Work

In Silico Models for Designing and Discovering Novel Anticancer Peptides

Year: 2013

Type: article

Abstract: Use of therapeutic peptides in cancer therapy has been receiving considerable attention in the recent years. Present study describes the development of computational models for predicting and discovering novel anticancer peptides. Preliminary analysis revealed that Cys, Gly, Ile, Lys, and Trp are dominated at various positions in anticancer peptides. Support vector machine models were developed using amino acid composition and binary profiles as input features on main dataset that contains experimentally validated anticancer peptides and random peptides derived from SwissProt database. In addition, models were developed on alternate dataset that contains antimicrobial peptides instead of random peptides. Binary profiles-based model achieved maximum accuracy 91.44% with MCC 0.83. We have developed a webserver, which would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides (http://crdd.osdd.net/raghava/anticp/).

Source: Scientific Reports

DOI: 10.1038/srep02984

Authors: Atul Tyagi, Pallavi Kapoor, Rahul Kumar, Kumardeep Chaudhary, Ankur Gautam, G. P. S. Raghava

Institutions: Institute of Microbial Technology, Council of Scientific and Industrial Research

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690