Abstract: We have developed a method NTXpred for predicting neurotoxins and
classifying them based on their function and origin. The dataset used in this
study consists of 582 non-redundant, experimentally annotated neurotoxins
obtained from Swiss-Prot. A number of modules have been developed for
predicting neurotoxins using residue composition based on feed-forwarded neural
network (FNN), recurrent neural network (RNN), support vector machine (SVM) and
achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In
addition, SVM modules have been developed for classifying neurotoxins based on
their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and
chordate) using amino acid composition and dipeptide composition and achieved
maximum overall accuracy of 78.94% and 88.07% respectively. The overall
accuracy increased to 92.10%, when the evolutionary information obtained
from PSI-BLAST was combined with SVM module of source classification. We have
also developed SVM modules for classifying neurotoxins based on functions using
amino acid, dipeptide composition and achieved overall accuracy of 83.11%,
91.10% respectively. The overall accuracy of function classification
improved to 95.11%, when PSI-BLAST output was combined with SVM module. All
the modules developed in this study were evaluated using five-fold
cross-validation technique. The NTXpred is available at
www.imtech.res.in/raghava/ntxpred/ and mirror site at
http://bioinformatics.uams.edu/mirror/ntxpred.
Keywords: NTXpred, prediction of neurotoxins, Webserver, blockers of ion channels
