A practical guide to large-scale docking

Brian J. Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase M. Webb, Reed M. Stein, Elissa A. Fink, Trent E. Balius, Jens Carlsson, John J. Irwin, Brian K. Shoichet

Published: 2021-09-23 DOI: 10.1038/s41596-021-00597-z

large-scale docking

drug discovery

melatonin receptor

binding energy prediction

docking controls

AI 解读

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

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References

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of comp
Introduction
Materials
Procedure
Troubleshooting
Anticipated results
References
ACKNOWLEDGMENTS

Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).
Leaver-Fay, A. et al. Rosetta3. in Methods in Enzymology 545–574 (2011); http://doi.org/10.1016/B978-0-12-381270-4.00019-6
Mayr, L. M. & Bojanic, D. Novel trends in high-throughput screening. Curr. Opin. Pharmacol. 9, 580–588 (2009).
Keserü, G. M. & Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nat. Rev. Drug Discov. 8, 203–212 (2009).
Keiser, M. J., Irwin, J. J. & Shoichet, B. K. The chemical basis of pharmacology. Biochemistry 49, 10267–10276 (2010).
Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 3–50 (1996).
Brenner, S. & Lerner, R. A. Encoded combinatorial chemistry. Proc. Natl Acad. Sci. USA 89, 5381–5383 (1992).
Fitzgerald, P. R. & Paegel, B. M. DNA-encoded chemistry: drug discovery from a few good reactions. Chem. Rev. http://doi.org/10.1021/acs.chemrev.0c00789 (2020).
Grebner, C. et al. Virtual screening in the Cloud: how big is big enough? J. Chem. Inf. Model 60, 24 (2020).
Davies, E. K., Glick, M., Harrison, K. N. & Richards, W. G. Pattern recognition and massively distributed computing. J. Comput. Chem. 23, 1544–1550 (2002).
Sterling, T. & Irwin, J. J. ZINC 15—ligand discovery for everyone. J. Chem. Inf. Model. 55, 2324–2337 (2015).
Patel, H. et al. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci. Data 7, 384 (2020).
Grygorenko, O. O. et al. Generating multibillion chemical space of readily accessible screening compounds. iScience 23, 101681 (2020).
Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019).
Stein, R. M. et al. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 579, 609–614 (2020).
Gorgulla, C. et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature 580, 663 (2020).
Meng, E. C., Shoichet, B. K. & Kuntz, I. D. Automated docking with grid‐based energy evaluation. J. Comput. Chem. 13, 505–524 (1992).
Sharp, K. A., Friedman, R. A., Misra, V., Hecht, J. & Honig, B. Salt effects on polyelectrolyte-ligand binding: comparison of Poisson–Boltzmann, and limiting law/counterion binding models. Biopolymers 36, 245–262 (1995).
Mysinger, M. M. & Shoichet, B. K. Rapid context-dependent ligand desolvation in molecular docking. J. Chem. Inf. Model. 50, 1561–1573 (2010).
Adeshina, Y. O., Deeds, E. J. & Karanicolas, J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc. Natl Acad. Sci. USA 117, 18477–18488 (2020).
Irwin, J. J. & Shoichet, B. K. Docking screens for novel ligands conferring new biology. J. Med. Chem. 59, 4103–4120 (2016).
Mobley, D. L. & Dill, K. A. Binding of small-molecule ligands to proteins: “what you see” is not always “what you get. Structure 17, 489–498 (2009).
Bissantz, C., Folkers, G. & Rognan, D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J. Med. Chem. 43, 4759–4767 (2000).
Tirado-Rives, J. & Jorgensen, W. L. Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J. Med. Chem. 49, 5880–5884 (2006).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2009).
Kramer, B., Rarey, M. & Lengauer, T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 37, 228–241 (1999).
Halgren, T. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 1750–1759 (2004).
Morris, G. M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998).
Abagyan, R., Totrov, M. & Kuznetsov, D. ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994).
Goodsell, D. S. & Olson, A. J. Automated docking of substrates to proteins by simulated annealing. Proteins 8, 195–202 (1990).
