Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential
IR@IMTECH: CSIR-Institute of Microbial Technology, Chandigarh
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| Title |
Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential
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| Creator |
Nagpal, Gandharva
Usmani, Salman Sadullah Dhanda, Sandeep Kumar Kaur, Harpreet Singh, Sandeep Sharma, Meenu Raghava, G.P.S. |
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| Subject |
QR Microbiology
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| Description |
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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| Publisher |
Nature Publishing Group
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| Date |
2017
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| Type |
Article
PeerReviewed |
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| Relation |
http://dx.doi.org/10.1038/srep42851
http://crdd.osdd.net/open/1981/ |
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| Identifier |
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
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