  Deconstructing fragment-based inhibitor discovery , 2006, Nature chemical biology.

[56]  Jacob Kongsted,et al.  Accurate predictions of nonpolar solvation free energies require explicit consideration of binding-site hydration. , 2011, Journal of the American Chemical Society.

[57]  Felice C. Lightstone,et al.  Accounting for water molecules in drug design , 2011, Expert opinion on drug discovery.

[58]  M. James,et al.  Macrocyclic Inhibitors of Penicillopepsin. 2. X-ray Crystallographic Analyses of Penicillopepsin Complexed with a P3−P1 Macrocyclic Peptidyl Inhibitor and with Its Two Acyclic Analogues , 1998 .

[59]  P. Kollman,et al.  Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. , 2001, Journal of the American Chemical Society.

[60]  Ashutosh Kumar,et al.  Investigation on the Effect of Key Water Molecules on Docking Performance in CSARdock Exercise , 2013, J. Chem. Inf. Model..

[61]  Mark McGann,et al.  FRED and HYBRID docking performance on standardized datasets , 2012, Journal of Computer-Aided Molecular Design.

[62]  P. Kollman,et al.  Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding , 2000 .

[63]  Alfonso T García-Sosa,et al.  Free Energy Calculations of Mutations Involving a Tightly Bound Water Molecule and Ligand Substitutions in a Ligand‐Protein Complex , 2010, Molecular informatics.

[64]  Klaus R. Liedl,et al.  A challenging system: Free energy prediction for factor Xa , 2011, J. Comput. Chem..

[65]  Thomas Stützle,et al.  An ant colony optimization approach to flexible protein–ligand docking , 2007, Swarm Intelligence.

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

[67]  A. Ferrari,et al.  Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. , 2007, Bioorganic & medicinal chemistry.

[68]  Istvan J. Enyedy,et al.  Can we use docking and scoring for hit-to-lead optimization? , 2008, J. Comput. Aided Mol. Des..

[69]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

[70]  F. Clerici,et al.  3-Aryl-N-aminoylsulfonylphenyl-1H-pyrazole-5-carboxamides: a new class of selective Rac inhibitors , 2013 .

[71]  Philip C Biggin,et al.  Statistical Analysis on the Performance of Molecular Mechanics Poisson–Boltzmann Surface Area versus Absolute Binding Free Energy Calculations: Bromodomains as a Case Study , 2017, J. Chem. Inf. Model..

[72]  J. Madura,et al.  Structure modeling, ligand binding, and binding affinity calculation (LR‐MM‐PBSA) of human heparanase for inhibition and drug design , 2006, Proteins.

[73]  Duncan Poole,et al.  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. , 2013, Journal of chemical theory and computation.

[74]  Robert T. C. Brownlee,et al.  Effect of atomic charge, solvation, entropy, and ligand protonation state on MM‐PB(GB)SA binding energies of HIV protease , 2012, J. Comput. Chem..

[75]  M. Mahdavi,et al.  Inhibition of cell proliferation and induction of apoptosis in K562 human leukemia cells by the derivative (3-NpC) from dihydro-pyranochromenes family. , 2015, Acta biochimica Polonica.

[76]  A. Marchand,et al.  Rational design of small-molecule inhibitors of the LEDGF/p75-integrase interaction and HIV replication. , 2010, Nature chemical biology.

[77]  D. Scott,et al.  Small molecules, big targets: drug discovery faces the protein–protein interaction challenge , 2016, Nature Reviews Drug Discovery.

[78]  J. Andrew McCammon,et al.  MM-PBSA Captures Key Role of Intercalating Water Molecules at a Protein−Protein Interface , 2009, Journal of chemical theory and computation.

[79]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[80]  Gregory A Ross,et al.  Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites , 2012, PloS one.

[81]  Thomas E. Exner,et al.  Influence of Protonation, Tautomeric, and Stereoisomeric States on Protein-Ligand Docking Results , 2009, J. Chem. Inf. Model..

