Advances in Docking

Author(s): Vladimir B. Sulimov*, Danil C. Kutov, Alexey V. Sulimov.

Journal Name: Current Medicinal Chemistry

Volume 26 , Issue 42 , 2019

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Abstract:

Background: Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for finding the most active compounds for the given target protein. This short review aims to give a concise description of distinctive features of docking programs focusing on computation methods and approximations influencing their accuracy.

Methods: This review is based on the peer-reviewed research literature including author’s own publications. The main features of several representative docking programs are briefly described focusing on their characteristics influencing docking accuracy: force fields, energy calculations, solvent models, algorithms of the best ligand pose search, global and local optimizations, ligand and target protein flexibility, and the simplifications made for the docking accelerating. Apart from other recent reviews focused mainly on the performance of different docking programs, in this work, an attempt is made to extract the most important functional characteristics defining the docking accuracy. Also a roadmap for increasing the docking accuracy is proposed. This is based on the new generation of docking programs which have been realized recently. These programs and respective new global optimization algorithms are described shortly.

Results: Several popular conventional docking programs are considered. Their search of the best ligand pose is based explicitly or implicitly on the global optimization problem. Several algorithms are used to solve this problem, and among them, the heuristic genetic algorithm is distinguished by its popularity and an elaborate design. All conventional docking programs for their acceleration use the preliminary calculated grids of protein-ligand interaction potentials or preferable points of protein and ligand conjugation. These approaches and commonly used fitting parameters restrict strongly the docking accuracy. Solvent is considered in exceedingly simplified approaches in the course of the global optimization and the search for the best ligand poses. More accurate approaches on the base of implicit solvent models are used frequently for more careful binding energy calculations after docking. The new generation of docking programs are developed recently. They find the spectrum of low energy minima of a protein-ligand complex including the global minimum. These programs should be more accurate because they do not use a preliminary calculated grid of protein-ligand interaction potentials and other simplifications, the energy of any conformation of the molecular system is calculated in the frame of a given force field and there are no fitting parameters. A new docking algorithm is developed and fulfilled specially for the new docking programs. This algorithm allows docking a flexible ligand into a flexible protein with several dozen mobile atoms on the base of the global energy minimum search. Such docking results in improving the accuracy of ligand positioning in the docking process. The adequate choice of the method of molecular energy calculations also results in the better docking positioning accuracy. An advancement in the application of quantum chemistry methods to docking and scoring is revealed.

Conclusion: The findings of this review confirm the great demand in docking programs for discovery of new medicine substances with the help of molecular modeling. New trends in docking programs design are revealed. These trends are focused on the increase of the docking accuracy at the expense of more accurate molecular energy calculations without any fitting parameters, including quantum-chemical methods and implicit solvent models, and by using new global optimization algorithms which make it possible to treat flexibility of ligands and mobility of protein atoms simultaneously. Finally, it is shown that all the necessary prerequisites for increasing the docking accuracy can be accomplished in practice.

Keywords: Docking, scoring, quantum chemistry, flexibility, global optimization, local optimization, drug design, force fields.

[1]
Chen, Y.C. Beware of docking! Trends Pharmacol. Sci., 2015, 36(2), 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID: 25543280]
[2]
Yuriev, E.; Holien, J.; Ramsland, P.A. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J. Mol. Recognit., 2015, 28(10), 581-604.
[http://dx.doi.org/10.1002/jmr.2471] [PMID: 25808539]
[3]
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: a review. Biophys. Rev., 2017, 9(2), 91-102.
[http://dx.doi.org/10.1007/s12551-016-0247-1] [PMID: 28510083]
[4]
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model., 2012, 52(7), 1757-1768.
[http://dx.doi.org/10.1021/ci3001277] [PMID: 22587354]
[5]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[6]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[7]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[8]
Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem., 2007, 28(6), 1145-1152.
[http://dx.doi.org/10.1002/jcc.20634] [PMID: 17274016]
[9]
Osterberg, F.; Morris, G.M.; Sanner, M.F.; Olson, A.J.; Goodsell, D.S. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins, 2002, 46(1), 34-40.
[http://dx.doi.org/10.1002/prot.10028] [PMID: 11746701]
[10]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19(14), 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639:AID-JCC10>3.0.CO;2-B]
[11]
Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: applications of AutoDock. J. Mol. Recognit., 1996, 9(1), 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1:AID-JMR241>3.0.CO;2-6] [PMID: 8723313]
[12]
Neves, M.A.; Totrov, M.; Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des., 2012, 26(6), 675-686.
[http://dx.doi.org/10.1007/s10822-012-9547-0] [PMID: 22569591]
[13]
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(5), 488-506.
[http://dx.doi.org/10.1002/jcc.540150503]
[14]
Allen, W.J.; Balius, T.E.; Mukherjee, S.; Brozell, S.R.; Moustakas, D.T.; Lang, P.T.; Case, D.A.; Kuntz, I.D.; Rizzo, R.C. DOCK 6: Impact of new features and current docking performance. J. Comput. Chem., 2015, 36(15), 1132-1156.
[http://dx.doi.org/10.1002/jcc.23905] [PMID: 25914306]
[15]
Moustakas, D.T.; Scott, C.H.P.; Kuntz, I.D. A practical guide to DOCK 5 in: Virtual Screening in Drug Discovery; Alvarez, J; Shoichet, B.K., Ed.; Taylor & Francis Group, LLC, 2005, pp. 303-326.