Mcgann, M. FRED pose prediction and virtual screening accuracy. J. Chem. Inf. Model 51, 578–596 (2011).
Jones, G., Willett, P., Glen, R. C., Leach, A. R. & Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748 (1997).
Corbeil, C. R., Williams, C. I. & Labute, P. Variability in docking success rates due to dataset preparation. J. Comput. Aided Mol. Des. 26, 775–786 (2012).
McGovern, S. L. & Shoichet, B. K. Information decay in molecular docking screens against Holo, Apo, and modeled conformations of enzymes. J. Med. Chem. 46, 2895–2907 (2003).
Rueda, M., Bottegoni, G. & Abagyan, R. Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50, 186–193 (2010).
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R. & Ferrin, T. E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 161, 269–288 (1982).
Halgren, T. A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 49, 377–389 (2009).
Ngan, C. H. et al. FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res. 40, W271–W275 (2012).
Wang, S. et al. D4 dopamine receptor high-resolution structures enable the discovery of selective agonists. Science 358, 381–386 (2017).
Katritch, V. et al. Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J. Med. Chem. 53, 1799 (2010).
Kolb, P. et al. Structure-based discovery of beta2-adrenergic receptor ligands. Proc. Natl Acad. Sci. USA 106, 6843–6848 (2009).
De Graaf, C. et al. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H 1 receptor. J. Med. Chem. 54, 8195–8206 (2011).
Mysinger, M. M. et al. Structure-based ligand discovery for the protein–protein interface of chemokine receptor CXCR4. Proc. Natl Acad. Sci. USA 109, 5517–5522 (2012).
Powers, R. A., Morandi, F. & Shoichet, B. K. Structure-based discovery of a novel, noncovalent inhibitor of AmpC β-lactamase. Structure 10, 1013–1023 (2002).
Zarzycka, B. et al. Discovery of small molecule CD40–TRAF6 inhibitors. J. Chem. Inf. Model. 55, 294–307 (2015).
Huang, N. & Shoichet, B. K. Exploiting ordered waters in molecular docking. J. Med. Chem. 51, 4862–4865 (2008).
Balius, T. E. et al. Testing inhomogeneous solvation theory in structure-based ligand discovery. Proc. Natl Acad. Sci. USA 114, E6839–E6846 (2017).
Weichenberger, C. X. & Sippl, M. J. NQ-Flipper: recognition and correction of erroneous asparagine and glutamine side-chain rotamers in protein structures. Nucleic Acids Res. 35, W403–W406 (2007).
Word, J. M., Lovell, S. C., Richardson, J. S. & Richardson, D. C. Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol. 285, 1735–1747 (1999).
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221–234 (2013).
Bas, D. C., Rogers, D. M. & Jensen, J. H. Very fast prediction and rationalization of pKa values for protein–ligand complexes. Proteins 73, 765–783 (2008).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Bandyopadhyay, D., Bhatnagar, A., Jain, S. & Pratyaksh, P. Selective stabilization of aspartic acid protonation state within a given protein conformation occurs via specific “molecular association”. J. Phys. Chem. B 124, 5350–5361 (2020).
Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 54, 5.6.1–5.6.37 (2016).
Bender, B. J. et al. Protocols for molecular modeling with Rosetta3 and RosettaScripts. Biochemistry 55, 4748–4763 (2016).
Yang, J. et al. Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade. Proteins 84, 233–246 (2016).
Jaiteh, M., Rodríguez-Espigares, I., Selent, J. & Carlsson, J. Performance of virtual screening against GPCR homology models: impact of template selection and treatment of binding site plasticity. PLoS Comput. Biol. 16, e1007680 (2020).
Cavasotto, C. N. et al. Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J. Med. Chem. 51, 581–588 (2008).
Phatak, S. S., Gatica, E. A. & Cavasotto, C. N. Ligand-steered modeling and docking: a benchmarking study in class A G-protein-coupled receptors. J. Chem. Inf. Model. 50, 2119–2128 (2010).
Kaufmann, K. W. & Meiler, J. Using RosettaLigand for small molecule docking into comparative models. PLoS One 7, e50769 (2012).
