SUMMARY: Today's genome browsers and protein databanks supply vast amounts of information about proteins. The challenge is to concisely bring together this information in an interactive and easy to generate format. AVAILABILITY AND IMPLEMENTATION: We have developed an interactive CIRCOS module called i-PV to visualize user supplied protein sequence, conservation and SNV data in a live presentable format. I-PV can be downloaded from http://www.i-pv.org. CONTACT: [email protected], [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Predict whether a mutation is deleterious based on the custom 3D model of a protein. RESULTS: We have developed MODICT, a mutation prediction tool which is based on per residue RMSD (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was tested for 42 described mutations in multiple genes including renin (REN), beta-tubulin (TUBB2B), biotinidase (BTD), sphingomyelin phosphodiesterase-1 (SMPD1), phenylalanine hydroxylase (PAH) and medium chain Acyl-Coa dehydrogenase (ACADM). Moreover, MODICT scores corresponded to experimentally verified residual enzyme activities in mutated biotinidase, phenylalanine hydroxylase and medium chain Acyl-CoA dehydrogenase. Several commercially available prediction algorithms were tested and results were compared. The MODICT PERL package and the manual can be downloaded from https://github.com/IbrahimTanyalcin/MODICT . CONCLUSIONS: We show here that MODICT is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used by POLYPHEN and SIFT.