[http://dx.doi.org/10.1201/9781420028775.pt5]
[16]
Romanov, A.N.; Kondakova, O.A.; Grigoriev, F.V.; Sulimov, A.V.; Luschekina, S.V.; Martynov, Y.B.; Sulimov, V.B. The SOL docking package for computer-aided drug design (in Russian). Numerical methods and programming, 2008, 9(2), 64-84.
[17]
Oferkin, I.V.; Sulimov, A.V.; Kondakova, O.A.; Sulimov, V.B. Implementation of parallel computing for docking programs SOLGRID and SOL (in Russian). Numerical methods and programming, 2011, 12, 205-219.
[18]
Sulimov, A.V.; Kutov, D.C.; Oferkin, I.V.; Katkova, E.V.; Sulimov, V.B. Application of the docking program SOL for CSAR benchmark. J. Chem. Inf. Model., 2013, 53(8), 1946-1956.
[http://dx.doi.org/10.1021/ci400094h] [PMID: 23829357]
[19]
Klimovich, P.V.; Shirts, M.R.; Mobley, D.L. Guidelines for the analysis of free energy calculations. J. Comput. Aided Mol. Des., 2015, 29(5), 397-411.
[http://dx.doi.org/10.1007/s10822-015-9840-9] [PMID: 25808134]
[20]
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., 2010, 31(2), 455-461.
[http://dx.doi.org/ 10.1002/jcc.21334] [PMID: 19499576]
[21]
Harris, R.; Olson, A.J.; Goodsell, D.S. Automated prediction of ligand-binding sites in proteins. Proteins, 2008, 70(4), 1506-1517.
[http://dx.doi.org/10.1002/prot.21645] [PMID: 17910060]
[22]
Baxter, J. Local optima avoidance in depot location. J. Oper. Res. Soc., 1981, 32(9), 815-819.
[http://dx.doi.org/10.1057/jors.1981.159]
[23]
Blum, C.; Roli, A.; Sampels, M., Eds.; Hybrid Metaheuristics: An Emerging Approach to Optimization; Springer, 2008.
[http://dx.doi.org/10.1007/978-3-540-78295-7]
[24]
Nocedal, J.; Wright, S.J. Numerical Optimization; Springer: New York, 2006.
[25]
Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys., 1953, 21(6), 1087-1092.
[http://dx.doi.org/10.1063/1.1699114]
[26]
Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28(7), 849-857.
[http://dx.doi.org/10.1021/jm00145a002] [PMID: 3892003]
[27]
Boobbyer, D.N.A.; Goodford, P.J.; McWhinnie, P.M.; Wade, R.C. New hydrogen-bond potentials for use in determining energetically favorable binding sites on molecules of known structure. J. Med. Chem., 1989, 32(5), 1083-1094.
[http://dx.doi.org/10.1021/jm00125a025] [PMID: 2709375]
[28]
Mehler, E.L.; Solmajer, T. Electrostatic effects in proteins: comparison of dielectric and charge models. Protein Eng., 1991, 4(8), 903-910.
[http://dx.doi.org/10.1093/protein/4.8.903] [PMID: 1667878]
[29]
Wesson, L.; Eisenberg, D. Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Sci., 1992, 1(2), 227-235.
[http://dx.doi.org/ 10.1002/pro.5560010204] [PMID: 1304905]
[30]
Chang, C.E.; Chen, W.; Gilson, M.K. Ligand configurational entropy and protein binding. Proc. Natl. Acad. Sci. USA, 2007, 104(5), 1534-1539.
[http://dx.doi.org/10.1073/pnas.0610494104] [PMID: 17242351]
[31]
Palos, I.; Lara-Ramirez, E.E.; Lopez-Cedillo, J.C.; Garcia-Perez, C.; Kashif, M.; Bocanegra-Garcia, V.; Nogueda-Torres, B.; Rivera, G. Repositioning FDA drugs as potential cruzain inhibitors from trypanosoma cruzi: virtual screening, in vitro and in vivo studies. Molecules, 2017, 22(6), 1015.
[http://dx.doi.org/10.3390/molecules22061015] [PMID: 28629155]
[32]
Totrov, M.; Abagyan, R. Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins, 1997, 29(Suppl. 1), 215-220.
[http://dx.doi.org/10.1002/(SICI)1097-0134(1997)1+<215:AID-PROT29>3.0.CO;2-Q] [PMID: 9485515]
[33]
Abagyan, R.; Totrov, M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol., 1994, 235(3), 983-1002.
[http://dx.doi.org/10.1006/jmbi.1994.1052] [PMID: 8289329]
[34]
Totrov, M.; Abagyan, R. Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide. Biopolymers, 2001, 60(2), 124-133.
[http://dx.doi.org/10.1002/1097-0282(2001)60:2<124:AID-BIP1008>3.0.CO;2-S] [PMID: 11455546]
[35]
Arnautova, Y.A.; Abagyan, R.A.; Totrov, M. Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling. Proteins, 2011, 79(2), 477-498.
[http://dx.doi.org/10.1002/prot.22896] [PMID: 21069716]
[36]
Halgren, T.A. Merck molecular force field. J. Comput. Chem., 1996, 17(5-6), 490-641.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490:AID-JCC1>3.0.CO;2-P]
[37]
Arnautova, Y.A.; Jagielska, A.; Scheraga, H.A. A new force field (ECEPP-05) for peptides, proteins, and organic molecules. J. Phys. Chem. B, 2006, 110(10), 5025-5044.
[http://dx.doi.org/10.1021/jp054994x] [PMID: 16526746]
[38]
Schapira, M.; Abagyan, R.; Totrov, M. Nuclear hormone receptor targeted virtual screening. J. Med. Chem., 2003, 46(14), 3045-3059.