Bordogna, A., Pandini, A. & Bonati, L. Predicting the accuracy of protein–ligand docking on homology models. J. Comput. Chem. 32, 81–98 (2011).
Katritch, V., Rueda, M., Lam, P. C.-H., Yeager, M. & Abagyan, R. GPCR 3D homology models for ligand screening: lessons learned from blind predictions of adenosine A2a receptor complex. Proteins 78, 197–211 (2010).
Schafferhans, A. & Klebe, G. Docking ligands onto binding site representations derived from proteins built by homology modelling. J. Mol. Biol. 307, 407–427 (2001).
Lansu, K. et al. In silico design of novel probes for the atypical opioid receptor MRGPRX2. Nat. Chem. Biol. 13, 529–536 (2017).
Huang, X.-P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015).
Trauelsen, M. et al. Receptor structure-based discovery of non-metabolite agonists for the succinate receptor GPR91. Mol. Metab. 6, 1585–1596 (2017).
Kolb, P. et al. Limits of ligand selectivity from docking to models: in silico screening for A1 adenosine receptor antagonists. PLoS One 7, e49910 (2012).
Daga, P. R., Polgar, W. E. & Zaveri, N. T. Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification. J. Chem. Inf. Model. 54, 2732–2743 (2014).
Diaz, C. et al. A strategy combining differential low-throughput screening and virtual screening (DLS-VS) accelerating the discovery of new modulators for the Orphan GPR34 receptor. Mol. Inf. 32, 213–229 (2013).
Langmead, C. J. et al. Identification of novel adenosine A 2A receptor antagonists by virtual screening. J. Med. Chem. 55, 1904–1909 (2012).
Tikhonova, I. G. et al. Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening. J. Med. Chem. 51, 625–633 (2008).
Martí-Solano, M., Schmidt, D., Kolb, P. & Selent, J. Drugging specific conformational states of GPCRs: challenges and opportunities for computational chemistry. Drug Discov. Today 21, 625–631 (2016).
Carlsson, J. et al. Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat. Chem. Biol. 7, 769–778 (2011).
Männel, B. et al. Structure-guided screening for functionally selective D2 dopamine receptor ligands from a virtual chemical library. ACS Chem. Biol. 12, 2652–2661 (2017).
Khare, P. et al. Identification of novel S-adenosyl-l-homocysteine hydrolase inhibitors through homology-model-based virtual screening, synthesis, and biological evaluation. J. Chem. Inf. Model. 52, 777–791 (2012).
Li, S. et al. Identification of inhibitors against p90 ribosomal S6 kinase 2 (RSK2) through structure-based virtual screening with the inhibitor-constrained refined homology model. J. Chem. Inf. Model. 51, 2939–2947 (2011).
Eberini, I. et al. In silico identification of new ligands for GPR17: a promising therapeutic target for neurodegenerative diseases. J. Comput. Aided Mol. Des. 25, 743–752 (2011).
Frimurer, T. M. et al. Model-based discovery of synthetic agonists for the Zn2+-sensing G-protein-coupled receptor 39 (GPR39) reveals novel biological functions. J. Med. Chem. 60, 886–898 (2017).
Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of Useful Decoys, Enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6 (2012).
Stein, R. M. et al. Property-unmatched decoys in docking benchmarks. J. Chem. Inf. Model. 61, 699–714 (2020).
Coleman, R. G., Carchia, M., Sterling, T., Irwin, J. J. & Shoichet, B. K. Ligand pose and orientational sampling in molecular docking. PLoS One 8, e75992 (2013).
Huang, N., Shoichet, B. K. & Irwin, J. J. Benchmarking sets for molecular docking. J. Med. Chem. 49, 6789–6801 (2006).
Jain, A. N. & Nicholls, A. Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22, 133–139 (2008).
Allen, W. J. & Rizzo, R. C. Implementation of the Hungarian algorithm to account for ligand symmetry and similarity in structure-based design. J. Chem. Inf. Model. 54, 518–529 (2014).
Schuller, M. et al. Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking. Sci. Adv. 7, eabf8711 (2021).
Fischer, A., Smieško, M., Sellner, M. & Lill, M. A. Decision making in structure-based drug discovery: visual inspection of docking results. J. Med. Chem. 64, 2489–2500 (2021).
