ACPP: A Web Server for Prediction and Design of Anti-cancer Peptides

Abstract

Cancer is one of the most common diseases, which causes more mortality worldwide. Despite the presence of several therapies against cancer, peptides as therapeutic agents are gaining importance. Experimental studies report that peptides containing apoptotic domain exhibit anticancer activity. Hence in this study, we propose a computational method using support vector machine and protein relatedness measure feature vector, in which provision was made to assess the query protein for the presence of any apoptotic domains or not and then to scan/predict the anti-cancer peptides in the protein. Different datasets, including newly developed positive and negative dataset, AntiCP dataset, and balanced randomly generated peptides were used to validate the proposed method. The validation results on independent dataset suggested (sensitivity = 0.95; specificity = 0.97; MCC = 0.92; and Accuracy = 0.96) that the proposed method outperformed the existing method in predicting anti-cancer peptides. The user friendly webserver includes three different modes (i) Protein scan with apoptotic domain prediction; (ii) Multiple peptide mode; and (iii) Peptide mutation mode for prediction and design of anti-cancer peptides. The server was developed using PERL CGI and freely accessible at http://acpp.bicpu.edu.in/predict.php. The established tool will be useful in investigating and designing potent anti-cancer peptides from the query protein effectively.