Review Article

对接进展

卷 26, 期 42, 2019

页: [7555 - 7580] 页: 26

弟呕挨: 10.2174/0929867325666180904115000

价格: $65

摘要

背景:能够与引起疾病的蛋白质结合的小分子设计是新医学发现整个过程的关键步骤。原子计算机建模可以显着提高这种设计的有效性。精确计算将小分子(配体)与靶蛋白结合的自由能是此类建模的最重要问题。对接是最流行的分子建模方法之一,用于发现目标蛋白质中的配体结合姿势并计算蛋白质-配体结合能。该能量用于为给定的目标蛋白质寻找活性最高的化合物。这篇简短的评论旨在简要介绍对接程序的独特功能,重点在于影响其准确性的计算方法和近似值。 方法:这篇评论是基于同行评审的研究文献,包括作者自己的出版物。简要介绍了几种有代表性的对接程序的主要功能,着重介绍了影响对接精度的特征:力场,能量计算,溶剂模型,最佳配体位姿搜索算法,全局和局部优化,配体和目标蛋白质的灵活性以及简化方法为对接加速。除了最近的其他评论主要集中在不同对接程序的性能上,在这项工作中,还尝试提取定义对接精度的最重要的功能特征。还提出了提高对接精度的路线图。这是基于最近实现的新一代对接程序。简短描述了这些程序和各自的新全局优化算法。 结果:考虑了几种流行的常规对接程序。他们对最佳配体位姿的搜索明确或隐含地基于全局优化问题。几种算法被用来解决这个问题,其中启发式遗传算法以其受欢迎程度和精心设计而著称。所有传统的对接程序都使用它们的加速来初步计算蛋白质-配体相互作用潜能的初步网格或蛋白质与配体结合的优选点。这些方法和常用的拟合参数强烈限制了对接精度。在全局优化和寻找最佳配体姿势的过程中,溶剂被认为是极其简化的方法。在对接后,经常使用基于隐式溶剂模型的更准确方法来进行更仔细的结合能计算。最近开发了新一代的对接程序。他们发现了蛋白质-配体复合物的低能极小光谱,包括全局极小值。这些程序应该更准确,因为它们没有使用蛋白质-配体相互作用势的初步计算网格和其他简化形式,分子系统任何构象的能量都是在给定力场的框架内计算的,并且没有合适的参数。为新的对接程序专门开发并实现了新的对接算法。该算法允许在全局能量最小搜索的基础上将柔性配体对接成具有数十个移动原子的柔性蛋白质。这种对接导致在对接过程中提高配体定位的准确性。分子能量计算方法的适当选择还导致更好的对接定位精度。揭示了量子化学方法在对接和评分中的应用进展。 结论:本综述的结果证实了对接程序对借助分子建模发现新药的巨大需求。揭示了对接程序设计的新趋势。这些趋势集中在提高对接精度上,而以更精确的分子能量计算为代价,而没有使用任何合适的参数,包括量子化学方法和隐式溶剂模型,并使用了新的全局优化算法,从而可以处理配体和蛋白质原子的迁移率同时出现。最终,表明提高对接精度的所有必要先决条件都可以在实践中完成。

关键词: 对接,计分,量子化学,灵活性,全局优化,局部优化,药物设计,力场。

[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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