UNLABELLED: ProtVirDB is a comprehensive and user-friendly web-based knowledgebase of virulent proteins belonging to protozoan species. The database will facilitate research and provide an integrated platform for comparative studies of virulent proteins in different parasitic protozoans and organize them under a unifying classification schema with functional categories. Remarkably, one-third of the protein sequences in the database showed presence of either mono- or hetero-repeats, or both concomitantly--hence reiterating the importance of repeats in parasite virulence mechanisms. A number of useful bioinformatics tools including BLAST and tools for phylogenetic analysis are integrated with the database. With the rapidly burgeoning interest in the pathogenesis mechanisms of protozoans and ongoing genome sequencing projects, we anticipate that the database will be a useful tool for the research community. AVAILABILITY: http://bioinfo.icgeb.res.in/protvirdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures. RESULTS: In the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins. CONCLUSION: The method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.