Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

[1]  Flavio Rizzolio,et al.  Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects , 2019, Journal of cellular physiology.

[2]  Maurizio Fermeglia,et al.  Homology Model and Docking-Based Virtual Screening for Ligands of the σ1 Receptor. , 2011, ACS medicinal chemistry letters.

[3]  Yoshiaki Nakagawa,et al.  Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA. , 2019, Bioorganic & medicinal chemistry.

[4]  Koji Tsuda,et al.  Machine learning accelerates MD-based binding pose prediction between ligands and proteins , 2017, Bioinform..

[5]  Sanna P Niinivehmas,et al.  Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening. , 2015, Journal of molecular graphics & modelling.

[6]  Walter Filgueira de Azevedo,et al.  Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. , 2017, Combinatorial chemistry & high throughput screening.

[7]  B. Kuhn,et al.  Validation and use of the MM-PBSA approach for drug discovery. , 2005, Journal of medicinal chemistry.

[8]  F. Kirchhoff,et al.  An optimized MM/PBSA virtual screening approach applied to an HIV‐1 gp41 fusion peptide inhibitor , 2011, Proteins.

[9]  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..

[10]  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.

[11]  Yu-chian Chen Beware of docking! , 2015, Trends in pharmacological sciences.

[12]  Abdulmujeeb T. Onawole,et al.  Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring , 2017, Comput. Biol. Chem..

[13]  P. Kollman,et al.  Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. , 2000, Journal of medicinal chemistry.

[14]  C. Supuran,et al.  Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors , 2018, International journal of molecular sciences.

[15]  Douglas R. Houston,et al.  Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context , 2013, J. Chem. Inf. Model..

[16]  T. Langer,et al.  Binding investigation and preliminary optimisation of the 3-amino-1,2,4-triazin-5(2H)-one core for the development of new Fyn inhibitors , 2018, Journal of enzyme inhibition and medicinal chemistry.

[17]  T. Tuccinardi Docking-based virtual screening: recent developments. , 2009, Combinatorial chemistry & high throughput screening.

[18]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[19]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[20]  Stefano Moro,et al.  Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview , 2018, Front. Pharmacol..

[21]  Xiao Hu,et al.  Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants , 2019, J. Chem. Inf. Model..

[22]  Lingling Jiang,et al.  Pharmacophore-Based Similarity Scoring for DOCK , 2014, The journal of physical chemistry. B.

[23]  O. Werz,et al.  A Multi‐step Virtual Screening Protocol for the Identification of Novel Non‐acidic Microsomal Prostaglandin E2 Synthase‐1 (mPGES‐1) Inhibitors , 2018, ChemMedChem.

[24]  Chao Shen,et al.  From machine learning to deep learning: Advances in scoring functions for protein–ligand docking , 2019, WIREs Computational Molecular Science.

[25]  Fabrizio Manetti,et al.  Construction and Validation of a RET TK Catalytic Domain by Homology Modeling , 2007, J. Chem. Inf. Model..

[26]  Irina S Moreira,et al.  Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes. , 2013, Journal of chemical theory and computation.

[27]  Steven W. Muchmore,et al.  POSIT: Flexible Shape-Guided Docking For Pose Prediction , 2015, J. Chem. Inf. Model..

[28]  Xiang Li,et al.  Statistical analysis of EGFR structures’ performance in virtual screening , 2015, Journal of Computer-Aided Molecular Design.

[29]  Mark McGann,et al.  FRED Pose Prediction and Virtual Screening Accuracy , 2011, J. Chem. Inf. Model..

[30]  Flavio Rizzolio,et al.  Identification of New Fyn Kinase Inhibitors Using a FLAP-Based Approach , 2013, J. Chem. Inf. Model..

[31]  Michael Levitt,et al.  Finite‐difference solution of the Poisson–Boltzmann equation: Complete elimination of self‐energy , 1996, Journal of computational chemistry.

[32]  Jianyou Shi,et al.  Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. , 2017, MedChemComm.

[33]  J. Gertsch,et al.  Optimization of a Benzoylpiperidine Class Identifies a Highly Potent and Selective Reversible Monoacylglycerol Lipase (MAGL) Inhibitor. , 2019, Journal of medicinal chemistry.

[34]  Tiziano Tuccinardi,et al.  Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies , 2016, Journal of enzyme inhibition and medicinal chemistry.

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

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

[37]  Ryan G. Coleman,et al.  ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..

[38]  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.

[39]  Yuguang Mu,et al.  OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction , 2019, ACS omega.

[40]  D. Case,et al.  Generalized born models of macromolecular solvation effects. , 2000, Annual review of physical chemistry.

[41]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[42]  P A Kollman,et al.  Free energy calculations on dimer stability of the HIV protease using molecular dynamics and a continuum solvent model. , 2000, Journal of molecular biology.

[43]  C. Gotti,et al.  Design of novel alpha7-subtype-preferring nicotinic acetylcholine receptor agonists: application of docking and MM-PBSA computational approaches, synthetic and pharmacological studies. , 2009, Bioorganic & medicinal chemistry letters.

[44]  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.

[45]  Garland R. Marshall,et al.  An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design , 2016, J. Chem. Inf. Model..

[46]  Chris de Graaf,et al.  Function-specific virtual screening for GPCR ligands using a combined scoring method , 2016, Scientific Reports.

[47]  Tiziano Tuccinardi,et al.  Application of a FLAP-Consensus Docking Mixed Strategy for the Identification of New Fatty Acid Amide Hydrolase Inhibitors , 2015, J. Chem. Inf. Model..

[48]  Huiyong Sun,et al.  Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. , 2019, Physical chemistry chemical physics : PCCP.

[49]  Dariusz Plewczynski,et al.  VoteDock: Consensus docking method for prediction of protein–ligand interactions , 2011, J. Comput. Chem..

[50]  T. Tuccinardi,et al.  Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands , 2015, Journal of enzyme inhibition and medicinal chemistry.

[51]  P. Kollman,et al.  Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate−DNA Helices , 1998 .

[52]  G. Degliesposti,et al.  Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening , 2009, Chemical biology & drug design.

[53]  Peter Kollman,et al.  Molecular recognition by β-cyclodextrin derivatives: molecular dynamics, free-energy perturbation and molecular mechanics/ Poisson–Boltzmann surface area goals and problems , 2002 .

[54]  Kirk E. Hevener,et al.  Fragment-Based Drug Discovery Using a Multidomain, Parallel MD-MM/PBSA Screening Protocol , 2013, J. Chem. Inf. Model..

[55]  Pedro J. Ballester,et al.  Performance of machine-learning scoring functions in structure-based virtual screening , 2017, Scientific Reports.

[56]  M. Natália D. S. Cordeiro,et al.  CompScore: boosting structure-based virtual screening performance by incorporating docking scoring functions components into consensus scoring , 2019 .