creators_name: Mathur, Deepika
creators_name: Singh, Sandeep
creators_name: Mehta, Ayesha
creators_name: Agrawal, Piyush
creators_name: Raghava, G.P.S.
type: article
datestamp: 2018-08-09 06:26:54
lastmod: 2019-03-20 12:43:37
metadata_visibility: show
title: In silico approaches for predicting the half-life of natural and modified peptides in blood.
ispublished: pub
subjects: QR
note: Open Access
abstract: This paper describes a web server developed for designing therapeutic peptides with desired half-life in blood. In this study, we used 163 natural and 98 modified peptides whose half-life has been determined experimentally in mammalian blood, for developing in silico models. Firstly, models have been developed on 261 peptides containing natural and modified residues, using different chemical descriptors. The best model using 43 PaDEL descriptors got a maximum correlation of 0.692 between the predicted and the actual half-life peptides. Secondly, models were developed on 163 natural peptides using amino acid composition feature of peptides and achieved a maximum correlation of 0.643. Thirdly, models were developed on 163 natural peptides using chemical descriptors and attained a maximum correlation of 0.743 using 45 selected PaDEL descriptors. In order to assist researchers in the prediction and designing of half-life of peptides, the models developed have been integrated into PlifePred web server (http://webs.iiitd.edu.in//raghava/plifepred/).
date: 2018-06-01
date_type: published
publication: PloS one
volume: 13
number: 6
publisher: Public Library of Science
pagerange: e0196829
refereed: TRUE
issn: 1932-6203
official_url: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196829
related_url_url: http://webs.iiitd.edu.in//raghava/plifepred/
related_url_type: org
citation:   Mathur, Deepika and Singh, Sandeep and Mehta, Ayesha and Agrawal, Piyush and Raghava, G.P.S.  (2018) In silico approaches for predicting the half-life of natural and modified peptides in blood.  PloS one, 13 (6).  e0196829.  ISSN 1932-6203