Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Covalent Docking in Drug Discovery: Scope and Limitations

Author(s): Andrea Scarpino, György G. Ferenczy and György M. Keserű*

Volume 26 , Issue 44 , 2020

Page: [5684 - 5699] Pages: 16

DOI: 10.2174/1381612824999201105164942

Price: $65

Abstract

Drug discovery efforts for new covalent inhibitors have drastically increased in the last few years. The binding mechanism of covalent compounds entails the formation of a chemical bond between their electrophilic warhead group and the protein of interest. The use of moderately reactive warheads targeting nonconserved nucleophilic residues can improve the affinity and selectivity profiles of covalent binders as compared to their non-covalent analogs. Recent advances have also enabled their use as chemical probes to disclose novel and also less tractable targets. Increasing interest in covalent drug discovery prompted the development of new computational tools, including covalent docking methods, that are available to predict the binding mode and affinity of covalent ligands. These tools integrate conventional non-covalent docking and scoring schemes by modeling the newly formed covalent bond and the interactions occurring at the reaction site. In this review, we provide a thorough analysis of state-of-the-art covalent docking programs by highlighting their main features and current limitations. Focusing on the implemented algorithms, we show the differences in handling the formation of the new covalent bond and their relative impact on the prediction. This analysis provides a comprehensive overview of the current technology and suggests future improvements in computer-aided covalent drug design. Finally, discussing successful retrospective and prospective covalent docking-based virtual screening applications, we intend to identify best practices for the drug discovery community.

Keywords: Covalent docking, drug design, targeted covalent inhibitors, binding mode prediction, virtual screening, warhead, reactivity.

[1]
Warren GL, Andrews CW, Capelli AM, et al. A critical assessment of docking programs and scoring functions. J Med Chem 2006; 49(20): 5912-31.
[http://dx.doi.org/10.1021/jm050362n] [PMID: 17004707]
[2]
Kellenberger E, Rodrigo J, Muller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 2004; 57(2): 225-42.
[PMID: 15340911]
[3]
Wang Z, Sun H, Yao X, et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 2016; 18(18): 12964-75.
[http://dx.doi.org/10.1039/C6CP01555G] [PMID: 27108770]
[4]
Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL III. Assessing scoring functions for protein-ligand interactions. J Med Chem 2004; 47(12): 3032-47.
[http://dx.doi.org/10.1021/jm030489h] [PMID: 15163185]
[5]
Kontoyianni M, McClellan LM, Sokol GS. Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 2004; 47(3): 558-65.
[http://dx.doi.org/10.1021/jm0302997] [PMID: 14736237]
[6]
Cross JB, Thompson DC, Rai BK, et al. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 2009; 49(6): 1455-74.
[http://dx.doi.org/10.1021/ci900056c] [PMID: 19476350]
[7]
Kiss R, Kiss B, Könczöl A, et al. Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening. J Med Chem 2008; 51(11): 3145-53.
[http://dx.doi.org/10.1021/jm7014777] [PMID: 18459760]
[8]
Katritch V, Jaakola V-P, Lane JR, et al. Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J Med Chem 2010; 53(4): 1799-809.
[http://dx.doi.org/10.1021/jm901647p] [PMID: 20095623]
[9]
de Graaf C, Kooistra AJ, Vischer HF, et al. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J Med Chem 2011; 54(23): 8195-206.
[http://dx.doi.org/10.1021/jm2011589] [PMID: 22007643]
[10]
Lane JR, Chubukov P, Liu W, et al. Structure-based ligand discovery targeting orthosteric and allosteric pockets of dopamine receptors. Mol Pharmacol 2013; 84(6): 794-807.
[http://dx.doi.org/10.1124/mol.113.088054] [PMID: 24021214]
[11]
Irwin JJ, Shoichet BK. Docking screens for novel ligands conferring new biology. J Med Chem 2016; 59(9): 4103-20.
[http://dx.doi.org/10.1021/acs.jmedchem.5b02008] [PMID: 26913380]
[12]
Manglik A, Lin H, Aryal DK, et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 2016; 537(7619): 185-90.
[http://dx.doi.org/10.1038/nature19112] [PMID: 27533032]
[13]
Kooistra AJ, Vischer HF, McNaught-Flores D, Leurs R, de Esch IJP, de Graaf C. Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci Rep 2016; 6: 28288.
[http://dx.doi.org/10.1038/srep28288] [PMID: 27339552]
[14]
Wang S, Wacker D, Levit A, et al. D4 dopamine receptor high-resolution structures enable the discovery of selective agonists. Science 2017; 358(6361): 381-6.
[http://dx.doi.org/10.1126/science.aan5468] [PMID: 29051383]
[15]
Lyu J, Wang S, Balius TE, et al. Ultra-large library docking for discovering new chemotypes. Nature 2019; 566(7743): 224-9.
[http://dx.doi.org/10.1038/s41586-019-0917-9] [PMID: 30728502]
[16]
Wang L, Wu Y, Deng Y, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015; 137(7): 2695-703.
[http://dx.doi.org/10.1021/ja512751q] [PMID: 25625324]
[17]
De Cesco S, Kurian J, Dufresne C, Mittermaier AK, Moitessier N. Covalent inhibitors design and discovery. Eur J Med Chem 2017; 138: 96-114.
