PubMed:27301453 JSONTXT

{"project":"Inflammaging","denotations":[{"id":"T1","span":{"begin":0,"end":99},"obj":"Sentence"},{"id":"T2","span":{"begin":100,"end":111},"obj":"Sentence"},{"id":"T3","span":{"begin":112,"end":294},"obj":"Sentence"},{"id":"T4","span":{"begin":295,"end":434},"obj":"Sentence"},{"id":"T5","span":{"begin":435,"end":672},"obj":"Sentence"},{"id":"T6","span":{"begin":673,"end":807},"obj":"Sentence"},{"id":"T7","span":{"begin":808,"end":816},"obj":"Sentence"},{"id":"T8","span":{"begin":817,"end":943},"obj":"Sentence"},{"id":"T9","span":{"begin":944,"end":1079},"obj":"Sentence"},{"id":"T10","span":{"begin":1080,"end":1285},"obj":"Sentence"},{"id":"T11","span":{"begin":1286,"end":1405},"obj":"Sentence"},{"id":"T12","span":{"begin":1406,"end":1417},"obj":"Sentence"},{"id":"T13","span":{"begin":1418,"end":1578},"obj":"Sentence"},{"id":"T14","span":{"begin":1579,"end":1732},"obj":"Sentence"},{"id":"T15","span":{"begin":1733,"end":1969},"obj":"Sentence"},{"id":"T1","span":{"begin":0,"end":99},"obj":"Sentence"},{"id":"T2","span":{"begin":100,"end":111},"obj":"Sentence"},{"id":"T3","span":{"begin":112,"end":294},"obj":"Sentence"},{"id":"T4","span":{"begin":295,"end":434},"obj":"Sentence"},{"id":"T5","span":{"begin":435,"end":672},"obj":"Sentence"},{"id":"T6","span":{"begin":673,"end":807},"obj":"Sentence"},{"id":"T7","span":{"begin":808,"end":816},"obj":"Sentence"},{"id":"T8","span":{"begin":817,"end":943},"obj":"Sentence"},{"id":"T9","span":{"begin":944,"end":1079},"obj":"Sentence"},{"id":"T10","span":{"begin":1080,"end":1285},"obj":"Sentence"},{"id":"T11","span":{"begin":1286,"end":1405},"obj":"Sentence"},{"id":"T12","span":{"begin":1406,"end":1417},"obj":"Sentence"},{"id":"T13","span":{"begin":1418,"end":1578},"obj":"Sentence"},{"id":"T14","span":{"begin":1579,"end":1732},"obj":"Sentence"},{"id":"T15","span":{"begin":1733,"end":1969},"obj":"Sentence"}],"text":"ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.\nBACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.\nRESULTS: A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.\nCONCLUSION: The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ ."}