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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and

Michael Heyne^1,2, Niv Papo³, Julia M Shifman⁴

¹Department of Biological Chemistry, Hebrew University of Jerusalem, Givat Ram Campus, 91906, Jerusalem, Israel.

Nature Communications

|January 17, 2020

Summary

We developed a high-throughput method to quantify changes in protein binding free energy (ΔΔG_bind) for thousands of mutants. This approach accelerates protein engineering and the study of protein-protein interaction evolution.

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Area of Science:

Biochemistry
Molecular Biology
Structural Biology

Background:

Quantifying mutation effects on protein-protein interactions is vital for evolutionary studies and protein engineering.
Measuring changes in binding free energy (ΔΔG_bind) for individual mutants is experimentally demanding and time-consuming.

Purpose of the Study:

To develop a high-throughput method for quantifying ΔΔG_bind for numerous protein mutants simultaneously.
To enable comprehensive mapping of protein-mutant binding landscapes.

Main Methods:

The protocol integrates protein randomization, Yeast Surface Display technology, and deep sequencing.
It utilizes a small set of experimental ΔΔG_bind measurements on purified proteins to extrapolate values for a large number of mutants.
The method was applied to map the binding landscape of the BPTI-Bovine Trypsin (BT) interaction.

Main Results:

The study successfully quantified ΔΔG_bind for thousands of protein mutants in a single experiment.
A comprehensive single-mutant binding landscape was generated for the high-affinity BPTI-BT interaction.
The method demonstrated high accuracy in quantifying ΔΔG_bind across a range of 12 kcal mol^-1.

Conclusions:

The developed method significantly streamlines the quantification of ΔΔG_bind, overcoming previous experimental limitations.
This approach facilitates large-scale mapping of protein-mutant binding landscapes, advancing protein engineering and evolutionary biology.
The study provides a powerful tool for dissecting the biophysical basis of protein-protein interactions.