Prediction of Myotoxic and Neurotoxic Activities in Phospholipases A2 from Primary Sequence Analysis

We developed a methodology to predict myotoxicity and neurotoxicity of proteins of the family of Phospholipases A2 (PLA2) from sequence data. Combining two bioinformatics tools, MEME and HMMER, it was possible to detect conserved motifs and represent them as Hidden Markov Models (HMMs). In ten-fold cross validation testing we have determined the efficacy of each motif on prediction of PLA2 function. We selected motifs whose efficacy in predict function were above 60 % at the Minimum Error Point (MEP), the score in which there are fewest both false positives and false negatives. Combining HMMs of the best motifs for each function, we have achieved a mean efficacy of 98 ± 4 % on prediction of myotoxic function and 77.4 ± 4.8% on prediction of neurotoxicity. We have used the results of this work to build a web tool (available at www.cbiot.ufrgs.br/bioinfo/phospholipase) to classify PLA2s of unknown function regarding myotoxic or neurotoxic activity. IV BSB 34 Favor ver os Anais do Simpósio em Springer Verlag, Lecture Notes in Bioinformatics (LNBI número 3594) para este trabalho. Please see the Symposium Proceedings in Lecture Notes in Bioinformatics (LNBI nr. 3594), Springer Verlag, for this paper.