[http://dx.doi.org/10.1016/j.ejmech.2017.06.019] [PMID: 28651155]
[18]
Scarpino A, Ferenczy GG, Keserű GM. Comparative evaluation of covalent docking tools. J Chem Inf Model 2018; 58(7): 1441-58.
[http://dx.doi.org/10.1021/acs.jcim.8b00228] [PMID: 29890081]
[19]
Gehringer M, Laufer SA. Emerging and re-emerging warheads for targeted covalent inhibitors: applications in medicinal chemistry and chemical biology. J Med Chem 2019; 62(12): 5673-724.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01153] [PMID: 30565923]
[20]
Singh J, Petter RC, Baillie TA, Whitty A. The resurgence of covalent drugs. Nat Rev Drug Discov 2011; 10(4): 307-17.
[http://dx.doi.org/10.1038/nrd3410] [PMID: 21455239]
[21]
Schwartz PA, Kuzmic P, Solowiej J, et al. Covalent EGFR inhibitor analysis reveals importance of reversible interactions to potency and mechanisms of drug resistance. Proc Natl Acad Sci USA 2014; 111(1): 173-8.
[http://dx.doi.org/10.1073/pnas.1313733111] [PMID: 24347635]
[22]
Kuntz ID, Chen K, Sharp KA, Kollman PA. The maximal affinity of ligands. Proc Natl Acad Sci USA 1999; 96(18): 9997-10002.
[http://dx.doi.org/10.1073/pnas.96.18.9997] [PMID: 10468550]
[23]
Smith AJT, Zhang X, Leach AG, Houk KN. Beyond picomolar affinities: quantitative aspects of noncovalent and covalent binding of drugs to proteins. J Med Chem 2009; 52(2): 225-33.
[http://dx.doi.org/10.1021/jm800498e] [PMID: 19053779]
[24]
Claxton AJ, Cramer J, Pierce C. A systematic review of the associations between dose regimens and medication compliance. Clin Ther 2001; 23(8): 1296-310.
[http://dx.doi.org/10.1016/S0149-2918(01)80109-0] [PMID: 11558866]
[25]
Serafimova IM, Pufall MA, Krishnan S, et al. Reversible targeting of noncatalytic cysteines with chemically tuned electrophiles. Nat Chem Biol 2012; 8(5): 471-6.
[http://dx.doi.org/10.1038/nchembio.925] [PMID: 22466421]
[26]
Krishnan S, Miller RM, Tian B, Mullins RD, Jacobson MP, Taunton J. Design of reversible, cysteine-targeted Michael acceptors guided by kinetic and computational analysis. J Am Chem Soc 2014; 136(36): 12624-30.
[http://dx.doi.org/10.1021/ja505194w] [PMID: 25153195]
[27]
Mah R, Thomas JR, Shafer CM. Drug discovery considerations in the development of covalent inhibitors. Bioorg Med Chem Lett 2014; 24(1): 33-9.
[http://dx.doi.org/10.1016/j.bmcl.2013.10.003] [PMID: 24314671]
[28]
Bradshaw JM, McFarland JM, Paavilainen VO, et al. Prolonged and tunable residence time using reversible covalent kinase inhibitors. Nat Chem Biol 2015; 11(7): 525-31.
[http://dx.doi.org/10.1038/nchembio.1817] [PMID: 26006010]
[29]
Lagoutte R, Patouret R, Winssinger N. Covalent inhibitors: an opportunity for rational target selectivity. Curr Opin Chem Biol 2017; 39: 54-63.
[http://dx.doi.org/10.1016/j.cbpa.2017.05.008] [PMID: 28609675]
[30]
Jöst C, Nitsche C, Scholz T, Roux L, Klein CD. Promiscuity and selectivity in covalent enzyme inhibition: a systematic study of electrophilic fragments. J Med Chem 2014; 57(18): 7590-9.
[http://dx.doi.org/10.1021/jm5006918] [PMID: 25148591]
[31]
Backus KM, Correia BE, Lum KM, et al. Proteome-wide covalent ligand discovery in native biological systems. Nature 2016; 534(7608): 570-4.
[PMID: 27309814]
[32]
Parker CG, Galmozzi A, Wang Y, et al. Ligand and target discovery by fragment-based screening in human cells. Cell 2017; 168(3): 527-541.e29.
[PMID: 28111073]
[33]
Ábrányi-Balogh P, Petri L, Imre T, et al. A road map for prioritizing warheads for cysteine targeting covalent inhibitors. Eur J Med Chem 2018; 160: 94-107.
[PMID: 30321804]
[34]
Johansson H, Isabella Tsai YC, Fantom K, et al. Fragment-based covalent ligand screening enables rapid discovery of inhibitors for the RBR E3 Ubiquitin Ligase HOIP. J Am Chem Soc 2019; 141(6): 2703-12.
[http://dx.doi.org/10.1021/jacs.8b13193] [PMID: 30657686]
[35]
Resnick E, Bradley A, Gan J, et al. Rapid covalent-probe discovery by electrophile-fragment screening. J Am Chem Soc 2019; 141(22): 8951-68.
[http://dx.doi.org/10.1021/jacs.9b02822] [PMID: 31060360]
[36]
Halgren TA. Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 2009; 49(2): 377-89.
