Selection of protein conformations for structure-based polypharmacology studies.

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.

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

[2]  J. Bajorath,et al.  Polypharmacology: challenges and opportunities in drug discovery. , 2014, Journal of medicinal chemistry.

[3]  Luca Pinzi,et al.  Computational polypharmacology comes of age , 2015, Front. Pharmacol..

[4]  S. Frantz Drug discovery: Playing dirty , 2005, Nature.

[5]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[6]  Jacob D. Durrant,et al.  POVME: an algorithm for measuring binding-pocket volumes. , 2011, Journal of molecular graphics & modelling.

[7]  Xian Liu,et al.  In Silico target fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion , 2014, Journal of Cheminformatics.

[8]  A. Caflisch,et al.  Discovery of Tyrosine Kinase Inhibitors by Docking into an Inactive Kinase Conformation Generated by Molecular Dynamics , 2012, ChemMedChem.

[9]  Giulio Rastelli,et al.  BEAR, a Novel Virtual Screening Methodology for Drug Discovery , 2011, Journal of biomolecular screening.

[10]  Lazaros Mavridis,et al.  Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods , 2010, J. Chem. Inf. Model..

[11]  Tingjun Hou,et al.  Feasibility of Using Molecular Docking-Based Virtual Screening for Searching Dual Target Kinase Inhibitors , 2013, J. Chem. Inf. Model..

[12]  A. Cavalli,et al.  Dynamic Docking: A Paradigm Shift in Computational Drug Discovery , 2017, Molecules.

[13]  Robert P. Sheridan,et al.  Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results , 2008, J. Comput. Aided Mol. Des..

[14]  Woody Sherman,et al.  Selecting an Optimal Number of Binding Site Waters To Improve Virtual Screening Enrichments Against the Adenosine A2A Receptor , 2014, J. Chem. Inf. Model..

[15]  Lirong Wang,et al.  ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research , 2017, J. Chem. Inf. Model..

[16]  Savvas N. Georgiades,et al.  Investigation of ‘Head-to-Tail’-Connected Oligoaryl N,O-Ligands as Recognition Motifs for Cancer-Relevant G-Quadruplexes , 2017, Molecules.

[17]  Richard A. Lewis,et al.  Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. , 2004, Journal of medicinal chemistry.

[18]  Giulio Rastelli,et al.  Advances and applications of binding affinity prediction methods in drug discovery. , 2012, Biotechnology advances.

[19]  Matteo Masetti,et al.  Protein Flexibility in Drug Discovery: From Theory to Computation , 2015, ChemMedChem.

[20]  Friedrich Rippmann,et al.  TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins , 2013, J. Chem. Inf. Model..

[21]  E. Kellenberger,et al.  Similarity between Flavonoid Biosynthetic Enzymes and Flavonoid Protein Targets Captured by Three-Dimensional Computing Approach , 2015, Planta Medica.

[22]  Andreas Bender,et al.  Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases , 2017, Journal of Cheminformatics.

[23]  Jordi Mestres,et al.  Identification of Similar Binding Sites to Detect Distant Polypharmacology , 2013, Molecular informatics.

[24]  Weilin Zhang,et al.  Computational Multitarget Drug Design , 2017, J. Chem. Inf. Model..

[25]  Jürgen Bajorath,et al.  Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited , 2017, Future science OA.

[26]  Weirong Yuan,et al.  Rapid identification of dual p53-MDM2/MDMX interaction inhibitors through virtual screening and hit-based substructure search , 2017 .

[27]  Chengfei Yan,et al.  Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015 , 2017, Journal of Computer-Aided Molecular Design.

[28]  Neera Borkakoti,et al.  Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates , 2018, Structure.

[29]  Christian Kramer,et al.  Improving Docking Results via Reranking of Ensembles of Ligand Poses in Multiple X-ray Protein Conformations with MM-GBSA , 2014, J. Chem. Inf. Model..

[30]  Ronald J. Quinn,et al.  Structural Insights into the Molecular Basis of the Ligand Promiscuity , 2012, J. Chem. Inf. Model..

[31]  W. Patrick Walters,et al.  Prediction of Protein Pairs Sharing Common Active Ligands Using Protein Sequence, Structure, and Ligand Similarity , 2016, J. Chem. Inf. Model..

[32]  A Srinivas Reddy,et al.  Virtual screening in drug discovery -- a computational perspective. , 2007, Current protein & peptide science.

[33]  Jürgen Bajorath,et al.  New methodologies for ligand-based virtual screening. , 2005, Current pharmaceutical design.

[34]  Jürgen Bajorath,et al.  X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design , 2018, ACS omega.

[35]  Giulio Rastelli,et al.  Structure‐Based and in silico Design of Hsp90 Inhibitors , 2009, ChemMedChem.

[36]  Ajay N. Jain Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation , 2009, J. Comput. Aided Mol. Des..

[37]  Philip E. Bourne,et al.  A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery , 2009, Bioinform..

[38]  Eytan Ruppin,et al.  Detecting similar binding pockets to enable systems polypharmacology , 2017, PLoS Comput. Biol..

[39]  R. Friesner,et al.  Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.

[40]  Marcel L. Verdonk,et al.  Protein-Ligand Docking against Non-Native Protein Conformers , 2008, J. Chem. Inf. Model..

[41]  Boon Chuan Low,et al.  In-Silico Approaches to Multi-target Drug Discovery , 2010, Pharmaceutical Research.

[42]  Amedeo Caflisch,et al.  Protein structure-based drug design: from docking to molecular dynamics. , 2018, Current opinion in structural biology.