[http://dx.doi.org/10.1021/jm0300173] [PMID: 12825943]
[39]
Schapira, M.; Totrov, M.; Abagyan, R. Prediction of the binding energy for small molecules, peptides and proteins. J. Mol. Recognit., 1999, 12(3), 177-190.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199905/06)12:3<177:AID-JMR451>3.0.CO;2-Z] [PMID: 10398408]
[40]
Nicola, G.; Smith, C.A.; Lucumi, E.; Kuo, M.R.; Karagyozov, L.; Fidock, D.A.; Sacchettini, J.C.; Abagyan, R. Discovery of novel inhibitors targeting enoyl-acyl carrier protein reductase in Plasmodium falciparum by structure-based virtual screening. Biochem. Biophys. Res. Commun., 2007, 358(3), 686-691.
[http://dx.doi.org/10.1016/j.bbrc.2007.04.113] [PMID: 17509532]
[41]
Brozell, S.R.; Mukherjee, S.; Balius, T.E.; Roe, D.R.; Case, D.A.; Rizzo, R.C. Evaluation of DOCK 6 as a pose generation and database enrichment tool. J. Comput. Aided Mol. Des., 2012, 26(6), 749-773.
[http://dx.doi.org/10.1007/s10822-012-9565-y] [PMID: 22569593]
[42]
Kolossvary, I.; Guida, W.C. Low mode search. An efficient, automated computational method for conformational analysis: apprication to cyclic and acyclic alkanes and cyclic peptides. J. Am. Chem. Soc., 1996, 118(21), 5011-5019.
[http://dx.doi.org/10.1021/ja952478m]
[43]
Kolossvary, I.; Keseru, G.M. Hessian-free low-mode conformational search for large-scale protein loop optimization: application to c-jun N-terminal kinase JNK3. J. Comput. Chem., 2001, 22(1), 21-30.
[http://dx.doi.org/10.1002/1096-987X(20010115)22:1<21:AID-JCC3>3.0.CO;2-I]
[44]
Becker, O.M.; Dhanoa, D.S.; Marantz, Y.; Chen, D.; Shacham, S.; Cheruku, S.; Heifetz, A.; Mohanty, P.; Fichman, M.; Sharadendu, A.; Nudelman, R.; Kauffman, M.; Noiman, S. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J. Med. Chem., 2006, 49(11), 3116-3135.
[http://dx.doi.org/10.1021/jm0508641] [PMID: 16722631]
[45]
Cole, J.C.; Nissink, J.W.M.; Taylor, R. Protein-ligand docking and virtual screening with gold in: Virtual Screening in Drug Discovery; Alvarez, J; Shoichet, B.K., Ed.; Taylor & Francis Group, LLC, 2005, pp. 379-415.
[http://dx.doi.org/10.1201/9781420028775.ch15]
[46]
Liebeschuetz, J.W.; Cole, J.C.; Korb, O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J. Comput. Aided Mol. Des., 2012, 26(6), 737-748.
[http://dx.doi.org/10.1007/s10822-012-9551-4] [PMID: 22371207]
[47]
Clark, M.; Cramer, R.D.; Van Opdenbosch, N. Validation of the general purpose tripos 5.2 force field. J. Comput. Chem., 1989, 10(8), 982-1012.
[http://dx.doi.org/10.1002/jcc.540100804]
[48]
Mooij, W.T.M.; Verdonk, M.L. General and targeted statistical potentials for protein-ligand interactions. Proteins, 2005, 61(2), 272-287.
[http://dx.doi.org/10.1002/prot.20588] [PMID: 16106379]
[49]
Korb, O.; Stützle, T.; Exner, T.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J. Chem. Inf. Model., 2009, 49(1), 84-96.
[http://dx.doi.org/10.1021/ci800298z] [PMID: 19125657]
[50]
Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins, 2003, 52(4), 609-623.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
[51]
Desai, P.V.; Patny, A.; Sabnis, Y.; Tekwani, B.; Gut, J.; Rosenthal, P.; Srivastava, A.; Avery, M. Identification of novel parasitic cysteine protease inhibitors using virtual screening. 1. The ChemBridge database. J. Med. Chem., 2004, 47(26), 6609-6615.
[http://dx.doi.org/10.1021/jm0493717] [PMID: 15588096]
[52]
Dayam, R.; Sanchez, T.; Clement, O.; Shoemaker, R.; Sei, S.; Neamati, N. β-diketo acid pharmacophore hypothesis. 1. Discovery of a novel class of HIV-1 integrase inhibitors. J. Med. Chem., 2005, 48(1), 111-120.
[http://dx.doi.org/10.1021/jm0496077] [PMID: 15634005]
[53]
Jain, A.N. Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J. Comput. Aided Mol. Des., 1996, 10(5), 427-440.
[http://dx.doi.org/10.1007/BF00124474] [PMID: 8951652]
[54]
Pham, T.A.; Jain, A.N. Customizing scoring functions for docking. J. Comput. Aided Mol. Des., 2008, 22(5), 269-286.
[http://dx.doi.org/10.1007/s10822-008-9174-y] [PMID: 18273558]
[55]
Jain, A.N. Morphological similarity: a 3D molecular similarity method correlated with protein-ligand recognition. J. Comput. Aided Mol. Des., 2000, 14(2), 199-213.
[http://dx.doi.org/10.1023/A:1008100132405] [PMID: 10721506]
[56]
Jain, A.N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 2003, 46(4), 499-511.
[http://dx.doi.org/10.1021/jm020406h] [PMID: 12570372]
[57]
Jain, A.N. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J. Comput. Aided Mol. Des., 2009, 23(6), 355-374.
