Literature DB >> 22180049

Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors.

Georgiana Surpateanu1, Bogdan I Iorga.   

Abstract

In this study, we have "blindly" assessed the ability of several combinations of docking software and scoring functions to predict the binding of a fragment-like library of bovine trypsine inhibitors. The most suitable protocols (involving Gold software and GoldScore scoring function, with or without rescoring) were selected for this purpose using a training set of compounds with known biological activities. The selected virtual screening protocols provided good results with the SAMPL3-VS dataset, showing enrichment factors of about 10 for Top 20 compounds. This methodology should be useful in difficult cases of docking, with a special emphasis on the fragment-based virtual screening campaigns.

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Year:  2011        PMID: 22180049     DOI: 10.1007/s10822-011-9526-x

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  29 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Announcing the worldwide Protein Data Bank.

Authors:  Helen Berman; Kim Henrick; Haruki Nakamura
Journal:  Nat Struct Biol       Date:  2003-12

3.  Virtual screening using protein-ligand docking: avoiding artificial enrichment.

Authors:  Marcel L Verdonk; Valerio Berdini; Michael J Hartshorn; Wijnand T M Mooij; Christopher W Murray; Richard D Taylor; Paul Watson
Journal:  J Chem Inf Comput Sci       Date:  2004 May-Jun

4.  In silico fragment-based discovery of DPP-IV S1 pocket binders.

Authors:  Christian Rummey; Sonja Nordhoff; Meinolf Thiemann; Günther Metz
Journal:  Bioorg Med Chem Lett       Date:  2006-03-01       Impact factor: 2.823

5.  Challenges of fragment screening.

Authors:  Diane Joseph-McCarthy
Journal:  J Comput Aided Mol Des       Date:  2009-06-30       Impact factor: 3.686

6.  Docking performance of fragments and druglike compounds.

Authors:  Marcel L Verdonk; Ilenia Giangreco; Richard J Hall; Oliver Korb; Paul N Mortenson; Christopher W Murray
Journal:  J Med Chem       Date:  2011-07-06       Impact factor: 7.446

Review 7.  Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

Authors:  Obdulia Rabal; Manuel Urbano-Cuadrado; Julen Oyarzabal
Journal:  Future Med Chem       Date:  2011-01       Impact factor: 3.808

8.  Structure of the complex of trypsin with a highly potent synthetic inhibitor at 0.97 A resolution.

Authors:  Manashi Sherawat; Punit Kaur; Markus Perbandt; Christian Betzel; William A Slusarchyk; Gregory S Bisacchi; Chiehying Chang; Bruce L Jacobson; Howard M Einspahr; Tej P Singh
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2007-03-16

9.  A novel serine protease inhibition motif involving a multi-centered short hydrogen bonding network at the active site.

Authors:  B A Katz; K Elrod; C Luong; M J Rice; R L Mackman; P A Sprengeler; J Spencer; J Hataye; J Janc; J Link; J Litvak; R Rai; K Rice; S Sideris; E Verner; W Young
Journal:  J Mol Biol       Date:  2001-04-13       Impact factor: 5.469

10.  Structural basis for inhibition promiscuity of dual specific thrombin and factor Xa blood coagulation inhibitors.

Authors:  H Nar; M Bauer; A Schmid; J M Stassen; W Wienen; H W Priepke; I K Kauffmann; U J Ries; N H Hauel
Journal:  Structure       Date:  2001-01-10       Impact factor: 5.006

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  6 in total

1.  Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations.

Authors:  Ludovic Chaput; Edithe Selwa; Eddy Elisée; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2018-09-11       Impact factor: 3.686

2.  Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset.

Authors:  Claire Colas; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2014-01-24       Impact factor: 3.686

3.  Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations.

Authors:  Edithe Selwa; Eddy Elisée; Agustin Zavala; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2017-09-02       Impact factor: 3.686

4.  Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets.

Authors:  Edithe Selwa; Virginie Y Martiny; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2016-10-03       Impact factor: 3.686

Review 5.  Blind prediction of HIV integrase binding from the SAMPL4 challenge.

Authors:  David L Mobley; Shuai Liu; Nathan M Lim; Karisa L Wymer; Alexander L Perryman; Stefano Forli; Nanjie Deng; Justin Su; Kim Branson; Arthur J Olson
Journal:  J Comput Aided Mol Des       Date:  2014-03-05       Impact factor: 3.686

6.  Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset.

Authors:  Eddy Elisée; Vytautas Gapsys; Nawel Mele; Ludovic Chaput; Edithe Selwa; Bert L de Groot; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2019-11-01       Impact factor: 3.686

  6 in total

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