Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein...

Area of Science:

• Genomics and Molecular Biology
• Human Genetics
• Computational Biology

Background:

• Pathogenic mutations altering protein function are key to human genetic diseases.
• Loss-of-function (LOF) mechanisms are well-studied, but dominant-negative (DN) and gain-of-function (GOF) mechanisms are less understood.
• Understanding diverse mutation mechanisms is crucial for accurate disease diagnosis.

Purpose of the Study:

• To investigate the protein-level effects of pathogenic missense mutations.
• To differentiate the impact of mutations based on their molecular disease mechanisms (LOF, DN, GOF).
• To evaluate the performance of current computational variant effect predictors on non-LOF mutations.

Main Methods:

• Analysis of pathogenic missense mutations across different molecular mechanisms.
• Comparison of mutation effects on protein structure for recessive vs. dominant and LOF vs. non-LOF mutations.
• Assessment of computational variant effect predictor performance using sequence conservation and 3D structural data.

Main Results:

• Dominant, non-LOF disease mutations exhibit milder effects on protein structure compared to other mutation types.
• Dominant-negative (DN) mutations are significantly enriched at protein-protein interaction interfaces.
• Current computational predictors, including sequence conservation-based methods, underperform in identifying non-LOF mutations.

Conclusions:

• Many pathogenic mutations acting via DN and GOF mechanisms may be overlooked by existing variant prioritization strategies.
• The tendency of non-LOF mutations to cluster in 3D space offers a potential avenue for improved computational identification.
• Incorporating molecular disease mechanisms into computational predictions can enhance the detection of pathogenic variants.