Anti-inflammatory epitope detection software tools | Immune system data analysis
The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer’s disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics.
A comprehensive web tool for analysis and prediction of proinflamatory response of any peptide/protein antigen. ProInflam is a machine learning-based prediction tool for proinflammatory epitopes. The computational identification of proinflammatory antigenic candidates before going for expensive and time-consuming experiments would be of great help to the scientific community.
Predicts the anti-inflammatory nature of peptides and proteins. AntiInflam is a machine learning based classification method that provides tools for (i) predicting the anti-inflammatory nature of small length peptides, (ii) determining the antigenic regions in a full-length protein, (iii) mapping the experimentally validated anti-inflammatory antigenic regions on a query protein and (iv) identifying the similar regions in the query peptide/protein and experimentally validated anti-inflammatory epitopes (AIEs).
Enables users to perform sequence-based prediction of anti-inflammatory peptides (AIP) using random forest. AIPpred is able to determine, for a given peptide, its class and probability values. This method utilizes a random forest-based prediction model, which was developed by utilizing optimal dipeptide composition (DPC) as an input feature.