[43]  J. A. Gavira,et al.  Current trends in protein crystallization. , 2016, Archives of biochemistry and biophysics.

[44]  Johannes H. Voigt,et al.  Cross-Docking of Inhibitors into CDK2 Structures. 2 , 2008, J. Chem. Inf. Model..

[45]  Giulio Rastelli,et al.  Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. , 2012, European journal of medicinal chemistry.

[46]  Peter M Fischer,et al.  Differential binding of inhibitors to active and inactive CDK2 provides insights for drug design. , 2006, Chemistry & biology.

[47]  Yanli Wang,et al.  Structural insights of a PI3K/mTOR dual inhibitor with the morpholino-triazine scaffold , 2016, Journal of Computer-Aided Molecular Design.

[48]  Federico D. Sacerdoti,et al.  Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[49]  J. Bajorath,et al.  Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space , 2017 .

[50]  Ting-Ting Wu,et al.  Design, synthesis and biological evaluation of N-phenylquinazolin-4-amine hybrids as dual inhibitors of VEGFR-2 and HDAC. , 2016, European journal of medicinal chemistry.

[51]  J. Skolnick,et al.  TM-align: a protein structure alignment algorithm based on the TM-score , 2005, Nucleic acids research.

[52]  Zhe Shi,et al.  Computer Aided Multi-target Drug Design, Multi-target Virtual Screening , 2010 .

[53]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[54]  R. Knegtel,et al.  A Role for Hydration in Interleukin‐2 Inducible T Cell Kinase (Itk) Selectivity , 2011, Molecular informatics.

[55]  Woody Sherman,et al.  Use of an Induced Fit Receptor Structure in Virtual Screening , 2006, Chemical biology & drug design.

[56]  Sarah L. Kinnings,et al.  Novel computational approaches to polypharmacology as a means to define responses to individual drugs. , 2012, Annual review of pharmacology and toxicology.

[57]  Fabrício F. Costa Big data in biomedicine. , 2014, Drug discovery today.

[58]  Christine Orengo,et al.  Choosing the Best Enzyme Complex Structure Made Easy. , 2018, Structure.

[59]  Woody Sherman,et al.  Generation of Receptor Structural Ensembles for Virtual Screening Using Binding Site Shape Analysis and Clustering , 2012, Chemical biology & drug design.

[60]  James Andrew McCammon,et al.  Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening , 2011, J. Chem. Inf. Model..

[61]  Jacob D. Durrant,et al.  Molecular dynamics simulations and drug discovery , 2011, BMC Biology.

[62]  Giulio Rastelli,et al.  Exploiting computationally derived out-of-the-box protein conformations for drug design. , 2016, Future medicinal chemistry.

[63]  Huaxi Xu,et al.  Structural Bioinformatics‐Based Identification of EGFR Inhibitor Gefitinib as a Putative Lead Compound for BACE , 2014, Chemical biology & drug design.

[64]  Philip E. Bourne,et al.  A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing , 2011, J. Chem. Inf. Model..

[65]  Gabriele Cruciani,et al.  BioGPS: Navigating biological space to predict polypharmacology, off‐targeting, and selectivity , 2015, Proteins.

[66]  Robert B. Russell,et al.  Combinations of Protein-Chemical Complex Structures Reveal New Targets for Established Drugs , 2011, PLoS Comput. Biol..

[67]  Nathan Brown,et al.  Best of Both Worlds: On the Complementarity of Ligand-Based and Structure-Based Virtual Screening , 2014, J. Chem. Inf. Model..

[68]  Shuxing Zhang,et al.  Polypharmacology: drug discovery for the future , 2013, Expert review of clinical pharmacology.

[69]  A. Cavalli,et al.  Role of Molecular Dynamics and Related Methods in Drug Discovery. , 2016, Journal of medicinal chemistry.

[70]  George Papadatos,et al.  The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..

[71]  Alan Talevi,et al.  Multi-target pharmacology: possibilities and limitations of the “skeleton key approach” from a medicinal chemist perspective , 2015, Front. Pharmacol..

[72]  R. Abagyan,et al.  Flexible ligand docking to multiple receptor conformations: a practical alternative. , 2008, Current opinion in structural biology.

[73]  Scott P. Brown,et al.  Effects of Conformational Dynamics on Predicted Protein Druggability , 2006, ChemMedChem.

[74]  Ruben Abagyan,et al.  Recipes for the Selection of Experimental Protein Conformations for Virtual Screening , 2010, J. Chem. Inf. Model..

[75]  Irina G. Tikhonova,et al.  Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles , 2013, J. Chem. Inf. Model..

[76]  C. Ehrt,et al.  Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. , 2016, Journal of medicinal chemistry.

[77]  W. Somers,et al.  Automated systems for protein crystallization. , 2004, Methods.

[78]  R. Abagyan,et al.  Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening , 2011, PloS one.

[79]  P. Hawkins,et al.  Comparison of shape-matching and docking as virtual screening tools. , 2007, Journal of medicinal chemistry.

[80]  Rui Duan,et al.  Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor , 2017, Journal of Computer-Aided Molecular Design.

[81]  Woody Sherman,et al.  Improving database enrichment through ensemble docking , 2008, J. Comput. Aided Mol. Des..

[82]  Ajay N. Jain Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.

[83]  M. Gilson,et al.  Calculation of protein-ligand binding affinities. , 2007, Annual review of biophysics and biomolecular structure.

[84]  J. Bajorath,et al.  State-of-the-art in ligand-based virtual screening. , 2011, Drug discovery today.

[85]  P. Svenningsson,et al.  Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson’s Disease , 2018, Journal of medicinal chemistry.

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