Protemot: prediction of protein binding sites with automatically extracted geometrical templates

Abstract

Geometrical analysis of protein tertiary substructures has been an effective approach employed to predict protein binding sites. This article presents the Protemot web server that carries out prediction of protein binding sites based on the structural templates automatically extracted from the crystal structures of protein–ligand complexes in the PDB (Protein Data Bank). The automatic extraction mechanism is essential for creating and maintaining a comprehensive template library that timely accommodates to the new release of PDB as the number of entries continues to grow rapidly. The design of Protemot is also distinctive by the mechanism employed to expedite the analysis process that matches the tertiary substructures on the contour of the query protein with the templates in the library. This expediting mechanism is essential for providing reasonable response time to the user as the number of entries in the template library continues to grow rapidly due to rapid growth of the number of entries in PDB. This article also reports the experiments conducted to evaluate the prediction power delivered by the Protemot web server. Experimental results show that Protemot can deliver a superior prediction power than a web server based on a manually curated template library with insufficient quantity of entries. Availability: .

Computational Tools and Databases for the Study and Characterization of Protein Interactions

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A Survey on Cross Language Information Retrieval

“…A number of resources available on Web provide the public and healthcare professionals with the most up-to-date findings in medical research, such as PubMed [68] and MedlinePlus [69].…”

Mining Conserved Structures of Enzymes from Functional Hierarchical Classification

“…In the equation, A means "in lib" correct, B means "in lib" incorrect, C means "in lib" no prediction, D means "out lib" incorrect, and E means "out lib" no prediction. In Table 2 (a), we use mined conserved patterns as prediction patterns to predict EC labels and the prediction result shows 83.45% Confidence and 67.02% Accuracy in the 1,000 protein chains randomly selected from 13 of prediction EC label(s), predicted EC label may be more than one.…”

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