Literature DB >> 19007114

Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening.

Oleg V Stroganov1, Fedor N Novikov, Viktor S Stroylov, Val Kulkov, Ghermes G Chilov.   

Abstract

An innovative molecular docking algorithm and three specialized high accuracy scoring functions are introduced in the Lead Finder docking software. Lead Finder's algorithm for ligand docking combines the classical genetic algorithm with various local optimization procedures and resourceful exploitation of the knowledge generated during docking process. Lead Finder's scoring functions are based on a molecular mechanics functional which explicitly accounts for different types of energy contributions scaled with empiric coefficients to produce three scoring functions tailored for (a) accurate binding energy predictions; (b) correct energy-ranking of docked ligand poses; and (c) correct rank-ordering of active and inactive compounds in virtual screening experiments. The predicted values of the free energy of protein-ligand binding were benchmarked against a set of experimentally measured binding energies for 330 diverse protein-ligand complexes yielding rmsd of 1.50 kcal/mol. The accuracy of ligand docking was assessed on a set of 407 structures, which included almost all published test sets of the following programs: FlexX, Glide SP, Glide XP, Gold, LigandFit, MolDock, and Surflex. rmsd of 2 A or less was observed for 80-96% of the structures in the test sets (80.0% on the Glide XP and FlexX test sets, 96.0% on the Surflex and MolDock test sets). The ability of Lead Finder to distinguish between active and inactive compounds during virtual screening experiments was benchmarked against 34 therapeutically relevant protein targets. Impressive enrichment factors were obtained for almost all of the targets with the average area under receiver operator curve being equal to 0.92.

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Year:  2008        PMID: 19007114     DOI: 10.1021/ci800166p

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  43 in total

1.  PF-114, a potent and selective inhibitor of native and mutated BCR/ABL is active against Philadelphia chromosome-positive (Ph+) leukemias harboring the T315I mutation.

Authors:  A A Mian; A Rafiei; I Haberbosch; A Zeifman; I Titov; V Stroylov; A Metodieva; O Stroganov; F Novikov; B Brill; G Chilov; D Hoelzer; O G Ottmann; M Ruthardt
Journal:  Leukemia       Date:  2014-11-14       Impact factor: 11.528

2.  Hit clustering can improve virtual fragment screening: CDK2 and PARP1 case studies.

Authors:  Alexey A Zeifman; Victor S Stroylov; Fedor N Novikov; Oleg V Stroganov; Alexandra L Zakharenko; Svetlana N Khodyreva; Olga I Lavrik; Ghermes G Chilov
Journal:  J Mol Model       Date:  2011-11-09       Impact factor: 1.810

3.  Lead Finder docking and virtual screening evaluation with Astex and DUD test sets.

Authors:  Fedor N Novikov; Viktor S Stroylov; Alexey A Zeifman; Oleg V Stroganov; Val Kulkov; Ghermes G Chilov
Journal:  J Comput Aided Mol Des       Date:  2012-05-09       Impact factor: 3.686

4.  Improving performance of docking-based virtual screening by structural filtration.

Authors:  Fedor N Novikov; Viktor S Stroylov; Oleg V Stroganov; Ghermes G Chilov
Journal:  J Mol Model       Date:  2009-12-30       Impact factor: 1.810

5.  [The structural protein Gag of the gypsy retrovirus forms virus-like particles in the bacterial cell].

Authors:  B V Semin; L A Ivanova; V I Popenko; Iu V Il'in
Journal:  Mol Biol (Mosk)       Date:  2011 May-Jun

6.  Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.

Authors:  G Madhavi Sastry; Matvey Adzhigirey; Tyler Day; Ramakrishna Annabhimoju; Woody Sherman
Journal:  J Comput Aided Mol Des       Date:  2013-04-12       Impact factor: 3.686

7.  Labelling Herceptin with a novel oxaliplatin derivative: a computational approach towards the selective drug delivery.

Authors:  José P Cerón-Carrasco; Javier Cerezo; Alberto Requena; José Zuñiga; Julia Contreras-García; Sonali Chavan; Miguel Manrubia-Cobo; Horacio Pérez-Sánchez
Journal:  J Mol Model       Date:  2014-08-23       Impact factor: 1.810

8.  The role of human in the loop: lessons from D3R challenge 4.

Authors:  Oleg V Stroganov; Fedor N Novikov; Michael G Medvedev; Artem O Dmitrienko; Igor Gerasimov; Igor V Svitanko; Ghermes G Chilov
Journal:  J Comput Aided Mol Des       Date:  2020-01-21       Impact factor: 3.686

9.  D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.

Authors:  Zied Gaieb; Conor D Parks; Michael Chiu; Huanwang Yang; Chenghua Shao; W Patrick Walters; Millard H Lambert; Neysa Nevins; Scott D Bembenek; Michael K Ameriks; Tara Mirzadegan; Stephen K Burley; Rommie E Amaro; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2019-01-10       Impact factor: 3.686

10.  Computational Design of Novel Allosteric Inhibitors for Plasmodium falciparum DegP.

Authors:  Sadaf Shehzad; Rajan Pandey; Pawan Malhotra; Dinesh Gupta
Journal:  Molecules       Date:  2021-05-07       Impact factor: 4.411

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