ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.
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.



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 %.



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/ .
Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India.
Sudheer
S
Gupta
Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India.
Midhun K
MK
Madhu
Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India.
Ashok K
AK
Sharma
Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India. vineetks@iiserb.ac.in.
Vineet K
VK
Sharma
D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000941:Antigens / Q000276:immunology*; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry; D018604:Epitope Mapping; D000939:Epitopes / Q000737:chemistry / Q000276:immunology; D006801:Humans; D018836:Inflammation Mediators / Q000276:immunology*; D020407:Internet; D000069550:Machine Learning; D010455:Peptides / Q000737:chemistry / Q000276:immunology*; D011506:Proteins / Q000737:chemistry / Q000276:immunology*; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software
D000941:Antigens; D004151:Dipeptides; D000939:Epitopes; D018836:Inflammation Mediators; D010455:Peptides; D011506:Proteins
Antigens; Machine-learning; Prediction; Proinflammatory; Vaccine