Literature DB >> 33759124

Negative Image-Based Screening: Rigid Docking Using Cavity Information.

Pekka A Postila1,2, Sami T Kurkinen1,2, Olli T Pentikäinen3,4.   

Abstract

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Keywords:  Cavity detection; Flexible molecular docking; Negative image-based screening (NIB); Rigid molecular docking; Virtual screening

Mesh:

Substances:

Year:  2021        PMID: 33759124     DOI: 10.1007/978-1-0716-1209-5_7

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  52 in total

1.  Comparative evaluation of 11 scoring functions for molecular docking.

Authors:  Renxiao Wang; Yipin Lu; Shaomeng Wang
Journal:  J Med Chem       Date:  2003-06-05       Impact factor: 7.446

2.  Nuclear hormone receptor targeted virtual screening.

Authors:  Matthieu Schapira; Ruben Abagyan; Maxim Totrov
Journal:  J Med Chem       Date:  2003-07-03       Impact factor: 7.446

3.  Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database.

Authors:  Dariusz Plewczynski; Michał Łaźniewski; Rafał Augustyniak; Krzysztof Ginalski
Journal:  J Comput Chem       Date:  2010-09-01       Impact factor: 3.376

4.  A critical assessment of docking programs and scoring functions.

Authors:  Gregory L Warren; C Webster Andrews; Anna-Maria Capelli; Brian Clarke; Judith LaLonde; Millard H Lambert; Mika Lindvall; Neysa Nevins; Simon F Semus; Stefan Senger; Giovanna Tedesco; Ian D Wall; James M Woolven; Catherine E Peishoff; Martha S Head
Journal:  J Med Chem       Date:  2006-10-05       Impact factor: 7.446

Review 5.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

Review 6.  Flexible ligand docking to multiple receptor conformations: a practical alternative.

Authors:  Maxim Totrov; Ruben Abagyan
Journal:  Curr Opin Struct Biol       Date:  2008-02-25       Impact factor: 6.809

Review 7.  Managing protein flexibility in docking and its applications.

Authors:  Chandrika B-Rao; Jyothi Subramanian; Somesh D Sharma
Journal:  Drug Discov Today       Date:  2009-02-03       Impact factor: 7.851

Review 8.  Software for molecular docking: a review.

Authors:  Nataraj S Pagadala; Khajamohiddin Syed; Jack Tuszynski
Journal:  Biophys Rev       Date:  2017-01-16

Review 9.  Molecular docking: a powerful approach for structure-based drug discovery.

Authors:  Xuan-Yu Meng; Hong-Xing Zhang; Mihaly Mezei; Meng Cui
Journal:  Curr Comput Aided Drug Des       Date:  2011-06       Impact factor: 1.606

10.  Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation.

Authors:  Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2009-04-02       Impact factor: 3.686

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