Kirchmair, J. et al. Predicting drug metabolism: experiment and/or computation? Nat. Rev. Drug Discov. 14, 387–404 (2015).
Kirchmair, J. et al. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model. 52, 617–648 (2012).
Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).
Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).
Jadhav, A. et al. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. J. Med. Chem. 53, 37–51 (2010).
Capuzzi, S. J., Muratov, E. N. & Tropsha, A. Phantom PAINS: problems with the utility of alerts for Pan-Assay Interference Compound S. J. Chem. Inf. Model. 57, 417–427 (2017).
Baell, J. B. & Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719–2740 (2010).
McGovern, S. L., Caselli, E., Grigorieff, N. & Shoichet, B. K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).
Feng, B. Y. et al. A high-throughput screen for aggregation-based inhibition in a large compound library. J. Med. Chem. 50, 2385–2390 (2007).
Ganesh, A. N. et al. Colloidal drug aggregate stability in high serum conditions and pharmacokinetic consequence. ACS Chem. Biol. 14, 751–757 (2019).
Coan, K. E. D. & Shoichet, B. K. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J. Am. Chem. Soc. 130, 9606–9612 (2008).
Coan, K. E. D., Maltby, D. A., Burlingame, A. L. & Shoichet, B. K. Promiscuous aggregate-based inhibitors promote enzyme unfolding. J. Med. Chem. 52, 2067–2075 (2009).
Wolan, D. W., Zorn, J. A., Gray, D. C. & Wells, J. A. Small-molecule activators of a proenzyme. Science 326, 853–858 (2009).
Zorn, J. A., Wolan, D. W., Agard, N. J. & Wells, J. A. Fibrils colocalize caspase-3 with procaspase-3 to foster maturation. J. Biol. Chem. 287, 33781–33795 (2012).
Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).
Teotico, D. G. et al. Docking for fragment inhibitors of AmpC -lactamase. Proc. Natl Acad. Sci. USA 106, 7455–7460 (2009).
Chen, Y. & Shoichet, B. K. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat. Chem. Biol. 5, 358–364 (2009).
Kolb, P. & Irwin, J. J. Docking screens: right for the right reasons? Curr. Top. Med. Chem. 9, 755–770 (2009).
Wu, Y., Lou, L. & Xie, Z.-R. A pilot study of all-computational drug design protocol–from structure prediction to interaction analysis. Front. Chem. 8, 81 (2020).
Greenidge, P. A., Kramer, C., Mozziconacci, J. C. & Sherman, W. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J. Chem. Inf. Model. 54, 2697–2717 (2014).
Mahmoud, A. H., Masters, M. R., Yang, Y. & Lill, M. A. Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning. Commun. Chem. 3, 19 (2020).
Liu, X. et al. An allosteric modulator binds to a conformational hub in the β2 adrenergic receptor. Nat. Chem. Biol. 16, 749–755 (2020).
Wacker, D. et al. Conserved binding mode of human β 2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography. J. Am. Chem. Soc. 132, 11443–11445 (2010).
Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).
Ewing, T. J. A. & Kuntz, I. D. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem. 18, 1175–1189 (1997).
Gallagher, K. & Sharp, K. Electrostatic contributions to heat capacity changes of DNA-ligand binding. Biophys. J. 75, 769–776 (1998).
Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W. & Shoichet, B. K. A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 322, 339–355 (2002).
Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221–234 (2013).
Armstrong, J. F. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY. Nucleic Acids Res. 48, D1006–D1021 (2019).
Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).
Irwin, J. J., Raushel, F. M. & Shoichet, B. K. Virtual screening against metalloenzymes for inhibitors and substrates. Biochemistry 44, 12316–12328 (2005).
Verdonk, M. L. et al. Virtual screening using protein−ligand docking: avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 44, 793–806 (2004).
Alon, A. et al. Crystal structures of the σ 2 receptor template large-library docking for selective chemotypes active in vivo. Preprint at bioRxiv http://doi.org/10.1101/2021.04.29.441652 (2021).
