Nagpal, Gandharva and Usmani, Salman Sadullah and Dhanda, Sandeep Kumar and Kaur, Harpreet and Singh, Sandeep and Sharma, Meenu and Raghava, G.P.S. (2017) Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Scientific Reports, 7. p. 42851. ISSN 2045-2322
Full text not available from this repository. (Request a copy)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/).
| Item Type: | Article |
|---|---|
| Additional Information: | Open Access |
| Uncontrolled Keywords: | Bioinformatics |
| Subjects: | Q Science > QR Microbiology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 24 Mar 2018 03:56 |
| Last Modified: | 03 Apr 2018 04:57 |
| URI: | http://crdd.osdd.net/open/id/eprint/1981 |
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