Dataset Information

Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

ABSTRACT: Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations.

SUBMITTER: Sael L

PROVIDER: S-EPMC3294165 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

Sael Lee L Kihara Daisuke D

Proteins 20120124 4

Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, e ...[more]

PMID: 22275074

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Project description:BACKGROUND: The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. RESULTS: The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA). CONCLUSIONS: The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (http://159.226.149.45/other1/CMASA/CMASA.htm).

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

Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

Publications

Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

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