VICMpred: SVM-Based Prediction of Functional Proteins of Gram-Negative Bacteria Using Amino Acid Patterns and Composition

VICMpred: SVM-Based Prediction of Functional Proteins of Gram-Negative Bacteria Using Amino Acid Patterns and Composition VICMpred is a computational web server developed for predicting the major functional classes of Gram-negative bacterial proteins from amino acid sequences. The tool classifies Gram-negative bacterial proteins into four broad functional categories: virulence factors, information molecules, cellular process proteins, and metabolism-related proteins. VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, and class-specific tetrapeptide patterns. Web Server: https://webs.iiitd.edu.in/raghava/vicmpred/ Citation Saha, S., and Raghava, G. P. S. VICMpred: An SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, Proteomics & Bioinformatics, 4(1), 42-47, 2006. https://doi.org/10.1016/S1672-0229(06)60015-6 About the Research Functional annotation of proteins is one of the major challenges in the post-genomic era. Due to the rapid growth of protein sequence databases, experimental functional characterization of every newly discovered protein is not practical. Traditional methods such as BLAST, FASTA, and PSI-BLAST depend on sequence similarity. However, proteins with similar functions may show poor sequence similarity, making direct function prediction difficult. VICMpred was developed as a direct function prediction method for Gram-negative bacterial proteins. Instead of only predicting subcellular localization, it predicts broad biological functions directly from protein sequence features. Data Compilation: The final dataset contained 670 non-redundant Gram-negative bacterial proteins. These included 255 cellular process proteins, 60 information molecules, 285 metabolism proteins, and 70 virulence factors. Methodology: VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, PSI-BLAST similarity search, class-specific tetrapeptide patterns, and hybrid combinations of these features.