Molecular Evolution of the Tissue-nonspecific Alkaline Phosphatase Allows Prediction and Validation of Missense Mutations Responsible for Hypophosphatasia

低磷酸酶 错义突变 碱性磷酸酶 化学 生物信息学 突变 磷酸酶 遗传学 生物 生物化学 基因
作者
Jérémie Silvent,Barbara Gasse,Étienne Mornet,Jean‐Yves Sire
出处
期刊:Journal of Biological Chemistry [Elsevier BV]
卷期号:289 (35): 24168-24179 被引量:66
标识
DOI:10.1074/jbc.m114.576843
摘要

ALPL encodes the tissue nonspecific alkaline phosphatase (TNSALP), which removes phosphate groups from various substrates. Its function is essential for bone and tooth mineralization. In humans, ALPL mutations lead to hypophosphatasia, a genetic disorder characterized by defective bone and/or tooth mineralization. To date, 275 ALPL mutations have been reported to cause hypophosphatasia, of which 204 were simple missense mutations. Molecular evolutionary analysis has proved to be an efficient method to highlight residues important for the protein function and to predict or validate sensitive positions for genetic disease. Here we analyzed 58 mammalian TNSALP to identify amino acids unchanged, or only substituted by residues sharing similar properties, through 220 millions years of mammalian evolution. We found 469 sensitive positions of the 524 residues of human TNSALP, which indicates a highly constrained protein. Any substitution occurring at one of these positions is predicted to lead to hypophosphatasia. We tested the 204 missense mutations resulting in hypophosphatasia against our predictive chart, and validated 99% of them. Most sensitive positions were located in functionally important regions of TNSALP (active site, homodimeric interface, crown domain, calcium site, …). However, some important positions are located in regions, the structure and/or biological function of which are still unknown. Our chart of sensitive positions in human TNSALP (i) enables to validate or invalidate at low cost any ALPL mutation, which would be suspected to be responsible for hypophosphatasia, by contrast with time consuming and expensive functional tests, and (ii) displays higher predictive power than in silico models of prediction.
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