Portal de Periódicos da CAPES

Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential

2017; Nature Portfolio; Volume: 7; Issue: 1 Linguagem: Inglês

10.1038/srep42851

2045-2322

Gandharva Nagpal, Salman Sadullah Usmani, Sandeep Kumar Dhanda, Harpreet Kaur, Sandeep Singh, Meenu Sharma, Gajendra P. S. Raghava,

Monoclonal and Polyclonal Antibodies Research

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/).