Format

Send to

Choose Destination
J Mol Graph Model. 2013 Mar;40:80-90. doi: 10.1016/j.jmgm.2013.01.001. Epub 2013 Jan 8.

AutoMap: a tool for analyzing protein-ligand recognition using multiple ligand binding modes.

Author information

1
Western Australian Biomedical Research Institute, Curtin Health Innovation Research Institute, School of Biomedical Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia. mark.agostino@curtin.edu.au

Abstract

Prediction of the protein residues most likely to be involved in ligand recognition is of substantial value in structure-based drug design. Considering multiple ligand binding modes is of potential relevance to studying ligand recognition, but is generally ignored by currently available techniques. We have previously presented the site mapping technique, which considers multiple ligand binding modes in its analysis of protein-ligand recognition. AutoMap is a partially automated implementation of our previously developed site mapping procedure. It consists of a series of Perl scripts that utilize the output of molecular docking to generate "site maps" of a protein binding site. AutoMap determines the hydrogen bonding and van der Waals interactions taking place between a target protein and each pose of a ligand ensemble. It tallies these interactions according to the protein residues with which they occur, then normalizes the tallies and maps these to the surface of the protein. The residues involved in interactions are selected according to specific cutoffs. The procedure has been demonstrated to perform well in studying carbohydrate-protein and peptide-antibody recognition. An automated procedure to optimize cutoff selection is demonstrated to rapidly identify the appropriate cutoffs for these previously studied systems. The prediction of key ligand binding residues is compared between AutoMap using automatically optimized cutoffs, AutoMap using a previously selected cutoff, the top ranked pose from docking and the predictions supplied by FTMap. AutoMap using automatically optimized cutoffs is demonstrated to provide improved predictions, compared to other methods, in a set of immunologically relevant test cases. The automated implementation of the site mapping technique provides the opportunity for rapid optimization and deployment of the technique for investigating a broad range of protein-ligand systems.

PMID:
23376613
DOI:
10.1016/j.jmgm.2013.01.001
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center