Literature DB >> 17910060

Automated prediction of ligand-binding sites in proteins.

Rodney Harris1, Arthur J Olson, David S Goodsell.   

Abstract

We present a method, termed AutoLigand, for the prediction of ligand-binding sites in proteins of known structure. The method searches the space surrounding the protein and finds the contiguous envelope with the specified volume of atoms, which has the largest possible interaction energy with the protein. It uses a full atomic representation, with atom types for carbon, hydrogen, oxygen, nitrogen and sulfur (and others, if desired), and is designed to minimize the need for artificial geometry. Testing on a set of 187 diverse protein-ligand complexes has shown that the method is successful in predicting the location and approximate volume of the binding site in 73% of cases. Additional testing was performed on a set of 96 protein-ligand complexes with crystallographic structures of apo and holo forms, and AutoLigand was able to predict the binding site in 80% of the apo structures. 2007 Wiley-Liss, Inc.

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Year:  2008        PMID: 17910060     DOI: 10.1002/prot.21645

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  39 in total

1.  Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library.

Authors:  Yoshifumi Fukunishi; Haruki Nakamura
Journal:  Protein Sci       Date:  2011-01       Impact factor: 6.725

2.  Drug search for leishmaniasis: a virtual screening approach by grid computing.

Authors:  Rodrigo Ochoa; Stanley J Watowich; Andrés Flórez; Carol V Mesa; Sara M Robledo; Carlos Muskus
Journal:  J Comput Aided Mol Des       Date:  2016-07-20       Impact factor: 3.686

3.  Lessons for fragment library design: analysis of output from multiple screening campaigns.

Authors:  I-Jen Chen; Roderick E Hubbard
Journal:  J Comput Aided Mol Des       Date:  2009-06-03       Impact factor: 3.686

4.  Comparative surface geometry of the protein kinase family.

Authors:  Elaine E Thompson; Alexandr P Kornev; Natarajan Kannan; Choel Kim; Lynn F Ten Eyck; Susan S Taylor
Journal:  Protein Sci       Date:  2009-10       Impact factor: 6.725

5.  Identification of ligands that target the HCV-E2 binding site on CD81.

Authors:  Reem Al Olaby; Hassan M Azzazy; Rodney Harris; Brett Chromy; Jost Vielmetter; Rod Balhorn
Journal:  J Comput Aided Mol Des       Date:  2013-04-24       Impact factor: 3.686

6.  Anacardic acid inhibits the catalytic activity of matrix metalloproteinase-2 and matrix metalloproteinase-9.

Authors:  Athira Omanakuttan; Jyotsna Nambiar; Rodney M Harris; Chinchu Bose; Nanjan Pandurangan; Rebu K Varghese; Geetha B Kumar; John A Tainer; Asoke Banerji; J Jefferson P Perry; Bipin G Nair
Journal:  Mol Pharmacol       Date:  2012-06-28       Impact factor: 4.436

7.  AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms.

Authors:  Pradeep Anand Ravindranath; Michel F Sanner
Journal:  Bioinformatics       Date:  2016-06-26       Impact factor: 6.937

8.  Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey.

Authors:  Tiago Simões; Daniel Lopes; Sérgio Dias; Francisco Fernandes; João Pereira; Joaquim Jorge; Chandrajit Bajaj; Abel Gomes
Journal:  Comput Graph Forum       Date:  2017-06-01       Impact factor: 2.078

9.  Protein pockets: inventory, shape, and comparison.

Authors:  Ryan G Coleman; Kim A Sharp
Journal:  J Chem Inf Model       Date:  2010-04-26       Impact factor: 4.956

10.  Predicting small ligand binding sites in proteins using backbone structure.

Authors:  Andrew J Bordner
Journal:  Bioinformatics       Date:  2008-10-21       Impact factor: 6.937

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