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Generation of a flexible loop structural ensemble and its application to induced-fit structural changes following ligand binding.

ABSTRACT: Molecular recognition is often mediated by flexible loops that have widely fluctuating structures and are sometimes disordered, but that form particular complex structures following ligand binding. In fact, many loop structures found in the PDB database are too flexible to be determined precisely. A new loop modeling method was therefore developed using force-biased multicanonical molecular dynamics with the implicit solvent model to generate an ensemble of putative loop structures with low free energy values. The method was then used to create ensembles for several flexible loops that were compared with the corresponding NMR and X-ray structures. The induced-fit structural change of dihydrofolate reductase (DHFR) was also predicted from a structural ensemble of ligand-free M20 loop conformations and successive docking simulations.

SUBMITTER: Watanabe YS

PROVIDER: S-EPMC5036648 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Generation of a flexible loop structural ensemble and its application to induced-fit structural changes following ligand binding.

Watanabe Yukihisa S YS Fukunishi Yoshifumi Y Nakamura Haruki H

Biophysics (Nagoya-shi, Japan) 20060131

Molecular recognition is often mediated by flexible loops that have widely fluctuating structures and are sometimes disordered, but that form particular complex structures following ligand binding. In fact, many loop structures found in the PDB database are too flexible to be determined precisely. A new loop modeling method was therefore developed using force-biased multicanonical molecular dynamics with the implicit solvent model to generate an ensemble of putative loop structures with low free ...[more]

PMID: 27857555

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Project description:BackgroundModelling 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.ResultsROTAIMAGE 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.ConclusionsROTAIMAGE 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.

Project description:Motivation:Sequence-order independent structural comparison, also called structural alignment, of small ligand molecules is often needed for computer-aided virtual drug screening. Although many ligand structure alignment programs are proposed, most of them build the alignments based on rigid-body shape comparison which cannot provide atom-specific alignment information nor allow structural variation; both abilities are critical to efficient high-throughput virtual screening. Results:We propose a novel ligand comparison algorithm, LS-align, to generate fast and accurate atom-level structural alignments of ligand molecules, through an iterative heuristic search of the target function that combines inter-atom distance with mass and chemical bond comparisons. LS-align contains two modules of Rigid-LS-align and Flexi-LS-align, designed for rigid-body and flexible alignments, respectively, where a ligand-size independent, statistics-based scoring function is developed to evaluate the similarity of ligand molecules relative to random ligand pairs. Large-scale benchmark tests are performed on prioritizing chemical ligands of 102 protein targets involving 1 415 871 candidate compounds from the DUD-E (Database of Useful Decoys: Enhanced) database, where LS-align achieves an average enrichment factor (EF) of 22.0 at the 1% cutoff and the AUC score of 0.75, which are significantly higher than other state-of-the-art methods. Detailed data analyses show that the advanced performance is mainly attributed to the design of the target function that combines structural and chemical information to enhance the sensitivity of recognizing subtle difference of ligand molecules and the introduces of structural flexibility that help capture the conformational changes induced by the ligand-receptor binding interactions. These data demonstrate a new avenue to improve the virtual screening efficiency through the development of sensitive ligand structural alignments. Availability and implementation:http://zhanglab.ccmb.med.umich.edu/LS-align/. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Generation of a flexible loop structural ensemble and its application to induced-fit structural changes following ligand binding.

Publications

Generation of a flexible loop structural ensemble and its application to induced-fit structural changes following ligand binding.

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