bioRxivBioinformatics
Convert Your Favorite Protein Modeling Program Into A Mutation Predictor: "MODICT"
Ibrahim Tanyalcin, Katrien Stouffs, Dorien Daneels, Carla Al Assaf, Willy Lissens, Anna Jansen, Alexander Gheldof
Abstract
Motivation: Predict whether a mutation is deleterious based on the custom 3D model of a protein.\n\nMethods: We have developed O_SCPLOWMODIOTC_SCPLOW, 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, beta-tubulin, biotinidase, sphingomyelin phosphodiesterase-1, phenylalanine hydroxylase and medium chain Acyl-Coa dehydrogenase. Moreover, O_SCPLOWMODIOTC_SCPLOW 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 O_SCPLOWMODIOTC_SCPLOW PERL package and the manual can be downloaded from https://github.com/MODICT/MODICT.\n\nConclusion: We show here that O_SCPLOWMODIOTC_SCPLOW 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.
- assay: protein structure
- research focus: variant effect prediction
- approach: deep learning
- approach: statistical model
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