SVM Classification to Predict Two Stranded Anti-parallel Coiled Coils based on Protein Sequence Data

Abstract

Coiled coils is an important 3-D protein structure with two or more stranded alpha-helical motif wounded around to form a “knobs-into-holes” structure. In this paper we propose an SVM classification approach to predict the two stranded anti-parallel coiled coils structure based on the primary amino acid sequence. The training dataset for the machine learning are collected from SOCKET database which is a SOCKET algorithm predicted coiled coils database. Total 41 sequences of at least two heptad repeats of the two stranded anti-parallel coiled coils motif are extracted from 12 proteins as the positive datasets. Total 37 of non coiled coils sequences and two stranded parallel coiled coils motif are extracted from 5 proteins as negative datasets. The normalized positional weight matrix on each heptad register a, b, c, d, e, f and g is from SOCKET database and is used to generate the positional weight on each entry. We performed SVM classification using the cross-validated datasets as training and testing groups. Our result shows 73% accuracy on the prediction of two stranded anti-parallel coiled coils based on the cross-validated data. The result suggests a useful approach of using SVM to classify the two stranded anti-parallel coiled coils based on the primary amino acid sequence.