Abstract

Introduction Advanced genetic strategies have transformed our understanding of the genetic basis and diagnosis of many phenotypes, including rare diseases. However, missense variants (MVs) are frequently identified and often classified as variants of uncertain significance (VUS). Although changes in protein free energy (Delta Delta G) were recently proposed as a tool for VUS classification, no objective cut-offs exist to distinguish between benign and pathogenic variants.Methods We utilized the computational tool mCSM to calculate Delta Delta G and predict the impact of MVs on protein stability. Specifically, we systematically analyzed the Delta Delta G of MVs in IFT140 to identify those potentially pathogenic and associated with Mainzer-Saldino syndrome (MSS). To this end, we evaluated Delta Delta G in IFT140 MVs sourced from ClinVar, gnomAD, and MSS patients, aiming to resolve the diagnosis of MSS in a child with a novel homozygous IFT140 variant, initially reported as a VUS.Results IFT140 MVs from MSS patients showed lower Delta Delta G values than those reported in gnomAD individuals (-1.389 vs. -0.681 kcal/mol; p = 0.0031). A ROC curve demonstrated strong discriminative ability (AUC = 0.8488; p = 0.0002), and a Delta Delta G cut-off of -1.3 kcal/mol achieving 50% sensibility and 90% specificity. The analysis of ClinVar IFT140 variants classified as VUS, showed that 75/323 (23%) presented Delta Delta G values below the cut-off. In the child clinically suspicious of MSS, this cut-off allowed the reclassification of the VUS (IFT140:p.W80C; Delta Delta G = -1.745 kcal/mol) as likely pathogenic, which confirmed the diagnosis molecularly.Conclusion Our findings demonstrate that Delta Delta G analysis can effectively distinguish potentially pathogenic variants in IFT140, enabling confirmation of MSS. The established cut-off of -1.3 kcal/mol showed strong discriminative power, aiding in the reclassification of VUS identified in IFT140. This approach highlights the utility of protein stability predictions in resolving diagnostic uncertainty in rare diseases.