In-Silico Analysis of Novel CYP1B1 Gene Mutations in Pakistani Families Reveals Structural and Functional Insights

Background: Mutations in the CYP1B1 gene have been associated with primary congenital glaucoma (PCG), a developmental visual disorder. CYP1B1 mutations have been linked to increased lipid peroxidation and abnormalities in trabecular meshwork. In this study, protein modelling is utilized to analyze the pathogenicity of hotsopt mutations in the CYP1B1 gene in Pakistani families with PCG.
Methodology: A comprehensive literature survey was conducted to identify CYP1B1 mutations in Pakistani Patients. Ensemble was used to identify missense mutations. 3-D modeling of the mutants was done using i-Tasser. Potential deleterious effects and pathogenicity were evaluated using PolyPhen-2. The Clustal Omega and Clustal W tools were used to create multiple sequence alignments and construct a phylogenetic tree of the relevant protein variants. The effects of amino acid substitutions on protein function were assessed using SIFT analysis, scoring pathogenic mutations from 0.0 to 1.0 in decreasing order.
Results: A total of 5 mutations—4 missense (c.716C>G/p. A115P, c.988G>T/p. A330F, c.355G>T/p. A119S, and c.1058C>T/p. E229K) and 1 frameshift mutation (c.1325delC/p. P442Efs*15)—were identified as hotspot variant among Pakistani patients. A119S was observed as the most common mutation, while E229K was identified as a novel mutation in patients. 3-D modelling of the mutant proteins using i-Tasser revealed the global topology of the variants. PolyPhen-2 analysis revealed the deleterious effects of the mutations. Moreover, a SIFT score of 1.0 was received for all mutations. MUpro analysis revealed decreased stability in protein structure. Multiple sequence alignments of CYP1B1 variants showed the highest conservations of these variants among multiple species highlighting its pathogenic significance.
Conclusion: This In-silico analysis contributes to understanding the structural and functional implications of CYP1B1 gene mutations. These insights can potentially help in the development of new targeted and personalized treatments for PCG patients.