Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology. (June 2022)

• Record Type: Journal Article
• Authors: Meireles, Mariana R.; Stelmach, Lara H.; Bandinelli, Eliane; Vieira, Gustavo F.
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Missense mutation -- Coagulation -- Molecular properties -- Bioinformatics -- Functional impact -- Mendelian disorders; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106768
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Missense variants are prevalent in hemophilia A (HA), leading to distinct phenotypes. Computational methods help infer the effects of variants in the protein chain. Investigating changes in molecular features helps the genotype-phenotype correlation. Position influences variants' effects on protein function and HA determination. The applied approach can help advance research in HA and other Mendelian disorders. Abstract: Background and objectives: Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype. Methods: An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution. Results: About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains. Conclusions: The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity. Graphical abstract: Image, graphical abstract