Transmembrane segments prediction with support vector machine based on high performance encoding schemes

A new prediction scheme of transmembrane segments (TM) was developed based on the support vector machine (SVM). To apply this SVM for prediction more efficiently, three optimization processes were performed: encoding scheme, sliding window size and parameter optimization. From the encoding scheme optimization, position-specific scoring matrix (PSSM) encoding scheme is proved to be the most informative one and the prediction accuracy (Q₂) with this scheme attained up to 92%. Based on the performance comparison with previous studies, this PSSM encoding scheme demonstrates the highest prediction accuracy among the common prediction methods, and the accuracy improvement is more than 13%. To verify this scheme, the blind test was done with E.coli SecE and E.coli SecY transmembrane proteins, and the result shows a decent match with the SwissProt database information and the TopPred results. However, another blind test result with five SecA proteins leaves room for discussion since it shows about 8-9 residues long TM segments for all five proteins.