VirFacPred: A Web Server for Designing and Predicting Virulent Proteins

VirFacPred: A Web Server for Designing and Predicting Virulent Proteins

VirFacPred is a computational web server developed for predicting virulent and non-virulent proteins from primary protein sequences.

The tool uses a wide range of information and computational techniques, including machine learning, BLAST-based similarity search, and MERCI-based motif scanning. VirFacPred is useful for identifying proteins that may contribute to pathogenicity, host invasion, survival, immune evasion, and virulence mechanisms.

Web Server: https://webs.iiitd.edu.in/raghava/virfacpred/

About the Research

Virulent proteins are proteins that help pathogens cause disease in a host. These proteins may help pathogens attach to host cells, invade tissues, escape immune responses, survive inside the host, or damage host systems.

Identification of virulent proteins is important for understanding pathogenic mechanisms and for discovering potential drug targets, vaccine candidates, and diagnostic markers.

VirFacPred was developed to predict virulent proteins using sequence-based computational approaches.

Data Compilation: The models were trained on a large dataset containing 8233 virulent proteins and 8233 non-virulent proteins.

Methodology: VirFacPred uses machine learning-based prediction, BLAST similarity search, and MERCI motif scanning. The web server provides both individual and hybrid prediction approaches.