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

Mark James Agostino, Ricardo Mancera, Paul Allen Ramsland, Elizabeth Yuriev

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)80 - 90
Number of pages11
JournalJournal of Molecular Graphics and Modelling
Volume40
DOIs
Publication statusPublished - 2013

Cite this

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title = "AutoMap: A tool for analyzing protein-ligand recognition using multiple ligand binding modes",
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.",
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AutoMap: A tool for analyzing protein-ligand recognition using multiple ligand binding modes. / Agostino, Mark James; Mancera, Ricardo; Ramsland, Paul Allen; Yuriev, Elizabeth.

In: Journal of Molecular Graphics and Modelling, Vol. 40, 2013, p. 80 - 90.

Research output: Contribution to journalArticleResearchpeer-review

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