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PubMed:27301453 JSONTXT

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Inflammaging

Id Subject Object Predicate Lexical cue
T1 0-99 Sentence denotes ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.
T2 100-111 Sentence denotes BACKGROUND:
T3 112-294 Sentence denotes 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.
T4 295-434 Sentence denotes It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS.
T5 435-672 Sentence denotes 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.
T6 673-807 Sentence denotes In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.
T7 808-816 Sentence denotes RESULTS:
T8 817-943 Sentence denotes A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database.
T9 944-1079 Sentence denotes 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.
T10 1080-1285 Sentence denotes 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.
T11 1286-1405 Sentence denotes The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.
T12 1406-1417 Sentence denotes CONCLUSION:
T13 1418-1578 Sentence denotes The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes.
T14 1579-1732 Sentence denotes This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations.
T15 1733-1969 Sentence denotes 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/ .
T1 0-99 Sentence denotes ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.
T2 100-111 Sentence denotes BACKGROUND:
T3 112-294 Sentence denotes 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.
T4 295-434 Sentence denotes It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS.
T5 435-672 Sentence denotes 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.
T6 673-807 Sentence denotes In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.
T7 808-816 Sentence denotes RESULTS:
T8 817-943 Sentence denotes A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database.
T9 944-1079 Sentence denotes 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.
T10 1080-1285 Sentence denotes 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.
T11 1286-1405 Sentence denotes The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.
T12 1406-1417 Sentence denotes CONCLUSION:
T13 1418-1578 Sentence denotes The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes.
T14 1579-1732 Sentence denotes This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations.
T15 1733-1969 Sentence denotes 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/ .