[http://dx.doi.org/10.1021/ci800324m] [PMID: 19434839]
[37]
Cox AD, Fesik SW, Kimmelman AC, Luo J, Der CJ. Drugging the undruggable RAS: Mission possible? Nat Rev Drug Discov 2014; 13(11): 828-51.
[http://dx.doi.org/10.1038/nrd4389] [PMID: 25323927]
[38]
Uetrecht JP. New concepts in immunology relevant to idiosyncratic drug reactions: the “danger hypothesis” and innate immune system. Chem Res Toxicol 1999; 12(5): 387-95.
[http://dx.doi.org/10.1021/tx980249i] [PMID: 10328748]
[39]
Nakayama S, Atsumi R, Takakusa H, et al. A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding. Drug Metab Dispos 2009; 37(9): 1970-7.
[http://dx.doi.org/10.1124/dmd.109.027797] [PMID: 19487250]
[40]
Baillie TA. Targeted covalent inhibitors for drug design. Angew Chem Int Ed Engl 2016; 55(43): 13408-21.
[http://dx.doi.org/10.1002/anie.201601091] [PMID: 27539547]
[41]
Lindvall MK. Molecular modeling in cysteine protease inhibitor design. Curr Pharm Des 2002; 8(18): 1673-81.
[http://dx.doi.org/10.2174/1381612023394142] [PMID: 12132998]
[42]
Potashman MH, Duggan ME. Covalent modifiers: an orthogonal approach to drug design. J Med Chem 2009; 52(5): 1231-46.
[http://dx.doi.org/10.1021/jm8008597] [PMID: 19203292]
[43]
Kalgutkar AS, Dalvie DK. Drug discovery for a new generation of covalent drugs. Expert Opin Drug Discov 2012; 7(7): 561-81.
[http://dx.doi.org/10.1517/17460441.2012.688744] [PMID: 22607458]
[44]
Wilson AJ, Kerns JK, Callahan JF, Moody CJ. Keap calm, and carry on covalently. J Med Chem 2013; 56(19): 7463-76.
[http://dx.doi.org/10.1021/jm400224q] [PMID: 23837912]
[45]
Kumalo HM, Bhakat S, Soliman MES. Theory and applications of covalent docking in drug discovery: Merits and pitfalls 2015; 20: 1984-2000.
[46]
Bauer RA. Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies. Drug Discov Today 2015; 20(9): 1061-73.
[http://dx.doi.org/10.1016/j.drudis.2015.05.005] [PMID: 26002380]
[47]
Adeniyi AA, Muthusamy R, Soliman MES. New drug design with covalent modifiers. Expert Opin Drug Discov 2016; 11(1): 79-90.
[http://dx.doi.org/10.1517/17460441.2016.1115478] [PMID: 26757171]
[48]
Awoonor-Williams E, Walsh AG, Rowley CN. Modeling covalent-modifier drugs. Biochim Biophys Acta Proteins Proteomics 2017; 1865(11 Pt B): 1664-75.
[http://dx.doi.org/10.1016/j.bbapap.2017.05.009] [PMID: 28528876]
[49]
Sotriffer C. Docking of covalent ligands: challenges and approaches. Mol Inform 2018; 37(9-10)e1800062
[http://dx.doi.org/10.1002/minf.201800062] [PMID: 29927068]
[50]
Ghosh AK, Samanta I, Mondal A, Liu WR. Covalent inhibition in drug discovery. ChemMedChem 2019; 14(9): 889-906.
[http://dx.doi.org/10.1002/cmdc.201900107] [PMID: 30816012]
[51]
Li B, Nachon F, Froment M-T, et al. Binding and hydrolysis of soman by human serum albumin. Chem Res Toxicol 2008; 21(2): 421-31.
[http://dx.doi.org/10.1021/tx700339m] [PMID: 18163544]
[52]
Zhang S, Shi Y, Jin H, Liu Z, Zhang L, Zhang L. Covalent complexes of proteasome model with peptide aldehyde inhibitors MG132 and MG101: docking and molecular dynamics study. J Mol Model 2009; 15(12): 1481-90.
[http://dx.doi.org/10.1007/s00894-009-0515-0] [PMID: 19440739]
[53]
Mariaule G, De Cesco S, Airaghi F, et al. 3-Oxo-hexahydro-1H-isoindole-4-carboxylic acid as a drug chiral bicyclic scaffold: Structure-based design and preparation of conformationally constrained covalent and noncovalent prolyl oligopeptidase inhibitors. J Med Chem 2016; 59(9): 4221-34.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01296] [PMID: 26619267]
[54]
Fakhouri L, El-Elimat T, Hurst DP, et al. Isolation, semisynthesis, covalent docking and transforming growth factor beta-activated kinase 1 (TAK1)-inhibitory activities of (5Z)-7-oxozeaenol analogues. Bioorg Med Chem 2015; 23(21): 6993-9.
[http://dx.doi.org/10.1016/j.bmc.2015.09.037] [PMID: 26481152]
[55]
Pejchal V, Štěpánková Š, Pejchalová M, et al. Synthesis, structural characterization, docking, lipophilicity and cytotoxicity of 1-[(1R)-1-(6-fluoro-1,3-benzothiazol-2-yl)ethyl]-3-alkyl carbamates, novel acetylcholinesterase and butyrylcholinesterase pseudo-irreversible inhibitors. Bioorg Med Chem 2016; 24(7): 1560-72.
