In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

Abstract

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

Computationally Engineered CRISPR-SpyCas9 High-Fidelity Variants with Improved Specificity and Reduced Non-specific DNA Damage

“…NMA is a dynamic approach to study the function of proteins. We previously reported the use of NMA to link between genotype and phenotype in context of disease-causing mutations [32][33][34][35] , and its use to assess the specificity profile of SpyCas9 in the presence of mismatches 29 . Inspired by its ability to simulate the protein function, we used NMA to build ComPE, an in-silico method combining principles of directed evolution and deep mutational scanning (Fig.…”

Gene-Disease Association Classification Using Xgboost Classifier

“…al. have explored diseases caused by gene TUBB4A [8] which primarily causes brain disorders. This gene encodes a protein beta tubulin.…”