AVP‐IC(50)Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC(50))

Qureshi, Abid; Tandon, Himani; Kumar, Manoj

Peptide‐based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC(50) values involves a lot of effort. Therefore, we have developed “AVP‐IC(50)Pred,” a regression‐based algorithm to predict the antiviral activity in terms of IC(50) values (μM). A total of 759 non‐redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino‐acid compositions, binary profile of N8‐C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance‐based classifier, and K‐Star were employed. During 10‐fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP‐IC(50)Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 104: 753–763, 2015.