[http://dx.doi.org/10.1016/j.bmc.2016.02.033] [PMID: 26947959]
[56]
Christopeit T, Albert A, Leiros HS. Discovery of a novel covalent non-β-lactam inhibitor of the metallo-β-lactamase NDM-1. Bioorg Med Chem 2016; 24(13): 2947-53.
[http://dx.doi.org/10.1016/j.bmc.2016.04.064] [PMID: 27184103]
[57]
Kellici TF, Mavromoustakos T, Jendrossek D, Papageorgiou AC. Crystal structure analysis, covalent docking, and molecular dynamics calculations reveal a conformational switch in PhaZ7 PHB depolymerase. Proteins 2017; 85(7): 1351-61.
[http://dx.doi.org/10.1002/prot.25296] [PMID: 28370478]
[58]
Brogi S, Fiorillo A, Chemi G, et al. Structural characterization of Giardia duodenalis thioredoxin reductase (gTrxR) and computational analysis of its interaction with NBDHEX. Eur J Med Chem 2017; 135: 479-90.
[http://dx.doi.org/10.1016/j.ejmech.2017.04.057] [PMID: 28477573]
[59]
Schirmeister T, Schmitz J, Jung S, Schmenger T, Krauth-Siegel RL, Gütschow M. Evaluation of dipeptide nitriles as inhibitors of rhodesain, a major cysteine protease of Trypanosoma brucei. Bioorg Med Chem Lett 2017; 27(1): 45-50.
[PMID: 27890381]
[60]
El-labbad EM, Ismail MAH, Abou Ei Ella DA, et al. Discovery of novel peptidomimetics as irreversible chikv nsp2 protease inhibitors using quantum mechanical-based ligand descriptors. Chem Biol Drug Des 2015; 86(6): 1518-27.
[PMID: 26212366]
[61]
He L, Pei H, Lan T, Tang M, Zhang C, Chen L. Design and synthesis of a highly selective jak3 inhibitor for the treatment of rheumatoid arthritis. Arch Pharm (Weinheim) 2017; 350(11)1700194
[PMID: 28944566]
[62]
Wodtke R, Hauser C, Ruiz-Gómez G, et al. Nε-Acryloyllysine Piperazides as irreversible inhibitors of Transglutaminase 2: synthesis, structure-activity relationships, and pharmacokinetic profiling. J Med Chem 2018; 61(10): 4528-60.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00286] [PMID: 29664627]
[63]
Ma Y, Xu B, Fang Y, et al. Synthesis and SAR study of novel peptide aldehydes as inhibitors of 20s proteasome. Molecules 2011; 16: 7551-64.
[64]
Liu R, Shi D, Zhang J, et al. Xanthatin promotes apoptosis via inhibiting thioredoxin reductase and eliciting oxidative stress. Mol Pharm 2018; 15(8): 3285-96.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00338] [PMID: 29939757]
[65]
Verdino A, Zollo F, De Rosa M, Soriente A, Hernández-Martínez MÁ, Marabotti A. Computational analysis of the interactions of a novel cephalosporin derivative with β-lactamases. BMC Struct Biol 2018; 18(1): 13.
[http://dx.doi.org/10.1186/s12900-018-0092-5] [PMID: 30286754]
[66]
Ramirez YA, Adler TB, Altmann E, et al. Structural basis of substrate recognition and covalent inhibition of Cdu1 from Chlamydia trachomatis. ChemMedChem 2018; 13(19): 2014-23.
[http://dx.doi.org/10.1002/cmdc.201800364] [PMID: 30028574]
[67]
Dong X-W, Zhang J-K, Xu L, et al. Covalent docking modelling-based discovery of tripeptidyl epoxyketone proteasome inhibitors composed of aliphatic-heterocycles. Eur J Med Chem 2019; 164: 602-14.
[http://dx.doi.org/10.1016/j.ejmech.2018.12.064] [PMID: 30639896]
[68]
Carlesso A, Chintha C, Gorman AM, Samali A, Eriksson LA. Merits and pitfalls of conventional and covalent docking in identifying new hydroxyl aryl aldehyde like compounds as human IRE1 inhibitors. Sci Rep 2019; 9(1): 3407.
[http://dx.doi.org/10.1038/s41598-019-39939-z] [PMID: 30833722]
[69]
Lamani M, Malamas MS, Farah SI, et al. Piperidine and piperazine inhibitors of fatty acid amide hydrolase targeting excitotoxic pathology. Bioorg Med Chem 2019; 27(23)115096
[http://dx.doi.org/10.1016/j.bmc.2019.115096] [PMID: 31629610]
[70]
Jaudzems K, Kurbatska V, Je̅kabsons A, Bobrovs R, Rudevica Z, Leonchiks A. Targeting bacterial sortase a with covalent inhibitors: 27 new starting points for structure-based hit-to-lead optimization. ACS Infect Dis 2019; 6(2): 186-94.
[71]
Roy KK, Tota S, Tripathi T, Chander S, Nath C, Saxena AK. Lead optimization studies towards the discovery of novel carbamates as potent AChE inhibitors for the potential treatment of Alzheimer’s disease. Bioorg Med Chem 2012; 20(21): 6313-20.
