Covalent docking in CDOCKER

Kumalo HM, Bhakat S, Soliman ME (2015) Theory and applications of covalent docking in drug discovery: merits and pitfalls. Molecules 20(2):1984–2000

PubMed  PubMed Central  Article  CAS  Google Scholar 

Baillie TA (2016) Targeted covalent inhibitors for drug design. Angew. Chem. Int. Ed. 55(43):13408–13421

CAS  Article  Google Scholar 

Scarpino A, Ferenczy GG, Keserű GM (2020) Covalent docking in drug discovery: Scope and limitations. Curr. Pharm, Des

Wan X, Yang T, Cuesta A, Pang X, Balius TE, Irwin JJ, Shoichet BK, Taunton J (2020) Discovery of lysine-targeted eif4e inhibitors through covalent docking. JACS 142(11):4960–4964

CAS  Article  Google Scholar 

Chowdhury SR, Kennedy S, Zhu K, Mishra R, Chuong P, Nguyen A-U, Kathman SG, Statsyuk AV (2019) Discovery of covalent enzyme inhibitors using virtual docking of covalent fragments. Bioorg. Med. Chem. 29(1):36–39

CAS  Article  Google Scholar 

Shraga A, Olshvang E, Davidzohn N, Khoshkenar P, Germain N, Shurrush K, Carvalho S, Avram L, Albeck S, Unger T et al (2019) Covalent docking identifies a potent and selective mkk7 inhibitor. Cell Chem. Biol. 26(1):98–108

CAS  PubMed  Article  Google Scholar 

Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug 3(11):935–949

CAS  Article  Google Scholar 

Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J. Mol. Regonit. 24(2):149–164

CAS  Article  Google Scholar 

Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des. 16(3):151–166

CAS  PubMed  Article  Google Scholar 

London N, Miller RM, Krishnan S, Uchida K, Irwin JJ, Eidam O, Gibold L, Cimermančič P, Bonnet R, Shoichet BK et al (2014) Covalent docking of large libraries for the discovery of chemical probes. Nat. Chem. Biol. 10(12):1066–1072

CAS  PubMed  PubMed Central  Article  Google Scholar 

Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267(3):727–748

CAS  PubMed  Article  Google Scholar 

Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245(1):43–53

CAS  PubMed  Article  Google Scholar 

Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using gold. Proteins 52(4):609–623

CAS  PubMed  Article  Google Scholar 

Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and autodocktools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30(16):2785–2791

CAS  PubMed  PubMed Central  Article  Google Scholar 

Bianco G, Forli S, Goodsell DS, Olson AJ (2016) Covalent docking using autodock: Two-point attractor and flexible side chain methods. Protein Sci. 25(1):295–301

CAS  PubMed  Article  Google Scholar 

Zhu K, Borrelli KW, Greenwood JR, Day T, Abel R, Farid RS, Harder E (2014) Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J. Chem. Inf. Model. 54(7):1932–1940

CAS  PubMed  Article  Google Scholar 

Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O (2014) Structure-based virtual screening approach for discovery of covalently bound ligands. J. Chem. Inf. Model. 54(7):1941–1950

CAS  PubMed  Article  Google Scholar 

Corbeil CR, Englebienne P, Moitessier N (2007) Docking ligands into flexible and solvated macromolecules. 1. development and validation of fitted 1.0. J. Chem. Inf. Model. 47(2):435–449

CAS  PubMed  Article  Google Scholar 

Abagyan R, Totrov M, Kuznetsov D (1994) Icm–a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15(5):488–506

CAS  Article  Google Scholar 

Vilar S, Cozza G, Moro S (2008) Medicinal chemistry and the molecular operating environment (moe): application of qsar and molecular docking to drug discovery. Curr. Topics Med. Chem. 8(18):1555–1572

CAS  Article  Google Scholar 

Scarpino A, Ferenczy GG, Keserű GM (2018) Comparative evaluation of covalent docking tools. J. Chem. Inf. Model. 58(7):1441–1458

