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Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

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By focusing on Hypophosphatasia, a rare inherited disorder we performed a comprehensive computational analysis of mutational effects on protein function in the encoded protein of Tissue Nonspecific Alkaline Phosphatase. This analysis demonstrated that pathogenic mutations can often modulate long-range allosteric interactions and communications in the dynamic protein network and the mechanisms underlying severity of mutational effects can be rationalized based on molecular principles of allostery. We have also developed a machine learning-based method to classify different disease phenotypes, and the interpretability of the classification model was addressed using the structural-functional analysis of network topologically important mutations. The results of this study revealed allosteric molecular signatures of severe mutations, suggesting that allosteric models of protein dynamics and function can be useful in dissecting complex genotype-phenotype relationships.


This article was originally published in PLoS Computational Biology, volume 18, issue 3, in 2022. (2471 kB)

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This work is licensed under a Creative Commons Attribution 4.0 License.



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