[http://dx.doi.org/10.1007/s10822-009-9266-3] [PMID: 19340588]
[58]
Böhm, H.J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des., 1994, 8(3), 243-256.
[http://dx.doi.org/10.1007/BF00126743] [PMID: 7964925]
[59]
Kumar, R.; Kumar, A.; Långström, B.; Darreh-Shori, T. Discovery of novel choline acetyltransferase inhibitors using structure-based virtual screening. Sci. Rep., 2017, 7(1), 16287.
[http://dx.doi.org/10.1038/s41598-017-16033-w] [PMID: 29176551]
[60]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[61]
Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47(7), 1750-1759.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
[62]
Jorgensen, W.L.; Maxwell, D.S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc., 1996, 118(45), 11225-11236.
[http://dx.doi.org/10.1021/ja9621760]
[63]
Eldridge, M.D.; Murray, C.W.; Auton, T.R.; Paolini, G.V.; Mee, R.P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des., 1997, 11(5), 425-445.
[http://dx.doi.org/10.1023/A:1007996124545] [PMID: 9385547]
[64]
Nikitina, E.; Sulimov, V.; Grigoriev, F.; Kondakova, O.; Luschekina, S. Mixed implicit/explicit solvation models in quantum mechanical calculations of binding enthalpy for protein-ligand complexes. Int. J. Quantum Chem., 2006, 106(8), 1943-1963.
[http://dx.doi.org/10.1002/qua.20943]
[65]
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49(21), 6177-6196.
[http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]
[66]
Siddiquee, K.; Zhang, S.; Guida, W.C.; Blaskovich, M.A.; Greedy, B.; Lawrence, H.R.; Yip, M.L.R.; Jove, R.; McLaughlin, M.M.; Lawrence, N.J.; Sebti, S.M.; Turkson, J. Selective chemical probe inhibitor of Stat3, identified through structure-based virtual screening, induces antitumor activity. Proc. Natl. Acad. Sci. USA, 2007, 104(18), 7391-7396.
[http://dx.doi.org/10.1073/pnas.0609757104] [PMID: 17463090]
[67]
Ward, R.A.; Perkins, T.D.J.; Stafford, J. Structure-based virtual screening for low molecular weight chemical starting points for dipeptidyl peptidase IV inhibitors. J. Med. Chem., 2005, 48(22), 6991-6996.
[http://dx.doi.org/10.1021/jm0505866] [PMID: 16250657]
[68]
Tintori, C.; Laurenzana, I.; Fallacara, A.L.; Kessler, U.; Pilger, B.; Stergiou, L.; Botta, M. High-throughput docking for the identification of new influenza A virus polymerase inhibitors targeting the PA-PB1 protein-protein interaction. Bioorg. Med. Chem. Lett., 2014, 24(1), 280-282.
[http://dx.doi.org/10.1016/j.bmcl.2013.11.019] [PMID: 24314669]
[69]
Romanov, A.N.; Jabin, S.N.; Martynov, Y.B.; Sulimov, A.V.; Grigoriev, F.V.; Sulimov, V.B. Surface generalized born method: a simple, fast, and precise implicit solvent model beyond the coulomb approximation. J. Phys. Chem. A, 2004, 108(43), 9323-9327.
[http://dx.doi.org/10.1021/jp046721s]
[70]
Katkova, E.V. Investigation of influence of genetic algorithm parameters on the docking effectivness with the SOL program (in Russian). Numerical methods and programming, 2012, 13, 539-550.
[71]
Damm-Ganamet, K.L.; Smith, R.D.; Dunbar, J.B., Jr; Stuckey, J.A.; Carlson, H.A. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J. Chem. Inf. Model., 2013, 53(8), 1853-1870.
[http://dx.doi.org/10.1021/ci400025f] [PMID: 23548044]
[72]
Sinauridze, E.I.; Romanov, A.N.; Gribkova, I.V.; Kondakova, O.A.; Surov, S.S.; Gorbatenko, A.S.; Butylin, A.A.; Monakov, M.Y.; Bogolyubov, A.A.; Kuznetsov, Y.V.; Sulimov, V.B.; Ataullakhanov, F.I. New synthetic thrombin inhibitors: molecular design and experimental verification. PLoS One, 2011, 6(5)e19969
[http://dx.doi.org/10.1371/journal.pone.0019969] [PMID: 21603576]
[73]
Sulimov, V.B.; Katkova, E.V.; Oferkin, I.V.; Sulimov, A.V.; Romanov, A.N.; Roschin, A.I.; Beloglazova, I.B.; Plekhanova, O.S.; Tkachuk, V.A.; Sadovnichiy, V.A. Application of molecular modeling to urokinase inhibitors development. BioMed Res. Int., 2014, 2014625176
[http://dx.doi.org/10.1155/2014/625176] [PMID: 24967388]
[74]
Sulimov, V.B.; Gribkova, I.V.; Kochugaeva, M.P.; Katkova, E.V.; Sulimov, A.V.; Kutov, D.C.; Shikhaliev, K.S.; Medvedeva, S.M.; Krysin, M.Y.; Sinauridze, E.I.; Ataullakhanov, F.I. Application of molecular modeling to development of new factor Xa inhibitors. BioMed Res. Int., 2015, 2015120802
[http://dx.doi.org/10.1155/2015/120802] [PMID: 26484350]
[75]
Repasky, M.P.; Murphy, R.B.; Banks, J.L.; Greenwood, J.R.; Tubert-Brohman, I.; Bhat, S.; Friesner, R.A. Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J. Comput. Aided Mol. Des., 2012, 26(6), 787-799.
