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.

Original languageEnglish
Article number425
Number of pages19
JournalBMC Bioinformatics
Volume17
Issue number1
DOIs
Publication statusPublished - 2016

ID: 27522934