Abstract
BackgroundPredict whether a mutation is deleterious based on the custom 3D model of a protein.ResultsWe 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1286-0) contains supplementary material, which is available to authorized users.
Highlights
Predict whether a mutation is deleterious based on the custom 3D model of a protein
The current concept of mutation effect prediction heavily depends on the composite algorithms that mainly implement a sequence-based BLAST search that tries to identify a number of similar protein sequences above a preset threshold, relate and combine several other parameters such as PSIC (Position-Specific Independent Counts), known three-dimensional (3D) structures of
Training and iteration As will be described throughout the “Results” section, MODICT is designed to work with distinct domains which are critical for protein functionality
Summary
We have derived a simple algorithm MODICT to predict whether a mutation is deleterious or not based on the RMSD obtained from superimposed mutated and wildtype 3D structures. MODICT scores from models generated by I-TASSER (negative control, 0.096 ; p.R209C, 0.266 ; p.H447R, 0.584 ) and PHYRE2 (negative control, 0.301 ; p.R209C, 0.504 ; p.H447R, 1.102 ) were compared with experimentally measured enzyme activity (wildtype 263eu, p.R209C, 91eu, p.H447R, 61eu) scaled to 1. MODICT scores were generated using 2 different modeling algorithms (I-TASSER, PHYRE2) and results were compared with residual enzyme activity as shown in Fig. 10 [13, 44]. There are only 2 mutations, taken together with the negative control score, raw MODICT scores without any conservation or weight files correlate strongly with enzymatic activity (PHYRE2: r = −0.805; I-TASSER: r = −0.838). Comparison of the generated MODICT scores after excluding outliers shows that the scores of individual mutations were negatively correlated with residual enzyme activities as shown in Fig. 6 (Pearson’s r = −0.494). Using the training module for the 14 mutations in Fig. 6 further improved the initial correlation coefficient from –0.494 to –0.722
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