Convert your favorite protein modeling program into a mutation predictor: "MODICT". Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Tanyalcin, Ibrahim; Stouffs, Katrien; Daneels, Dorien; Al Assaf, Carla; Lissens, Willy; Jansen, Anna; Gheldof, Alexander
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 19
• Publication Date: 2016-12
• Subjects: Prediction -- 3D protein model -- Bioinformatics; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1286-0
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Predict whether a mutation is deleterious based on the custom 3D model of a protein. Results We have developedmodict, a mutation prediction tool which is based on per residuermsd (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. Themodict perl package and the manual can be downloaded fromhttps://github.com/IbrahimTanyalcin/MODICT . Conclusions We show here thatmodict is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used bypolyphen andsift .