[http://dx.doi.org/10.1007/s10822-012-9575-9] [PMID: 22576241]
[76]
McGann, M. FRED and HYBRID docking performance on standardized datasets. J. Comput. Aided Mol. Des., 2012, 26(8), 897-906.
[http://dx.doi.org/10.1007/s10822-012-9584-8] [PMID: 22669221]
[77]
Schneider, N.; Hindle, S.; Lange, G.; Klein, R.; Albrecht, J.; Briem, H.; Beyer, K.; Claußen, H.; Gastreich, M.; Lemmen, C.; Rarey, M. Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function. J. Comput. Aided Mol. Des., 2012, 26(6), 701-723.
[http://dx.doi.org/10.1007/s10822-011-9531-0] [PMID: 22203423]
[78]
Novikov, F.N.; Stroylov, V.S.; Zeifman, A.A.; Stroganov, O.V.; Kulkov, V.; Chilov, G.G. Lead Finder docking and virtual screening evaluation with Astex and DUD test sets. J. Comput. Aided Mol. Des., 2012, 26(6), 725-735.
[http://dx.doi.org/10.1007/s10822-012-9549-y] [PMID: 22569592]
[79]
Corbeil, C.R.; Williams, C.I.; Labute, P. Variability in docking success rates due to dataset preparation. J. Comput. Aided Mol. Des., 2012, 26(6), 775-786.
[http://dx.doi.org/10.1007/s10822-012-9570-1] [PMID: 22566074]
[80]
Spitzer, R.; Jain, A.N. Surflex-Dock: Docking benchmarks and real-world application. J. Comput. Aided Mol. Des., 2012, 26(6), 687-699.
[http://dx.doi.org/10.1007/s10822-011-9533-y] [PMID: 22569590]
[81]
Oferkin, I.V.; Katkova, E.V.; Sulimov, A.V.; Kutov, D.C.; Sobolev, S.I.; Voevodin, V.V.; Sulimov, V.B. Evaluation of docking target functions by the comprehensive investigation of protein-ligand energy minima. Adv. Bioinforma., 2015, 2015126858
[http://dx.doi.org/10.1155/2015/126858] [PMID: 26693223]
[82]
Oferkin, I.V.; Sulimov, A.V.; Katkova, E.V.; Kutov, D.K.; Grigoriev, F.V.; Kondakova, O.A.; Sulimov, V.B. [Supercomputer investigation of the protein-ligand system low-energy minima] Biomed. Khim., 2015, 61(6), 712-716.
[http://dx.doi.org/10.18097/PBMC20156106712] [PMID: 26716742]
[83]
Oferkin, I.V.; Zheltkov, D.A.; Tyrtyshnikov, E.E.; Sulimov, A.V.; Kutov, D.C.; Sulimov, V.B. Evaluation of the docking algorithm based on Tensor Train global optimization. Bulletin of the South Ural State University, Ser. Mathematical Modelling. Program. Comput. Softw., 2015, 8(4), 83-99.
[84]
Sulimov, A.V.; Zheltkov, D.A.; Oferkin, I.V.; Kutov, D.C.; Katkova, E.V.; Tyrtyshnikov, E.E.; Sulimov, V.B. Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms. Comput. Struct. Biotechnol. J., 2017, 15, 275-285.
[http://dx.doi.org/10.1016/j.csbj.2017.02.004] [PMID: 28377797]
[85]
Sulimov, A.V.; Zheltkov, D.A.; Oferkin, I.V.; Kutov, D.C.; Katkova, E.V.; Tyrtyshnikov, E.E.; Sulimov, V.B. Tensor Train global optimization: application to docking in the configuration space with a large number of dimensions in: Supercomputing: Third Russian Supercomputing Days, RuSCDays 2017, Moscow, Russia, Revised Selected Papers. Voevodin, V; Sobolev, S., Ed.; Springer International Publishing: Cham, 2017, pp. 151-167.
[86]
Chen, W.; Gilson, M.K.; Webb, S.P.; Potter, M.J. Modeling protein-ligand binding by mining minima. J. Chem. Theory Comput., 2010, 6(11), 3540-3557.
[http://dx.doi.org/10.1021/ct100245n] [PMID: 22639555]
[87]
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Ilin, I.S.; Sulimov, V.B. New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking. J. Mol. Graph. Model., 2017, 78, 139-147.
[http://dx.doi.org/10.1016/j.jmgm.2017.10.007] [PMID: 29055806]
[88]
Byrd, R.; Lu, P.; Nocedal, J.; Zhu, C. A Limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 1995, 16(5), 1190-1208.
[http://dx.doi.org/10.1137/0916069]
[89]
Zhu, C.; Byrd, R.H.; Lu, P.; Nocedal, J. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw., 1997, 23(4), 550-560.
[http://dx.doi.org/10.1145/279232.279236]
[90]
Sadovnichy, V. Tikhonravov,, A.; Voevodin,, V.; Opanasenko,, V. In: ContemporaryHigh Performance Computing: From Petascale toward Exascale;; Boca Raton, United States: Boca Raton, United States, 2013; pp. 283-307.
[91]
MSU Supercomputers: Lomonosov-2. Available at:. http://hpc.msu.ru/?q=node/159 (Accessed Date: 10 February, 2018)
[92]
Stewart, J.J. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters. J. Mol. Model., 2013, 19(1), 1-32.
[http://dx.doi.org/10.1007/s00894-012-1667-x] [PMID: 23187683]
[93]
Klamt, A.; Schuurmann, G. COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J. Chem. Soc., Perkin Trans. 2, 1993, (5), 799-805.