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

[83]  Lance Stewart,et al.  The mechanism of topoisomerase I poisoning by a camptothecin analog , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[84]  F. Clerici,et al.  2-Amino-3-(phenylsulfanyl)norbornane-2-carboxylate: an appealing scaffold for the design of Rac1-Tiam1 protein-protein interaction inhibitors. , 2014, Journal of medicinal chemistry.

[85]  Youyong Li,et al.  Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. , 2014, Physical chemistry chemical physics : PCCP.

[86]  Bing Wang,et al.  The role of quantum mechanics in structure-based drug design. , 2007, Drug discovery today.

[87]  Gerhard Klebe,et al.  Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. , 2003, Journal of molecular biology.

[88]  Michael H. Gelb,et al.  Crystallographic analysis of transition state mimics bound to penicillopepsin: difluorostatine- and difluorostatone-containing peptides. , 1994 .

[89]  Chi‐Huey Wong,et al.  HIV-1 protease: mechanism and drug discovery. , 2003, Organic & biomolecular chemistry.

[90]  K. Rossman,et al.  Crystal structure of Rac1 in complex with the guanine nucleotide exchange region of Tiam1 , 2000, Nature.

[91]  G S Weston,et al.  Three-dimensional structure of AmpC beta-lactamase from Escherichia coli bound to a transition-state analogue: possible implications for the oxyanion hypothesis and for inhibitor design. , 1998, Biochemistry.

[92]  U. Ryde,et al.  Ligand affinities predicted with the MM/PBSA method: dependence on the simulation method and the force field. , 2006, Journal of Medicinal Chemistry.

[93]  Tingjun Hou,et al.  Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations , 2011, J. Chem. Inf. Model..

[94]  António J. M. Ribeiro,et al.  Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. , 2013, Current medicinal chemistry.

[95]  F. Clerici,et al.  Virtual screening approach for the identification of new Rac1 inhibitors. , 2009, Journal of medicinal chemistry.

[96]  Paul D Lyne,et al.  Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. , 2006, Journal of medicinal chemistry.

[97]  W. Punch,et al.  Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm. , 1997, Journal of molecular biology.

[98]  Yi Zheng,et al.  Rational design and characterization of a Rac GTPase-specific small molecule inhibitor. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[99]  Robert Abel,et al.  Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding , 2007, Proceedings of the National Academy of Sciences.

[100]  Ruben Abagyan,et al.  Docking and scoring with ICM: the benchmarking results and strategies for improvement , 2012, Journal of Computer-Aided Molecular Design.

[101]  Jessica Holien,et al.  Improvements, trends, and new ideas in molecular docking: 2012–2013 in review , 2015, Journal of molecular recognition : JMR.

[102]  Matthias Rarey,et al.  UNICON: A Powerful and Easy-to-Use Compound Library Converter , 2016, J. Chem. Inf. Model..

[103]  Mohan R. Pradhan,et al.  Small Molecules Targeting the Inactive Form of the Mnk1/2 Kinases , 2017, ACS omega.

[104]  Jan Hermans,et al.  Promise of advances in simulation methods for protein crystallography: implicit solvent models, time-averaging refinement, and quantum mechanical modeling. , 2003, Methods in enzymology.

[105]  Thomas Stützle,et al.  Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS , 2009, J. Chem. Inf. Model..

[106]  Alessandro Contini,et al.  Improved Computation of Protein-Protein Relative Binding Energies with the Nwat-MMGBSA Method , 2016, J. Chem. Inf. Model..

[107]  Woody Sherman,et al.  Contribution of Explicit Solvent Effects to the Binding Affinity of Small‐Molecule Inhibitors in Blood Coagulation Factor Serine Proteases , 2011, ChemMedChem.

[108]  Holger Claussen,et al.  Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function , 2012, Journal of Computer-Aided Molecular Design.

[109]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[110]  Rachelle J Bienstock,et al.  Computational drug design targeting protein-protein interactions. , 2012, Current pharmaceutical design.

[111]  A. Ortiz,et al.  Assessment of solvation effects on calculated binding affinity differences: trypsin inhibition by flavonoids as a model system for congeneric series. , 1997, Journal of medicinal chemistry.

[112]  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..