Interpreting the effect of mutations to protein binding sites from large-scale genomic screens

Area of Science:

• Genomics
• Computational Biology
• Biophysics

Background:

• Predicting missense mutation effects on protein function is difficult, limiting interpretation of large-scale genomic screens.
• Mutations can alter protein interactions with partners or small molecules (e.g., ATP), modulating function.
• Existing methods struggle to statistically assess the frequency of mutations impacting binding within genomic screen contexts.

Purpose of the Study:

• To develop a methodology for generating functional and statistical insights from genomic screen mutational data.
• To quantitatively analyze whether mutations affecting protein-protein or protein-ligand binding occur more or less frequently than expected by chance.
• To provide a framework for interpreting mutations in protein binding, protein-DNA interactions, and therapeutic resistance evolution.

Main Methods:

• Calculating the potential impact of all possible mutations on protein binding and ligand interactions.
• Comparing the distribution of expected mutation impacts to observed mutations from large-scale genomic screens.
• Applying the methodology to diverse biological examples including protein-protein binding, DNA interactions, and drug resistance.

Main Results:

• The methodology enables quantitative and statistical assessment of mutation impacts on binding.
• It allows determination of whether observed binding-related mutations deviate from chance expectations.
• Demonstrated utility in interpreting mutations across various biological contexts, including therapeutic resistance.

Conclusions:

• This approach offers a novel way to interpret functional consequences of mutations identified in genomic screens.
• It provides statistical rigor for understanding the role of binding-altering mutations in biological processes.
• The method is broadly applicable for analyzing mutation impacts on molecular interactions and evolutionary dynamics.