[http://dx.doi.org/10.1016/j.bmc.2012.09.005] [PMID: 23026084]
[72]
Tyzack JD, Williamson MJ, Torella R, Glen RC. Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis. J Chem Inf Model 2013; 53(6): 1294-305.
[http://dx.doi.org/10.1021/ci400058s] [PMID: 23701380]
[73]
Rondelet G, Fleury L, Faux C, et al. Inhibition studies of DNA methyltransferases by maleimide derivatives of RG108 as non-nucleoside inhibitors. Future Med Chem 2017; 9(13): 1465-81.
[http://dx.doi.org/10.4155/fmc-2017-0074] [PMID: 28795598]
[74]
Tyagi S, Pleiss J. Biochemical profiling in silico--predicting substrate specificities of large enzyme families. J Biotechnol 2006; 124(1): 108-16.
[http://dx.doi.org/10.1016/j.jbiotec.2006.01.027] [PMID: 16519956]
[75]
Juhl PB, Trodler P, Tyagi S, Pleiss J. Modelling substrate specificity and enantioselectivity for lipases and esterases by substrate-imprinted docking. BMC Struct Biol 2009; 9: 39.
[http://dx.doi.org/10.1186/1472-6807-9-39] [PMID: 19493341]
[76]
Chen H, Wu J-P, Yang L-R, Xu G. Improving Pseudomonas alcaligenes lipase’s diastereopreference in hydrolysis of diastereomeric mixture of menthyl propionate by site-directed mutagenesis. Biotechnol Bioprocess Eng; BBE 2014; 19: 592-604.
[http://dx.doi.org/10.1007/s12257-014-0066-9]
[77]
Häußler D, Mangold M, Furtmann N, et al. Phosphono bisbenzguanidines as irreversible dipeptidomimetic inhibitors and activity-based probes of matriptase-2. Chemistry 2016; 22(25): 8525-35.
[http://dx.doi.org/10.1002/chem.201600206] [PMID: 27214780]
[78]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[79]
Kramer B, Rarey M, Lengauer T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 1999; 37(2): 228-41.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228:AID-PROT8>3.0.CO;2-8] [PMID: 10584068]
[80]
Fradera X, Kaur J, Mestres J. Unsupervised guided docking of covalently bound ligands. J Comput Aided Mol Des 2004; 18(10): 635-50.
[http://dx.doi.org/10.1007/s10822-004-5291-4] [PMID: 15849994]
[81]
Katritch V, Byrd CM, Tseitin V, et al. Discovery of small molecule inhibitors of ubiquitin-like poxvirus proteinase I7L using homology modeling and covalent docking approaches. J Comput Aided Mol Des 2007; 21(10-11): 549-58.
[http://dx.doi.org/10.1007/s10822-007-9138-7] [PMID: 17960327]
[82]
Ouyang X, Zhou S, Su CTT, Ge Z, Li R, Kwoh CK. CovalentDock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J Comput Chem 2013; 34(4): 326-36.
[http://dx.doi.org/10.1002/jcc.23136] [PMID: 23034731]
[83]
London N, Miller RM, Krishnan S, et al. Covalent docking of large libraries for the discovery of chemical probes. Nat Chem Biol 2014; 10(12): 1066-72.
[http://dx.doi.org/10.1038/nchembio.1666 ] [PMID: 25344815]
[84]
Scholz C, Knorr S, Hamacher K, Schmidt B. DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J Chem Inf Model 2015; 55(2): 398-406.
[http://dx.doi.org/10.1021/ci500681r] [PMID: 25541749]
[85]
Bianco G, Forli S, Goodsell DS, Olson AJ. Covalent docking using autodock: Two-point attractor and flexible side chain methods. Protein Sci 2016; 25(1): 295-301.
[http://dx.doi.org/10.1002/pro.2733] [PMID: 26103917]
[86]
Zhu K, Borrelli KW, Greenwood JR, et al. Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 2014; 54(7): 1932-40.
[http://dx.doi.org/10.1021/ci500118s] [PMID: 24916536]
[87]
Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O. Structure-based virtual screening approach for discovery of covalently bound ligands. J Chem Inf Model 2014; 54(7): 1941-50.
[http://dx.doi.org/10.1021/ci500175r] [PMID: 24932913]
[88]
Ai Y, Yu L, Tan X, Chai X, Liu S. Discovery of covalent ligands via noncovalent docking by dissecting covalent docking based on a “steric-clashes alleviating receptor (SCAR)” strategy. J Chem Inf Model 2016; 56(8): 1563-75.
[http://dx.doi.org/10.1021/acs.jcim.6b00334] [PMID: 27411028]
[89]
Scarpino A, Petri L, Knez D, et al. WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors. J Comput Aided Mol Des 2020; 14(10): 1011-21.
[90]
Powers JP, Piper DE, Li Y, et al. SAR and mode of action of novel non-nucleoside inhibitors of hepatitis C NS5b RNA polymerase. J Med Chem 2006; 49(3): 1034-46.
[http://dx.doi.org/10.1021/jm050859x] [PMID: 16451069]
[91]
Moitessier N, Pottel J, Therrien E, et al. Medicinal chemistry projects requiring imaginative structure-based drug design methods. Acc Chem Res 2016; 49(9): 1646-57.