[http://dx.doi.org/10.1039/P29930000799]
[94]
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Sulimov, V.B. Combined docking with classical force field and quantum chemical semiempirical method PM7. Adv. Bioinforma., 2017, 20177167691
[http://dx.doi.org/10.1155/2017/7167691] [PMID: 28191015]
[95]
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Kondakova, O.A.; Sulimov, V.B. earch for approaches to improving the calculation accuracy of the protein-ligand binding energy by docking. Russian Chemical Bulletin; International Edition, 2017, 66, pp. (10)1913-1924.
[96]
Řezáč, J.; Hobza, P. Advanced corrections of hydrogen bonding and dispersion for semiempirical quantum mechanical methods. J. Chem. Theory Comput., 2012, 8(1), 141-151.
[http://dx.doi.org/10.1021/ct200751e] [PMID: 26592877]
[97]
Řezáč, J.; Hobza, P. A halogen-bonding correction for the semiempirical PM6 method. Chem. Phys. Lett., 2011, 506(4), 286-289.
[http://dx.doi.org/10.1016/j.cplett.2011.03.009]
[98]
Pecina, A.; Meier, R.; Fanfrlík, J.; Lepšík, M.; Řezáč, J.; Hobza, P.; Baldauf, C. The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. Chem. Commun. (Camb.), 2016, 52(16), 3312-3315.
[http://dx.doi.org/10.1039/C5CC09499B] [PMID: 26821703]
[99]
Klebe, G. Applying thermodynamic profiling in lead finding and optimization. Nat. Rev. Drug Discov., 2015, 14(2), 95-110.
[http://dx.doi.org/10.1038/nrd4486] [PMID: 25614222]
[100]
Zheltkov, D.A.; Oferkin, I.V.; Katkova, E.V.; Sulimov, A.V.; Sulimov, V.B.; Tyrtyshnikov, E.E. TTDock: a docking method based on tensor train decompositions. Numerical methods and programming, 2013, 14, 279-291.
[101]
Zheltkov, D.A.; Tyrtyshnikov, E.E. The increase in dimensionality in the docking method based on tensor train (in russian) Numerical methods and programming (in Russian), 2013, 14, 292-294.
[102]
Oseledets, I.; Tyrtyshnikov, E. Breaking the Curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput., 2009, 31(5), 3744-3759.
[http://dx.doi.org/10.1137/090748330]
[103]
Oseledets, I.; Tyrtyshnikov, E. TT-cross approximation for multidimensional arrays. Linear Algebra Appl., 2010, 432(1), 70-88.
[http://dx.doi.org/10.1016/j.laa.2009.07.024]
[104]
Oseledets, I. Tensor-Train Decomposition. SIAM J. Sci. Comput., 2011, 33(5), 2295-2317.
[http://dx.doi.org/10.1137/090752286]
[105]
Goreinov, S.A.; Tyrtyshnikov, E.E.; Zamarashkin, N.L. A theory of pseudoskeleton approximations. Linear Algebra Appl., 1997, 261(1), 1-21.
[http://dx.doi.org/10.1016/S0024-3795(96)00301-1]
[106]
Tyrtyshnikov, E. Incomplete cross approximation in the mosaic-skeleton method. Computing, 2000, 64(4), 367-380.
[http://dx.doi.org/10.1007/s006070070031]
[107]
Goreinov, S.; Tyrtyshnikov, E. The maximal-volume concept in approximation by low-rank matrices. Contemp. Math., 2001, 268, 47-51.
[http://dx.doi.org/10.1090/conm/280/4620]
[108]
Goreinov, S.A.; Oseledets, I.V.; Savostyanov, D.V.; Tyrtyshnikov, E.E.; Zamarashkin, N.L. How to find a good submatrix. Matrix methods: theory, algorithms and applications, 2010, 247-256.
[http://dx.doi.org/10.1142/9789812836021_0015]
[109]
Zheltkov, D.A.; Tyrtyshnikov, E.E. Parallel implementation of matrix cross method. Numerical Methods and Programming, 2015, 16, 369-375.
[110]
Nelder, J.A.; Mead, R. A simplex method for function minimization. Comput. J., 1965, 7(4), 308-313.
[http://dx.doi.org/10.1093/comjnl/7.4.308]
[111]
Elstner, M.; Porezag, D.; Jungnickel, G.; Elsner, J.; Haugk, M.; Frauenheim, T.; Suhai, S.; Seifert, G. Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties. Phys. Rev. B., 1998, 58(11), 7260-7268.
[http://dx.doi.org/10.1103/PhysRevB.58.7260]
[112]
Ryde, U.; Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev., 2016, 116(9), 5520-5566.
[http://dx.doi.org/10.1021/acs.chemrev.5b00630] [PMID: 27077817]
[113]
Chaskar, P.; Zoete, V.; Röhrig, U.F. 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]
[114]
Zoete, V.; Schuepbach, T.; Bovigny, C.; Chaskar, P.; Daina, A.; Röhrig, U.F.; Michielin, O. Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape. J. Comput. Chem., 2016, 37(4), 437-447.
[http://dx.doi.org/10.1002/jcc.24249] [PMID: 26558715]
[115]
Brahmkshatriya, P.S.; Dobeš, P.; Fanfrlik, J.; Rezáç, J.; Paruch, K.; Bronowska, A.; Lepšík, M.; Hobza, P. Quantum mechanical scoring: structural and energetic insights into cyclin-dependent kinase 2 inhibition by pyrazolo[1,5-a]pyrimidines. Curr Comput Aided Drug Des, 2013, 9(1), 118-129.
