Identification of catalytic residues from protein structure using support vector machine with sequence and structural features |
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Authors: | Pugalenthi Ganesan Kumar K Krishna Suganthan P N Gangal Rajeev |
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Affiliation: | a School of Electrical and Electronic Engineering, Nanyang Technological University, Block S2, 50 Nanyang Avenue, Singapore 639798, Singapore b Insilico Consulting, 402 City Centre, 39/2 Erandwane, Karve Road, Pune, Maharashtra, India |
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Abstract: | Identification of catalytic residues can provide valuable insights into protein function. With the increasing number of protein 3D structures having been solved by X-ray crystallography and NMR techniques, it is highly desirable to develop an efficient method to identify their catalytic sites. In this paper, we present an SVM method for the identification of catalytic residues using sequence and structural features. The algorithm was applied to the 2096 catalytic residues derived from Catalytic Site Atlas database. We obtained overall prediction accuracy of 88.6% from 10-fold cross validation and 95.76% from resubstitution test. Testing on the 254 catalytic residues shows our method can correctly predict all 254 residues. This result suggests the usefulness of our approach for facilitating the identification of catalytic residues from protein structures. |
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Keywords: | Active site Protein function prediction Functional residues Sequence-structural features Spatial neighbors |
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