SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence

Abstract

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Figure 1

SVMProt web page.

Figure 2

Example of the SVMProt output returned to the user.

Figure 3

Hypothetical sequence for illustration of derivation of the feature vector of a protein.

Figure 4

Statistical relationship between the R-value and P-value (probability of correct classification) derived from analysis of 9932 positive and 45 999 negative samples of proteins.