|Annotating the affect of missense mutations on the
protein's function using sequence and structural features
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The discrimination between functionally-neutral amino acid substitutions and deleterious mutations that harm protein function is of major importance to our understanding of diseases in molecular details. The rapidly growing amount of experimental data enables the development of computational tools to facilitate the annotation of these substitutions. To this end, we estalished MuD (Mutation Detector). MuD utilizes structural- and sequence-based features with a Random Forests classifier to assess the impact of a given substitution on the protein's function and/or structure. MuD was benchmarked using a cross-validation process on a subset of the Bromberg and Rost dataset(1) and achieved Mathew's correlation coefficient of 0.49 which is as good as current tools.
The uniqueness of MuD is its interactivity. The user can guide the classifier
by supplying structural data to improve the prediction accuracy. The strength
of this semi-automatic scheme is well demonstrated on three independent cases
(Table 1), which were not included in the Bromberg and Rost dataset(1) used
to develop the predictor. Following the scheme of our server, first we removed
ligands which are biologically irrelevant from the crystal structures and
then selected the correct oligomerization state according to the literature.
Thus the prediction improved (Table 1) surpassing the performance of SNAP(1),
SIFT(2) and PolyPhen(3).
The Sub-BR dataset
The Sub-BR utilzed for training and benchmarking MuD is a balanced set of non-neutral and neutral substitutions. It comprises of 12,133 substitutions from 1178 proteins. Out of the 12,133 of the Sub-BR dataset, 10,253 substitutions originated from the PMD dataset (7) and 2,065 are evolutionary model-based substitutions. The structural classification of the Sub-BR proteins according to the Structural Classification Of Proteins database (SCOP) (8) can be found in the link below: