Skip to content
#protein folding Open access

Molecular and Structural Characterization of Five Novel GLA Gene Variants in Fabry Disease

Sep 2026 · International Journal of Molecular Sciences · 0 citations · 28 references
Lysosomal Storage Disorders Research

Abstract

Fabry disease is an X-linked lysosomal storage disorder caused by pathogenic variants in the GLA gene, encoding α-galactosidase A. Enzyme deficiency leads to progressive globotriaosylceramide (Gb3) accumulation and multisystemic involvement. Here, we characterize five previously undescribed GLA variants (p.D109N, p.N215T, p.N192H, p.L166P, and p.F248S) through an integrated approach combining biomolecular and computational analyses to investigate their effects on enzyme structure, catalytic activity, and dimerization. The identified substitutions affect residues located in regions critical for protein folding and active-site integrity. The p.F248S variant may destabilize the hydrophobic core and reduce thermodynamic stability, whereas p.D109N and p.N192H may disrupt hydrogen-bond networks required for proper catalytic geometry. The p.N215T substitution is associated with impaired glycosylation, while p.L166P may induce local conformational changes that compromise correct folding. Biochemical analyses of all samples carrying these variants revealed reduced α-galactosidase A activity and increased the Gb3 levels, but also of uncertain significance (VUS). Overall, these findings support an effect of the five variants on α-galactosidase A structure and function and highlight the value of integrating clinical, genetic, biochemical, and computational data for variant interpretation. Molecular characterization of novel variants may improve genetic diagnosis, inform therapeutic decisions, and support the development of targeted treatment strategies in Fabry disease.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.