[http://dx.doi.org/10.1021/acs.accounts.6b00185] [PMID: 27529781]
[92]
Flanagan ME, Abramite JA, Anderson DP, et al. Chemical and computational methods for the characterization of covalent reactive groups for the prospective design of irreversible inhibitors. J Med Chem 2014; 57(23): 10072-9.
[http://dx.doi.org/10.1021/jm501412a] [PMID: 25375838]
[93]
Lonsdale R, Burgess J, Colclough N, et al. Expanding the armory: predicting and tuning covalent warhead reactivity. J Chem Inf Model 2017; 57(12): 3124-37.
[http://dx.doi.org/10.1021/acs.jcim.7b00553] [PMID: 29131621]
[94]
Fanfrlík J, Brahmkshatriya PS, Řezáč J, et al. Quantum mechanics-based scoring rationalizes the irreversible inactivation of parasitic Schistosoma mansoni cysteine peptidase by vinyl sulfone inhibitors. J Phys Chem B 2013; 117(48): 14973-82.
[http://dx.doi.org/10.1021/jp409604n] [PMID: 24195769]
[95]
Chaskar P, Zoete V, Röhrig UF. On-the-Fly QM/MM Docking with attracting cavities. J Chem Inf Model 2017; 57(1): 73-84.
[http://dx.doi.org/10.1021/acs.jcim.6b00406] [PMID: 27983849]
[96]
Palazzesi F, Grundl MA, Pautsch A, Weber A, Tautermann CS. A fast Ab initio predictor tool for covalent reactivity estimation of acrylamides. J Chem Inf Model 2019; 59(8): 3565-71.
[http://dx.doi.org/10.1021/acs.jcim.9b00316] [PMID: 31246457]
[97]
Oballa RM, Truchon JF, Bayly CI, et al. A generally applicable method for assessing the electrophilicity and reactivity of diverse nitrile-containing compounds. Bioorg Med Chem Lett 2007; 17(4): 998-1002.
[http://dx.doi.org/10.1016/j.bmcl.2006.11.044] [PMID: 17157022]
[98]
Cee VJ, Volak LP, Chen Y, et al. Systematic study of the Glutathione (GSH) reactivity of N-Arylacrylamides: 1. effects of aryl substitution. J Med Chem 2015; 58(23): 9171-8.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01018] [PMID: 26580091]
[99]
Mihalovits LM, Ferenczy GG, Keserű GM. Catalytic mechanism and covalent inhibition of UDP-N-Acetylglucosamine Enolpyruvyl Transferase (MurA): Implications to the design of novel antibacterials. J Chem Inf Model 2019; 59(12): 5161-73.
[http://dx.doi.org/10.1021/acs.jcim.9b00691] [PMID: 31715096]
[100]
Lawandi J, Toumieux S, Seyer V, et al. Constrained peptidomimetics reveal detailed geometric requirements of covalent prolyl oligopeptidase inhibitors. J Med Chem 2009; 52(21): 6672-84.
[http://dx.doi.org/10.1021/jm901013a] [PMID: 19888757]
[101]
Molecular Operating Environment (MOE) 2013.08: Chemical Computing Group ULC; Montreal, QC, Canada 2013.
[102]
Jones G, Willett P, Glen RC. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 1995; 245(1): 43-53.
[http://dx.doi.org/10.1016/S0022-2836(95)80037-9] [PMID: 7823319]
[103]
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins 2003; 52(4): 609-23.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
[104]
Clark M, Cramer RD III, Van Opdenbosch N. Validation of the general purpose tripos 5.2 force field. J Comput Chem 1989; 10: 982-1012.
[http://dx.doi.org/10.1002/jcc.540100804]
[105]
Wen C, Yan X, Gu Q, et al. Systematic studies on the protocol and criteria for selecting a covalent docking tool 2019; 24
[http://dx.doi.org/10.3390/molecules24112183]
[106]
Schröder J, Klinger A, Oellien F, Marhöfer RJ, Duszenko M, Selzer PM. Docking-based virtual screening of covalently binding ligands: an orthogonal lead discovery approach. J Med Chem 2013; 56(4): 1478-90.
[http://dx.doi.org/10.1021/jm3013932] [PMID: 23350811]
[107]
Dietrich Ihlenfeldt W, Takahashi Y, Abe H, Sasaki S. Computation and management of chemical properties in CACTVS: An extensible networked approach toward modularity and compatibility. J Chem Inf Comput Sci 2002; 34: 109-16.
[http://dx.doi.org/10.1021/ci00017a013]
[108]
Zhang S, Tan J, Lai Z, et al. Effective virtual screening strategy toward covalent ligands: identification of novel NEDD8-activating enzyme inhibitors. J Chem Inf Model 2014; 54(6): 1785-97.
[http://dx.doi.org/10.1021/ci5002058] [PMID: 24857708]
[109]
Brownell JE, Sintchak MD, Gavin JM, et al. Substrate-assisted inhibition of ubiquitin-like protein-activating enzymes: the NEDD8 E1 inhibitor MLN4924 forms a NEDD8-AMP mimetic in situ. Mol Cell 2010; 37(1): 102-11.
[http://dx.doi.org/10.1016/j.molcel.2009.12.024] [PMID: 20129059]
[110]
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 2012; 52(7): 1757-68.
