Computer-aided Designing of Immunosuppressive Peptides Based on IL-10 Inducing Potential

Overview

Journal Sci Rep

Specialty Science

Date 2017 Feb 18

PMID 28211521

Citations 121

Authors

Gandharva Nagpal

Salman Sadullah Usmani

Sandeep Kumar Dhanda

Harpreet Kaur

Sandeep Singh

Meenu Sharma

Gajendra P S Raghava

Affiliations

Soon will be listed here.

Abstract

In the past, numerous methods have been developed to predict MHC class II binders or T-helper epitopes for designing the epitope-based vaccines against pathogens. In contrast, limited attempts have been made to develop methods for predicting T-helper epitopes/peptides that can induce a specific type of cytokine. This paper describes a method, developed for predicting interleukin-10 (IL-10) inducing peptides, a cytokine responsible for suppressing the immune system. All models were trained and tested on experimentally validated 394 IL-10 inducing and 848 non-inducing peptides. It was observed that certain types of residues and motifs are more frequent in IL-10 inducing peptides than in non-inducing peptides. Based on this analysis, we developed composition-based models using various machine-learning techniques. Random Forest-based model achieved the maximum Matthews's Correlation Coefficient (MCC) value of 0.59 with an accuracy of 81.24% developed using dipeptide composition. In order to facilitate the community, we developed a web server "IL-10pred", standalone packages and a mobile app for designing IL-10 inducing peptides (http://crdd.osdd.net/raghava/IL-10pred/).

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