Literature DB >> 15807511

ProPose: steered virtual screening by simultaneous protein-ligand docking and ligand-ligand alignment.

Markus H J Seifert1.   

Abstract

The 'model-free' screening engine ProPose implements a general method for performing simultaneous protein-ligand docking, ligand-ligand alignment, pharmacophore queries-and combinations thereof-in order to incorporate a priori information into screening protocols. In this manuscript we describe a case study on herpes simplex virus thymidine kinase, an important antiviral drug target, where we evaluate different approaches for handling a specific type of a priori information, i.e., multiple target structures. We demonstrate that a simultaneous alignment on two target structures--in conjunction with logic operations on interactions and docking constraints derived from protein structure--is an effective means of (i) improving the enrichment of chemical substructures that are compatible with the a priori known ligands, (ii) ensuring the steric fit into the target protein, and (iii) handling target flexibility. The combination of ligand- and receptor-based methods steers the virtual screening by ranking molecules according to the similarity of their interaction pattern with known ligands, thereby--to some extent--outweighing the deficiencies of simple scoring functions often used in initial virtual screening.

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Year:  2005        PMID: 15807511     DOI: 10.1021/ci0496393

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


  6 in total

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Journal:  Antimicrob Agents Chemother       Date:  2006-02       Impact factor: 5.191

Review 2.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go.

Authors:  N Moitessier; P Englebienne; D Lee; J Lawandi; C R Corbeil
Journal:  Br J Pharmacol       Date:  2007-11-26       Impact factor: 8.739

3.  Robust optimization of scoring functions for a target class.

Authors:  Markus H J Seifert
Journal:  J Comput Aided Mol Des       Date:  2009-05-27       Impact factor: 3.686

Review 4.  Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Authors:  Rohan Gupta; Devesh Srivastava; Mehar Sahu; Swati Tiwari; Rashmi K Ambasta; Pravir Kumar
Journal:  Mol Divers       Date:  2021-04-12       Impact factor: 3.364

Review 5.  Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview.

Authors:  Romina A Guedes; Patrícia Serra; Jorge A R Salvador; Rita C Guedes
Journal:  Molecules       Date:  2016-07-16       Impact factor: 4.411

6.  Sanjeevini: a freely accessible web-server for target directed lead molecule discovery.

Authors:  B Jayaram; Tanya Singh; Goutam Mukherjee; Abhinav Mathur; Shashank Shekhar; Vandana Shekhar
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

  6 in total

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