PRRpred: Prediction of pattern recognition receptors

Cite: Kaur,D. et al. (2020) A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information. Front. Immunol., 11, 71.

PRRpred: Prediction of pattern recognition receptors

PRRpred is a web server designed for predicting pattern Recognition Receptors (PRRs) and Non-PRRs. PRRs are germline encoded proteins that identify molecules related to pathogens and initiate an offense mechanism against the related pathogens, thereby aiding the innate immune response system. Several studies in the past have highlighted the discovery of new PRRs and shown their roles in various diseases. Further elucidation of their function and structure has been claimed to be a key step in therapy and vaccine design. This tool has been developed in order to identify PRRs using largest available dataset obtained from updated version of PRRDB. The prediction modules have been developed using primary sequence or evolutionary information of a protein. In addition to machine learning techniques, we also used sequence similarity approach based on BLAST for predicting PRRs with high precision.

Prediction Modules

Primary sequence

This module uses primary sequence of a protein for prediction, it allows user to submit multiple sequences at a time.

PSSM Profile

This module uses evolutionary information of a protein in form of PSSM profile, which is generated by PSIBLAST for predicting PRRs. It only allows user to submit a single sequence at a time.