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Recognizing protein substructure similarity using segmental threading.

ABSTRACT: Protein template identification is essential to protein structure and function predictions. However, conventional whole-chain threading approaches often fail to recognize conserved substructure motifs when the target and templates do not share the same fold. We developed a new approach, SEGMER, for identifying protein substructure similarities by segmental threading. The target sequence is split into segments of two to four consecutive or nonconsecutive secondary structural elements, which are then threaded through PDB to identify appropriate substructure motifs. SEGMER is tested on 144 nonredundant hard proteins. When combined with whole-chain threading, the TM-score of alignments and accuracy of spatial restraints of SEGMER increase by 16% and 25%, respectively, compared with that by the whole-chain threading methods only. When tested on 12 free modeling targets from CASP8, SEGMER increases the TM-score and contact accuracy by 28% and 48%, respectively. This significant improvement should have important impact on protein structure modeling and functional inference.

SUBMITTER: Wu S

PROVIDER: S-EPMC2908588 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Recognizing protein substructure similarity using segmental threading.

Wu Sitao S Zhang Yang Y

Structure (London, England : 1993) 20100701 7

Protein template identification is essential to protein structure and function predictions. However, conventional whole-chain threading approaches often fail to recognize conserved substructure motifs when the target and templates do not share the same fold. We developed a new approach, SEGMER, for identifying protein substructure similarities by segmental threading. The target sequence is split into segments of two to four consecutive or nonconsecutive secondary structural elements, which are t ...[more]

PMID: 20637422

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Project description:BackgroundVirtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.ResultsWe introduce a new approach (LigCSRre) to the 3D ligand similarity search of drug candidates. It combines a 3D maximum common substructure search algorithm independent on atom order with a tunable description of atomic compatibilities to prune the search and increase its physico-chemical relevance. We show, on 47 experimentally validated active compounds across five protein targets having different specificities, that for single compound search, the approach is able to recover on average 52% of the co-actives in the top 1% of the ranked list which is better than gold standards of the field. Moreover, the combination of several runs on a single protein target using different query active compounds shows a remarkable improvement in enrichment. Such Results demonstrate LigCSRre as a valuable tool for ligand-based screening.ConclusionLigCSRre constitutes a new efficient and generic approach to the 3D similarity screening of small compounds, whose flexible design opens the door to many enhancements. The program is freely available to the academics for non-profit research at: http://bioserv.rpbs.univ-paris-diderot.fr/LigCSRre.html.

Project description:Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce PrISE, a family of local structural similarity-based computational methods for predicting protein-protein interface residues.We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The PrISE family of interface prediction methods uses a representation of structural elements that captures the atomic composition and accessible surface area of the residues that make up each structural element. Each of the members of the PrISE methods identifies for each structural element in the query protein, a collection of similar structural elements in its repository of structural elements and weights them according to their similarity with the structural element of the query protein. PrISEL relies on the similarity between structural elements (i.e. local structural similarity). PrISEG relies on the similarity between protein surfaces (i.e. general structural similarity). PrISEC, combines local structural similarity and general structural similarity to predict interface residues. These predictors label the central residue of a structural element in a query protein as an interface residue if a weighted majority of the structural elements that are similar to it are interface residues, and as a non-interface residue otherwise. The results of our experiments using three representative benchmark datasets show that the PrISEC outperforms PrISEL and PrISEG; and that PrISEC is highly competitive with state-of-the-art structure-based methods for predicting protein-protein interface residues. Our comparison of PrISEC with PredUs, a recently developed method for predicting interface residues of a query protein based on the known interface residues of its (global) structural homologs, shows that performance superior or comparable to that of PredUs can be obtained using only local surface structural similarity. PrISEC is available as a Web server at http://prise.cs.iastate.edu/Local surface structural similarity based methods offer a simple, efficient, and effective approach to predict protein-protein interface residues.

Dataset Information

Recognizing protein substructure similarity using segmental threading.

Publications

Recognizing protein substructure similarity using segmental threading.

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