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Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble.

ABSTRACT: BACKGROUND: Modelling the ligand binding site of a protein is an important component of understanding protein-ligand interactions and is being actively studied. Even if the side chains are restricted to rotamers, a set of commonly-observed low-energy conformations, the exhaustive combinatorial search of ligand binding site conformers is known as NP-hard. Here we propose a new method, ROTAIMAGE, for modelling the plausible conformers for the ligand binding site given a fixed backbone structure. RESULTS: ROTAIMAGE includes a procedure of selecting ligand binding site residues, exhaustively searching rotameric conformers, clustering them by dissimilarities in pocket shape, and suggesting a representative conformer per cluster. Prior to the clustering, the list of conformers generated by exhaustive search can be reduced by pruning the conformers that have near identical pocket shapes, which is done using simple bit operations. We tested our approach by modelling the active-site inhibitor binding pockets of matrix metalloproteinase-1 and -13. For both cases, analyzing the conformers based on their pocket shapes substantially reduced the 'computational complexity' (10 to 190 fold). The subsequent clustering revealed that the pocket shapes of both proteins could be grouped into approximately 10 distinct clusters. At this level of clustering, the conformational space spanned by the known crystal structures was well covered. Heatmap analysis identified a few bit blocks that combinatorially dictated the clustering pattern. Using this analytical approach, we demonstrated that each of the bit blocks was associated with a specific pocket residue. Identification of residues that influenced the shape of the pocket is an interesting feature unique to the ROTAIMAGE algorithm. CONCLUSIONS: ROTAIMAGE is a novel algorithm that was efficient in exploring the conformational space of the ligand binding site. Its ability to identify 'key' pocket residues also provides further insight into conformational flexibility with specific implications for protein-ligand interactions.

SUBMITTER: Sung E

PROVIDER: S-EPMC3098062 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble.

Sung Edon E Kim Sangsoo S Shin Whanchul W

BMC bioinformatics 20100518

<h4>Background</h4>Modelling the ligand binding site of a protein is an important component of understanding protein-ligand interactions and is being actively studied. Even if the side chains are restricted to rotamers, a set of commonly-observed low-energy conformations, the exhaustive combinatorial search of ligand binding site conformers is known as NP-hard. Here we propose a new method, ROTAIMAGE, for modelling the plausible conformers for the ligand binding site given a fixed backbone struc ...[more]

PMID: 20478076

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Project description:A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Dataset Information

Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble.

Publications

Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble.

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