Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Abstract

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.

Functional insight into Cordyceps militaris sugar transporters by structure modeling, network analysis and allosteric regulation

“…FoldX 51,52 was used to perform single point saturation mutations on the Hex subfamily proteins, and its effect on protein stability can be measured by the difference in folding Gibbs free energy (DDG value) before and after the mutation. The AlloSigMA 2 server 53,54 was used to evaluate the allosteric effects of each mutation based on the structure-based statistical mechanical model of allostery, and the effect of steric hindrance at a particular site for each selected residue increase (UP mutation) or decrease (DOWN mutation) was calculated.…”

Ensemble origins and distance-dependence of long-range mutational effects in proteins

“…in the first shell around the mutated site, and that the native structure is unperturbed or does not change. However, evidence is accumulating on the long-range effects of even single-point mutations from hydrogen-deuterium exchange experiments ( Offenbacher et al., 2017 ; Pacheco-Garcia et al., 2021 ; Puri et al., 2022 ; Roche et al., 2013 ), NMR measures of order parameters and chemical shift perturbations ( Bouvignies et al., 2011 ; Consonni et al., 1999 ; Haririnia et al., 2008 ; Whitley et al., 2008 ), double-mutant-cycle measures of thermodynamic coupling ( Chi et al., 2008 ; Fodor and Aldrich, 2004 ), anisotropic-/elastic-network models and simulations ( Chennubhotla and Bahar, 2007 ; Gerek and Ozkan, 2011 ; Xiao et al., 2022 ; Yu et al., 2019 ), and statistical-mechanical-cum-perturbation analysis of protein structures ( Guarnera and Berezovsky, 2019 ; Liu et al., 2009 ; Tee et al., 2020 ). In addition, while charged residue mutations have been successfully exploited to engineer stabilities ( Sanchez-Ruiz and Makhatadze, 2001 ), they can also alter the folding-conformational landscape due to the long-range nature of charge-charge interactions.…”