Signal peptide and cleavage site predictions are very important fields in bioinformatics because  of its contributions in modern cell biological research, molecular mechanisms of diseases, and  drug discoveries. In this paper, we present the results in signal peptide and cleavage site  predictions using the weight matrix approach utilizing genetic algorithm (GA)-optimized  position weight matrix (PWM) profiles each for eukaryotic, gram-negative and gram-positive  prokaryotic organisms. The consistency tests yielded overall performance ratings of roughly  97% for signal peptide prediction while approximately 77% for cleavage site prediction at  position 0. Cross-validation results showed that the overall performances of using the GA optimized profile matrices in predicting the presence of signal peptides were as accurate as  around 95%. However, for cleavage site prediction, the three optimized profile matrices  produced overall accuracy of about 72%-74% in predicting the actual cleavage site location. For  protein sequences belonging to the prokaryote organism that are not labeled as gram-negative  or gram-positive, predicting for the correct cleavage site location by the GA-optimized PWM  profile of the former consistently resulted to higher success ratings. A comparison between the  latest existing profile matrices (used in signal peptide and cleavage site predictions) showed  only a slight improvement in the overall performance. Although the improvement is minimal, it  makes a lot of difference when analyzing large datasets or genomic protein sequences.

Proceedings of the IEEE Region 10  International Conference (IEEE TENCON 2012). DOI: 10.1109/TENCON.2012.6412173