Babaoglu, K. et al. Comprehensive mechanistic analysis of hits from high-throughput and docking screens against β-lactamase. J. Med. Chem. 51, 2502–2511 (2008).
Lorber, D. M. & Shoichet, B. K. Flexible ligand docking using conformational ensembles. Protein Sci. 7, 938–950 (1998).
Alhossary, A., Handoko, S. D., Mu, Y. & Kwoh, C.-K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31, 2214–2216 (2015).
Quiroga, R. & Villarreal, M. A. Vinardo: a scoring function based on Autodock Vina improves scoring, docking, and virtual screening. PLoS One 11, e0155183 (2016).
Bottegoni, G., Kufareva, I., Totrov, M. & Abagyan, R. Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J. Med. Chem. 52, 397–406 (2009).
Cho, Y., Ioerger, T. R. & Sacchettini, J. C. Discovery of novel nitrobenzothiazole inhibitors for Mycobacterium tuberculosis ATP phosphoribosyl transferase (HisG) through virtual screening. J. Med. Chem. 51, 5984–5992 (2008).
Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W. & Taylor, R. D. Improved protein–ligand docking using GOLD. Proteins 52, 609–623 (2003).
Li, C. et al. Identification of diverse dipeptidyl peptidase IV inhibitors via structure-based virtual screening. J. Mol. Model. 18, 4033–4042 (2012).
Friesner, R. A. et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 49, 6177–6196 (2006).
Rai, B. K. et al. Comprehensive assessment of torsional strain in crystal structures of small molecules and protein–ligand complexes using ab initio calculations. J. Chem. Inf. Model. 59, 4195–4208 (2019).
Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The Cambridge structural database. Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 72, 171–179 (2016).
Gu, S., Smith, M. S., Yang, Y., Irwin, J. J. & Shoichet, B. K. Ligand strain energy in large library docking. Preprint at bioRxiv http://doi.org/10.1101/2021.04.06.438722 (2021).
Xing, L., Klug-Mcleod, J., Rai, B. & Lunney, E. A. Kinase hinge binding scaffolds and their hydrogen bond patterns. Bioorg. Med. Chem. 23, 6520–6527 (2015).
Peng, Y. et al. 5-HT2C receptor structures reveal the structural basis of GPCR polypharmacology. Cell 172, 719–730.e14 (2018).
Bissantz, C., Kuhn, B. & Stahl, M. A medicinal chemist’s guide to molecular interactions. J. Med. Chem. 53, 5061–5084 (2010).
Alexander, N., Woetzel, N. & Meiler, J. Bcl::Cluster: a method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System. in 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) 2011, 13–18 (IEEE, 2011).
Bender, A. & Glen, R. C. A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J. Chem. Inf. Model. 45, 1369–1375 (2005).
Simeonov, A. et al. Fluorescence spectroscopic profiling of compound libraries. J. Med. Chem. 51, 2363–2371 (2008).
Lea, W. A. & Simeonov, A. Fluorescence polarization assays in small molecule screening. Expert Opin. Drug Disco. 6, 17–32 (2011).
Thorne, N., Auld, D. S. & Inglese, J. Apparent activity in high-throughput screening: origins of compound-dependent assay interference. Curr. Opin. Chem. Biol. 14, 315–324 (2010).
Walters, W. P. & Namchuk, M. Designing screens: how to make your hits a hit. Nat. Rev. Drug Discov. 2, 259–266 (2003).
Thorne, N. et al. Firefly luciferase in chemical biology: a compendium of inhibitors, mechanistic evaluation of chemotypes, and suggested use as a reporter. Chem. Biol. 19, 1060–1072 (2012).
Sassano, M. F., Doak, A. K., Roth, B. L. & Shoichet, B. K. Colloidal aggregation causes inhibition of G protein-coupled receptors. J. Med. Chem. 56, 2406–2414 (2013).
Owen, S. C., Doak, A. K., Wassam, P., Shoichet, M. S. & Shoichet, B. K. Colloidal aggregation affects the efficacy of anticancer drugs in cell culture. ACS Chem. Biol. 7, 1429–1435 (2012).
McLaughlin, C. K. et al. Stable colloidal drug aggregates catch and release active enzymes. ACS Chem. Biol. 11, 992–1000 (2016).
McGovern, S. L. & Shoichet, B. K. Kinase inhibitors: not just for kinases anymore. J. Med. Chem. 46, 1478–1483 (2003).

A practical guide to large-scale docking

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