CAS  PubMed  Article  Google Scholar 

Wu G, Robertson DH, Brooks Charles LIII, Vieth M (2003) Detailed analysis of grid-based molecular docking: A case study of cdocker - a charmm-based md docking algorithm. J. Comput. Chem. 24(13):1549–1562

CAS  PubMed  Article  Google Scholar 

Irwin JJ, Shoichet BK (2005) Zinc- a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45(1):177–182

CAS  PubMed  PubMed Central  Article  Google Scholar 

Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) Zinc: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52(7):1757–1768

CAS  PubMed  PubMed Central  Article  Google Scholar 

Ouyang X, Zhou S, Su CTT, Ge Z, Li R, Kwoh CK (2013) Covalentdock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J. Comput. Chem. 34(4):326–336

CAS  PubMed  Article  Google Scholar 

Sterling T, Irwin JJ (2015) Zinc 15-ligand discovery for everyone. J. Chem. Inf. Model. 55(11):2324–2337

CAS  PubMed  PubMed Central  Article  Google Scholar 

Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Bindingdb: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35(1):198–201

Article  Google Scholar 

Inc CCG (2016) Molecular operating environment (MOE). Chemical Computing Group Inc. 1010 Sherbooke St. West, Suite# 910, Montreal \(\ldots\)

Landrum G (2013) RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Academic Press, USA

Google Scholar 

Riniker S, Landrum GA (2015) Better informed distance geometry: using what we know to improve conformation generation. J. Chem. Inf. Model. 55(12):2562–2574

CAS  PubMed  Article  Google Scholar 

Wang S, Witek J, Landrum GA, Riniker S (2020) Improving conformer generation for small rings and macrocycles based on distance geometry and experimental torsional-angle preferences. J. Chem. Inf. Model. 60(4):2044–2058

CAS  PubMed  Article  Google Scholar 

Vanommeslaeghe K, MacKerell AD Jr (2012) Automation of the charmm general force field (cgenff) i: bond perception and atom typing. J. Chem. Inf. Model. 52(12):3144–3154

CAS  PubMed  PubMed Central  Article  Google Scholar 

Vanommeslaeghe K, Raman EP, MacKerell AD Jr (2012) Automation of the charmm general force field (cgenff) ii: assignment of bonded parameters and partial atomic charges. J. Chem. Inf. Model. 52(12):3155–3168

CAS  PubMed  PubMed Central  Article  Google Scholar 

Feig M, Karanicolas J, Brooks Charles LIII (2004) Mmtsb tool set: enhanced sampling and multiscale modeling methods for applications in structural biology. J. Mol. Graph 22(5):377–395

CAS  Article  Google Scholar 

Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I et al (2010) Charmm general force field: A force field for drug-like molecules compatible with the charmm all-atom additive biological force fields. J. Comput. Chem. 31(4):671–690

CAS  PubMed  PubMed Central  Google Scholar 

Brooks BR, Brooks Charles LIII, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S et al (2009) Charmm: the biomolecular simulation program. J. Comput. Chem. 30(10):1545–1614

CAS  PubMed  PubMed Central  Article  Google Scholar 

Ding X, Wu Y, Wang Y, Vilseck JZ, Brooks Charles LIII (2020) Accelerated cdocker with gpus, parallel simulated annealing, and fast fourier transforms. J. Chem. Theory Comput. 16(6):3910–3919

CAS  PubMed  PubMed Central  Article  Google Scholar 

Gagnon JK, Law SM, Brooks Charles LIII (2016) Flexible CDOCKER: Development and application of a pseudo-explicit structure-based docking method within charmm. J. Comput. Chem. 37(8):753–762

CAS  PubMed  Article  Google Scholar 

Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 48(9):2839–2846

Article  Google Scholar 

Luo YL (2021) Mechanism-based and computational-driven covalent drug design. J. Chem. Inf. Model. 61(11):5307–5311

CAS  PubMed  Article  Google Scholar 

Li A, Sun H, Du L, Wu X, Cao J, You Q, Li Y (2014) Discovery of novel covalent proteasome inhibitors through a combination of pharmacophore screening, covalent docking, and molecular dynamics simulations. J. Mol. Model. 20(11):1–13

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