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primary topic: In silico models for designing and discovering novel anticancer peptides.

In silico models for designing and discovering novel anticancer peptides.
type: AcademicArticle
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/).
date: 2013-10-18
volume: 3
is part of: Scientific reports
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
subject: QR Microbiology

Nature Publishing Group
type: Organization

Scientific reports
type: Collection
issn: 20452322

Atul Tyagi
type: Person
given name: Atul
family name: Tyagi

Pallavi Kapoor
type: Person
given name: Pallavi
family name: Kapoor

Ankur Gautam
type: Person
given name: Ankur
family name: Gautam

Rahul Kumar
type: Person
given name: Rahul
family name: Kumar

G.P.S. Raghava
type: Person
given name: G.P.S.
family name: Raghava

Kumardeep Chaudhary
type: Person
given name: Kumardeep
family name: Chaudhary

HTML Summary of #2457 In silico models for designing and discovering novel anticancer peptides.
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primary topic: In silico models for designing and discovering novel anticancer peptides.

QR Microbiology
type: Concept