Computational modeling and prediction of deletion mutants

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• In-frame deletion mutations are linked to various diseases.
• Understanding their impact on protein structure and function is crucial but challenging due to limited structural data.
• Advancements in deep learning for protein structure prediction necessitate updated computational tools for deletion mutation analysis.

Purpose of the Study:

• To experimentally characterize the structural and thermodynamic consequences of single residue deletions in an alpha-helical sterile alpha motif domain.
• To evaluate and benchmark computational protocols for modeling and predicting the effects of deletion mutations.
• To develop a reliable computational metric for classifying tolerated versus deleterious deletion mutations.

Main Methods:

• Systematic single-residue deletion mutagenesis of a sterile alpha motif domain.
• Structural and thermodynamic analysis using 2D NMR spectroscopy and differential scanning fluorimetry.
• Computational modeling and prediction using AlphaFold2 and RosettaRelax, with evaluation of pLDDT and Rosetta ΔΔG metrics.

Main Results:

• Experimental data revealed significant structural and thermodynamic changes upon residue deletion.
• The AlphaFold2 followed by RosettaRelax protocol demonstrated superior performance in modeling deletion mutants.
• A combined metric of pLDDT and Rosetta ΔΔG proved most effective for classifying mutation tolerance.
• The developed method was validated on independent datasets, including disease-associated mutations.

Conclusions:

• Computational approaches, particularly AlphaFold2 and RosettaRelax, can effectively predict the impact of in-frame deletion mutations.
• A combined structural and energy-based metric reliably classifies deletion mutation effects.
• This approach aids in understanding disease mechanisms driven by deletion mutations and can be applied to broader protein datasets.