[http://dx.doi.org/10.1021/ci3001277] [PMID: 22587354]
[111]
Li A, Sun H, Du L, et al. Discovery of novel covalent proteasome inhibitors through a combination of pharmacophore screening, covalent docking, and molecular dynamics simulations. J Mol Model 2014; 20(11): 2515.
[http://dx.doi.org/10.1007/s00894-014-2515-y] [PMID: 25394401]
[112]
Sgrignani J, De Luca F, Torosyan H, et al. Structure-based approach for identification of novel phenylboronic acids as serine-β-lactamase inhibitors. J Comput Aided Mol Des 2016; 30(10): 851-61.
[http://dx.doi.org/10.1007/s10822-016-9962-8] [PMID: 27632226]
[113]
Sgrignani J, Novati B, Colombo G, Grazioso G. Covalent docking of selected boron-based serine beta-lactamase inhibitors. J Comput Aided Mol Des 2015; 29(5): 441-50.
[http://dx.doi.org/10.1007/s10822-015-9834-7] [PMID: 25676821]
[114]
Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261(3): 470-89.
[http://dx.doi.org/10.1006/jmbi.1996.0477] [PMID: 8780787]
[115]
Rarey M, Kramer B, Lengauer T. Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J Comput Aided Mol Des 1997; 11(4): 369-84.
[http://dx.doi.org/10.1023/A:1007913026166] [PMID: 9334903]
[116]
BioSolveIT. SeeSAR 2019. Available from: https://www.biosolveit.de/SeeSAR/
[117]
Fradera X, Knegtel RMA, Mestres J. Similarity-driven flexible ligand docking. Proteins 2000; 40(4): 623-36.
[http://dx.doi.org/10.1002/1097-0134(20000901)40:4<623:AID-PROT70>3.0.CO;2-I] [PMID: 10899786]
[118]
Ewing TJA, Kuntz ID. Critical evaluation of search algorithms used in automated molecular docking. Comput Appl Biosci 1997; 18: 1175-89.
[119]
Ewing TJA, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15(5): 411-28.
[http://dx.doi.org/10.1023/A:1011115820450] [PMID: 11394736]
[120]
Mestres J, Rohrer DC, Maggiora GM. MIMIC: A molecular-field matching program. Exploiting applicability of molecular similarity approaches. J Comput Chem 1997; 18: 934-54.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199705)18:7<934:AID-JCC6>3.0.CO;2-S]
[121]
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 1994; 15: 488-506.
[http://dx.doi.org/10.1002/jcc.540150503]
[122]
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30(16): 2785-91.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[123]
Morse PM. Diatomic molecules according to the wave mechanics. II. Vibrational Levels. Phys Rev 1929; 34: 57-64.
[http://dx.doi.org/10.1103/PhysRev.34.57]
[124]
Case DA, Darden TA, Cheatham TEI, et al. AMBER 12. University of California, San Francisco, CA, USA 2012.
[125]
Frisch MJ, Trucks GW, Schlegel HB, et al. Gaussian 09. Wallingford, CT, USA: Gaussian, Inc. 2009.
[126]
Blake L, Soliman MES. Identification of irreversible protein splicing inhibitors as potential anti-TB drugs: Insight from hybrid non-covalent/covalent docking virtual screening and molecular dynamics simulations. Med Chem Res 2014; 23: 2312-23.
[http://dx.doi.org/10.1007/s00044-013-0822-y]
[127]
Sbongile M, Soliman MES. In silico identification of irreversible cathepsin B inhibitors as anti- cancer agents: virtual screening, covalent docking analysis and molecular dynamics simulations. Comb Chem High Throughput Screen 2015; 18(4): 399-410.
[http://dx.doi.org/10.2174/1386207318666150305154621] [PMID: 25747438]
[128]
Labute P. The generalized Born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 2008; 29(10): 1693-8.
[http://dx.doi.org/10.1002/jcc.20933] [PMID: 18307169]
[129]
Mysinger MM, Shoichet BK. Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 2010; 50(9): 1561-73.
[http://dx.doi.org/10.1021/ci100214a] [PMID: 20735049]
[130]
Openeye Scientific Software. Santa Fe, NM 2019.
[131]
Gasteiger J, Rudolph C, Sadowski J. Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Methodol 1990; 3: 537-47.
[http://dx.doi.org/10.1016/0898-5529(90)90156-3]
[132]
Li J, Zhu T, Hawkins GD, et al. Extension of the platform of applicability of the SM5.42R universal solvation model. Theor Chem Acc 1999; 103: 9-63.
[http://dx.doi.org/10.1007/s002140050513]
[133]
Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M. Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 2007; 21(12): 681-91.
[http://dx.doi.org/10.1007/s10822-007-9133-z] [PMID: 17899391]
[134]
Greenwood JR, Calkins D, Sullivan AP, Shelley JC. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aided Mol Des 2010; 24(6-7): 591-604.
[http://dx.doi.org/10.1007/s10822-010-9349-1] [PMID: 20354892]
[135]
Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J Chem Inf Model 2010; 50(4): 572-84.
[http://dx.doi.org/10.1021/ci100031x] [PMID: 20235588]
[136]
London N, Farelli JD, Brown SD, et al. Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases. Biochemistry 2015; 54(2): 528-37.
