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Molecular damage in F abry disease: Characterization and prediction of alpha‐galactosidase A pathological mutations
Author(s) -
Riera Casandra,
Lois Sergio,
Domínguez Carmen,
FernandezCadenas Israel,
Montaner Joan,
RodríguezSureda Victor,
de la Cruz Xavier
Publication year - 2015
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24708
Subject(s) - mutation , pathological , identification (biology) , sequence (biology) , computational biology , point mutation , scale invariant feature transform , genetics , biology , gene , medicine , computer science , artificial intelligence , pathology , botany , feature extraction
Loss‐of‐function mutations of the enzyme alpha‐galactosidase A (GLA) causes Fabry disease (FD), that is a rare and potentially fatal disease. Identification of these pathological mutations by sequencing is important because it allows an early treatment of the disease. However, before taking any treatment decision, if the mutation identified is unknown, we first need to establish if it is pathological or not. General bioinformatic tools (PolyPhen‐2, SIFT, Condel, etc.) can be used for this purpose, but their performance is still limited. Here we present a new tool, specifically derived for the assessment of GLA mutations. We first compared mutations of this enzyme known to cause FD with neutral sequence variants, using several structure and sequence properties. Then, we used these properties to develop a family of prediction methods adapted to different quality requirements. Trained and tested on a set of known Fabry mutations, our methods have a performance (Matthews correlation: 0.56–0.72) comparable or better than that of the more complex method, Polyphen‐2 (Matthews correlation: 0.61), and better than those of SIFT (Matthews correl.: 0.54) and Condel (Matthews correl.: 0.51). This result is validated in an independent set of 65 pathological mutations, for which our method displayed the best success rate (91.0%, 87.7%, and 73.8%, for our method, PolyPhen‐2 and SIFT, respectively). These data confirmed that our specific approach can effectively contribute to the identification of pathological mutations in GLA, and therefore enhance the use of sequence information in the identification of undiagnosed Fabry patients. Proteins 2015; 83:91–104. © 2014 Wiley Periodicals, Inc.