[http://dx.doi.org/10.2174/1573409911309010011] [PMID: 23157414]
[116]
Rao, L.; Zhang, I.Y.; Guo, W.; Feng, L.; Meggers, E.; Xu, X. Nonfitting protein-ligand interaction scoring function based on first-principles theoretical chemistry methods: development and application on kinase inhibitors. J. Comput. Chem., 2013, 34(19), 1636-1646.
[http://dx.doi.org/10.1002/jcc.23303] [PMID: 23681957]
[117]
Yilmazer, N.D.; Korth, M. Recent progress in treating protein-ligand interactions with quantum-mechanical methods. Int. J. Mol. Sci., 2016, 17(5), 742.
[http://dx.doi.org/10.3390/ijms17050742] [PMID: 27196893]
[118]
Yilmazer, N.D.; Heitel, P.; Schwabe, T.; Korth, M. Benchmark of electronic structure methods for protein-ligand interactions based on high-level reference data. J. Theor. Comput. Chem., 2015, 141540001
[http://dx.doi.org/10.1142/S0219633615400015]
[119]
Sparta, M.; Neese, F. Chemical applications carried out by local pair natural orbital based coupled-cluster methods. Chem. Soc. Rev., 2014, 43(14), 5032-5041.
[http://dx.doi.org/10.1039/C4CS00050A] [PMID: 24676339]
[120]
Neese, F.; Hansen, A.; Liakos, D.G. Efficient and accurate approximations to the local coupled cluster singles doubles method using a truncated pair natural orbital basis. J. Chem. Phys., 2009, 131(6)064103
[http://dx.doi.org/10.1063/1.3173827] [PMID: 19691374]
[121]
Liakos, D.G.; Sparta, M.; Kesharwani, M.K.; Martin, J.M.L.; Neese, F. Exploring the accuracy limits of local pair natural orbital coupled-cluster theory. J. Chem. Theory Comput., 2015, 11(4), 1525-1539.
[http://dx.doi.org/10.1021/ct501129s] [PMID: 26889511]
[122]
Liakos, D.G.; Neese, F. Is it possible to obtain coupled cluster quality energies at near density functional theory cost? domain-based local pair natural orbital coupled cluster vs modern density functional theory. J. Chem. Theory Comput., 2015, 11(9), 4054-4063.
[http://dx.doi.org/10.1021/acs.jctc.5b00359] [PMID: 26575901]
[123]
Grimme, S. Accurate description of van der Waals complexes by density functional theory including empirical corrections. J. Comput. Chem., 2004, 25(12), 1463-1473.
[http://dx.doi.org/10.1002/jcc.20078] [PMID: 15224390]
[124]
Jurecka, P.; Cerný, J.; Hobza, P.; Salahub, D.R. Density functional theory augmented with an empirical dispersion term. Interaction energies and geometries of 80 noncovalent complexes compared with ab initio quantum mechanics calculations. J. Comput. Chem., 2007, 28(2), 555-569.
[http://dx.doi.org/10.1002/jcc.20570] [PMID: 17186489]
[125]
Foster, M.E.; Sohlberg, K. A new empirical correction to the AM1 method for macromolecular complexes. J. Chem. Theory Comput., 2010, 6(7), 2153-2166.
[http://dx.doi.org/10.1021/ct100177u] [PMID: 26615942]
[126]
Foster, M.E.; Sohlberg, K. Self-consistent addition of an atomic charge dependent hydrogen-bonding correction function. Comput. Theor. Chem., 2012, 984, 9-12.
[http://dx.doi.org/10.1016/j.comptc.2011.12.027]
[127]
Řezáč, J.; Fanfrlík, J.; Salahub, D.; Hobza, P. Semiempirical quantum chemical PM6 method augmented by dispersion and H-bonding correction terms reliably describes various types of noncovalent complexes. J. Chem. Theory Comput., 2009, 5(7), 1749-1760.
[http://dx.doi.org/10.1021/ct9000922] [PMID: 26610000]
[128]
Korth, M.; Pitoňák, M.; Řezáč, J.; Hobza, P. A transferable H-Bonding correction for semiempirical quantum-chemical methods. J. Chem. Theory Comput., 2010, 6(1), 344-352.
[http://dx.doi.org/10.1021/ct900541n] [PMID: 26614342]
[129]
Korth, M. Third-generation hydrogen-bonding corrections for semiempirical QM methods and force fields. J. Chem. Theory Comput., 2010, 6, 3808-3816.
[http://dx.doi.org/10.1021/ct100408b]
[130]
Kromann, J.C.; Christensen, A.S.; Steinmann, C.; Korth, M.; Jensen, J.H. A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+. PeerJ, 2014, 2e449
[http://dx.doi.org/10.7717/peerj.449] [PMID: 25024918]
[131]
Stewart, J.J.P. http://OpenMOPAC.net
[132]
Stewart, J.J.P. Application of localized molecular orbitals to the solution of semiempirical self-consistent field equations International Journal of Quantum Chemistry Volume 58, Issue 2. Int. J. Quantum Chem., 1996, 58(2), 133-146.
[http://dx.doi.org/10.1002/(SICI)1097-461X(1996)58:2<133:AID-QUA2>3.0.CO;2-Z]
[133]
Moghaddam, S.; Inoue, Y.; Gilson, M.K. Host-guest complexes with protein-ligand-like affinities: computational analysis and design. J. Am. Chem. Soc., 2009, 131(11), 4012-4021.
[http://dx.doi.org/10.1021/ja808175m] [PMID: 19133781]
[134]
Grimme, S. Supramolecular binding thermodynamics by dispersion-corrected density functional theory. Chemistry, 2012, 18(32), 9955-9964.
[http://dx.doi.org/10.1002/chem.201200497] [PMID: 22782805]
[135]
Muddana, H.S.; Gilson, M.K. Calculation of host-guest binding affinities using a quantum-mechanical energy model. J. Chem. Theory Comput., 2012, 8(6), 2023-2033.
[http://dx.doi.org/10.1021/ct3002738] [PMID: 22737045]
[136]
Yilmazer, N.D.; Korth, M. Comparison of molecular mechanics, semi-empirical quantum mechanical, and density functional theory methods for scoring protein-ligand interactions. J. Phys. Chem. B, 2013, 117(27), 8075-8084.
[http://dx.doi.org/10.1021/jp402719k] [PMID: 23758433]
[137]
Fanfrlík, J.; Brahmkshatriya, P.S.; Řezáč, J.; Jílková, A.; Horn, M.; Mareš, M.; Hobza, P.; Lepšík, M. 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), 14973-14982.
[http://dx.doi.org/10.1021/jp409604n] [PMID: 24195769]
[138]
Fanfrlík, J.; Bronowska, A.K.; Rezác, J.; Prenosil, O.; Konvalinka, J.; Hobza, P. A reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 protease with 22 ligands. J. Phys. Chem. B, 2010, 114(39), 12666-12678.
[http://dx.doi.org/10.1021/jp1032965] [PMID: 20839830]
[139]
Vorlová, B.; Nachtigallová, D.; Jirásková-Vaníčková, J.; Ajani, H.; Jansa, P.; Rezáč, J.; Fanfrlík, J.; Otyepka, M.; Hobza, P.; Konvalinka, J.; Lepšík, M. Malonate-based inhibitors of mammalian serine racemase: kinetic characterization and structure-based computational study. Eur. J. Med. Chem., 2015, 89, 189-197.
[http://dx.doi.org/10.1016/j.ejmech.2014.10.043] [PMID: 25462239]
[140]
Stigliani, J-L.; Bernardes-Génisson, V.; Bernadou, J.; Pratviel, G. Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations. Org. Biomol. Chem., 2012, 10(31), 6341-6349.
[http://dx.doi.org/10.1039/c2ob25602a] [PMID: 22751934]
[141]
Ucisik, M.N.; Zheng, Z.; Faver, J.C.; Merz, K.M. Bringing clarity to the prediction of protein-ligand binding free energies via “Blurring”. J. Chem. Theory Comput., 2014, 10(3), 1314-1325.
[http://dx.doi.org/10.1021/ct400995c] [PMID: 24803861]
[142]
Raha, K.; Merz, K.M. Jr. Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem., 2005, 48(14), 4558-4575.
[http://dx.doi.org/10.1021/jm048973n] [PMID: 15999994]
[143]
Fong, P.; McNamara, J.P.; Hillier, I.H.; Bryce, R.A. Assessment of QM/MM scoring functions for molecular docking to HIV-1 protease. J. Chem. Inf. Model., 2009, 49(4), 913-924.
[http://dx.doi.org/10.1021/ci800432s] [PMID: 19309119]
[144]
Pan, X-L.; Liu, W.; Liu, J-Y. Mechanism of the glycosylation step catalyzed by human α-galactosidase: a QM/MM metadynamics study. J. Phys. Chem. B, 2013, 117(2), 484-489.
[http://dx.doi.org/10.1021/jp308747c] [PMID: 23249437]
[145]
Fanfrlík, J.; Kolář, M.; Kamlar, M.; Hurný, D.; Ruiz, F.X.; Cousido-Siah, A.; Mitschler, A.; Rezáč, J.; Munusamy, E.; Lepšík, M.; Matějíček, P.; Veselý, J.; Podjarny, A.; Hobza, P. Modulation of aldose reductase inhibition by halogen bond tuning. ACS Chem. Biol., 2013, 8(11), 2484-2492.
[http://dx.doi.org/10.1021/cb400526n] [PMID: 23988122]
[146]
Ilatovskiy, A.V.; Abagyan, R.; Kufareva, I. Quantum mechanics approaches to drug research in the era of structural chemogenomics. Int. J. Quantum Chem., 2013, 113(12), 1669-1675.
[http://dx.doi.org/10.1002/qua.24400] [PMID: 25414519]
[147]
Sulimov, V.B.; Mikhalev, A.Y.; Oferkin, I.V.; Oseledets, I.V.; Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Tyrtyshnikov, E.E. Polarized continuum solvent model: considerable acceleration with the multicharge matrix approximation. International Journal of Applied Engineering Research, 2015, 10(24), 44815-44830.
[148]
Katkova, E.V.; Onufriev, A.V.; Aguilar, B.; Sulimov, V.B. Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding. J. Mol. Graph. Model., 2017, 72(Suppl. C), 70-80.
[http://dx.doi.org/10.1016/j.jmgm.2016.12.011] [PMID: 28064081]
[149]
Gioia, D.; Bertazzo, M.; Recanatini, M.; Masetti, M.; Cavalli, A. Dynamic docking: a paradigm shift in computational drug discovery. Molecules, 2017, 22(11)E2029
[http://dx.doi.org/10.3390/molecules22112029] [PMID: 29165360]
[150]
Kutov, D.C.; Katkova, E.V.; Kondakova, O.A.; Sulimov, A.V.; Sulimov, V.B. Influence of the method of hydrogen atoms incorporation into the target protein on the protein-ligand binding energy. Bulletin of the South Ural State University, Ser. Mathematical Modelling. Program. Comput. Softw., 2017, 10(3), 94-107.
[151]
Sulimov, V.B.; Sulimov, A.V. Docking: molecular modeling for drug discovery. (in Russian); AINTELL: Moscow, 2017.


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