Navigating Uncertainty: Assessing Variants of Uncertain Significance in the CDKL5 Gene for Developmental and Epileptic Encephalopathy Using In Silico Prediction Tools and Computational Analysis

Abstract

Mutations in the CDKL5 gene are associated with developmental and epileptic encephalopathy (DEE), a severe disorder characterized by developmental delay and epileptic activity. In genetic analyses of DEEs, variants classified as pathogenic confirm the diagnosis of the disease while Variants of Uncertain Significance (VUS) remain in a gray area due to insufficient evidence. This study aimed to optimize the interpretation of VUS in the CDKL5 gene by evaluating the performance of 22 in silico prediction tools using 186 known pathogenic or benign missense variants from the ClinVar database. The best-performing tools were then applied to analyze CDKL5 VUS variants, complemented by the evaluation of evolutionary conservation, structural analyses, and molecular dynamics simulations to assess their impact on protein structure and function. The results identified SNPred as the most reliable tool, achieving 100% accuracy, sensitivity, and specificity. Other high-performing tools, including ESM-1v, AlphaMissense, EVE, and ClinPred, demonstrated over 98% accuracy. Among 44 CDKL5 VUS variants evaluated, 20 were initially classified as pathogenic by these tools. However, further evaluation using stringent criteria—incorporating conservation scores, structural disruptions identified by Missense3D and PyMol, and molecular dynamics simulation results—led to the reclassification of 8 VUS variants as “potentially pathogenic” and the remaining 12 as “variants with conflicting data”. This comprehensive approach provides a robust framework for the classification of VUS in the CDKL5 gene, offering critical insights for accurate diagnosis and treatment strategies in DEE. These findings will serve as a valuable resource for clinicians and geneticists in resolving the diagnostic ambiguity associated with VUS.