Protein structural class prediction using support vector machine

Shafiullah, GM.ORCID: 0000-0002-2211-184X and Al-Mamun, H.A. (2010) Protein structural class prediction using support vector machine. In: International Conference on Electrical and Computer Engineering (ICECE) 2010, 18 - 20 Dec. 2010, Dhaka, Bangladesh

Abstract

Protein structural class prediction can play a vital role in protein 3-D structure prediction by reducing the search space of 3-D structure prediction algorithms. In this paper we used support vector machine to predict protein structural class solely based of its amino acid sequences, i.e. mainly α, mainly β, α- β and fss from CATH protein structure database; all-α, all-β, α/β, α+β from SCOP protein structure database. Four different datasets were used in this paper among them two were constructed using a unique way called Representative Protein Extraction method. During the training phase for the binary classification 99.91% accuracy was achieved for fss vs. others. Also during the testing phase for SCOP database the overall prediction accuracy was 97.14% whereas for CATH database it was 96%. The results obtained in this study are quite encouraging, indicating that it can be used as a complimentary method for protein class prediction to many other existing methods.

Item Type:	Conference Paper
URI:	http://researchrepository.murdoch.edu.au/id/eprint/31866

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