[http://dx.doi.org/10.1021/bi501140k] [PMID: 25513739]
[137]
Nnadi CI, Jenkins ML, Gentile DR, et al. Novel K-Ras G12C switch-II covalent binders destabilize RAS and accelerate nucleotide exchange. J Chem Inf Model 2018; 58(2): 464-71.
[http://dx.doi.org/10.1021/acs.jcim.7b00399] [PMID: 29320178]
[138]
Shraga A, Olshvang E, Davidzohn N, et al. Covalent docking identifies a potent and selective MKK7 inhibitor. Cell Chem Biol 2019; 26(1): 98-108.e5.
[http://dx.doi.org/10.1016/j.chembiol.2018.10.011] [PMID: 30449673]
[139]
Correy GJ, Zaidman D, Harmelin A, et al. Overcoming insecticide resistance through computational inhibitor design. Proc Natl Acad Sci USA 2019; 116(42): 21012-21.
[http://dx.doi.org/10.1073/pnas.1909130116] [PMID: 31575743]
[140]
CTFile Formats Rxnfiles. Symyx Technologies, Inc. 2007.
[141]
Neudert G, Klebe G. DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 2011; 51(10): 2731-45.
[http://dx.doi.org/10.1021/ci200274q ] [PMID: 21863864]
[142]
Omar SI, Lepre MG, Morbiducci U, Deriu MA, Tuszynski JA. Virtual screening using covalent docking to find activators for G245S mutant p53. PLoS One 2018; 13(9)e0200769
[http://dx.doi.org/10.1371/journal.pone.0200769] [PMID: 30192754]
[143]
Lambert JMR, Gorzov P, Veprintsev DB, et al. PRIMA-1 reactivates mutant p53 by covalent binding to the core domain. Cancer Cell 2009; 15(5): 376-88.
[http://dx.doi.org/10.1016/j.ccr.2009.03.003] [PMID: 19411067]
[144]
Wassman CD, Baronio R, Demir Ö, et al. Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53. Nat Commun 2013; 4: 1407.
[http://dx.doi.org/10.1038/ncomms2361] [PMID: 23360998]
[145]
Bensinger D, Stubba D, Cremer A, et al. Virtual screening identifies irreversible FMS-like tyrosine kinase 3 inhibitors with activity toward resistance-conferring mutations. J Med Chem 2019; 62(5): 2428-46.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01714] [PMID: 30742435]
[146]
Forli S, Botta M. Lennard-Jones potential and dummy atom settings to overcome the AUTODOCK limitation in treating flexible ring systems. J Chem Inf Model 2007; 47(4): 1481-92.
[http://dx.doi.org/10.1021/ci700036j] [PMID: 17585754]
[147]
Corbeil CR, Englebienne P, Moitessier N. Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 2007; 47(2): 435-49.
[http://dx.doi.org/10.1021/ci6002637] [PMID: 17305329]
[148]
Corbeil CR, Moitessier N. Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 2009; 49(4): 997-1009.
[http://dx.doi.org/10.1021/ci8004176] [PMID: 19391631]
[149]
De Cesco S, Deslandes S, Therrien E, et al. Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells. J Med Chem 2012; 55(14): 6306-15.
[http://dx.doi.org/10.1021/jm3002839] [PMID: 22765237]
[150]
Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004; 47(7): 1739-49.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[151]
Halgren TA, Murphy RB, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004; 47(7): 1750-9.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
[152]
Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 2006; 49(21): 6177-96.
[http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]
[153]
Watts KS, Dalal P, Murphy RB, Sherman W, Friesner RA, Shelley JC. ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 2010; 50(4): 534-46.
[http://dx.doi.org/10.1021/ci100015j] [PMID: 20373803]
[154]
Jacobson MP, Pincus DL, Rapp CS, et al. A hierarchical approach to all-atom protein loop prediction. Proteins 2004; 55(2): 351-67.
[http://dx.doi.org/10.1002/prot.10613] [PMID: 15048827]
[155]
Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA. The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins 2011; 79(10): 2794-812.
[http://dx.doi.org/10.1002/prot.23106] [PMID: 21905107]
[156]
Chowdhury SR, Kennedy S, Zhu K, et al. Discovery of covalent enzyme inhibitors using virtual docking of covalent fragments. Bioorg Med Chem Lett 2019; 29(1): 36-9.
[http://dx.doi.org/10.1016/j.bmcl.2018.11.019] [PMID: 30455147]
[157]
Scarpino A, Bajusz D, Proj M, et al. Discovery of immunoproteasome inhibitors using large-scale covalent virtual screening 2019; 24: 2590.
[http://dx.doi.org/10.3390/molecules24142590]
[158]
Liao C, Wang Y, Tan X, Sun L, Liu S. Discovery of novel inhibitors of human S-adenosylmethionine decarboxylase based on in silico high-throughput screening and a non-radioactive enzymatic assay. Sci Rep 2015; 5: 10754.
[http://dx.doi.org/10.1038/srep10754] [PMID: 26030749]
[159]
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2): 455-61.
[PMID: 19499576]
[160]
Leaver-Fay A, Tyka M, Lewis SM, et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 2011; 487: 545-74.
[http://dx.doi.org/10.1016/B978-0-12-381270-4.00019-6] [PMID: 21187238 ]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy