Deep learning-based assessment of missense variants in the COG4 gene presented with bilateral congenital cataract.

Objective: We compared the protein structure and pathogenicity of clinically relevant variants of the COG4 gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.

Methods and analysis: The sequences of clinically relevant Cog4 missense variants (one novel identified p.Y714F and three pre-existing p.G512R, p.R729W and p.L769R from Uniprot Q9H9E3) were imported into AF2 for protein structural prediction, and the pathogenicity was estimated using AM and ThermoMPNN. Different pathogenicity metrics were aggregated with principal component analysis (PCA) and further analysed at three levels (amino acid position, substitution and post-translation) based on all possible Cog4 missense variants (n=14 915).

Results: Localised protein structural impact including change of conformation and amino acid polarity, breakage of hydrogen bond and salt-bridge, and formation of alpha-helix were identified among clinically relevant Cog4 variants. The global structural comparison with multidimensional scaling demonstrated variants with similar protein structures (AF2) tended to exhibit similar clinical and biological phenotypes. The Cog4 p.Y714F variant exhibited greater protein structural similarity to mutated Cog4 found in Saul‒Wilson syndrome (p.G512R) and shared similar clinical phenotype (congenital cataract and psychomotor retardation). PCA of included pathogenic metrics demonstrated p.Y714F occurred at a critical position in Cog4 amino acid sequence with disrupted post-translational phosphorylation.

Conclusion: Deep learning algorithms, including AF2, AM and ThermoMPNN, can be useful for evaluating variant of uncertain significance (VUS) by structural and pathogenicity prediction. Despite classified as VUS (American College of Medical Genetics and Genomics criteria: PM1, PP4), the pathogenicity in this Cog4 variant cannot be ruled out and warrants further investigation.