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A computational model for controlling conformational cooperativity and function in proteins.

¹Department of Chemical and Biological Engineering, Koc University, Sariyer, Istanbul, Turkey.

|July 28, 2018

Summary

This summary is machine-generated.

This study introduces a universal computational model to predict protein residue correlations after perturbations. The model uses fluctuation covariance and conditional probabilities for rapid, accurate mapping of mutations and allosteric effects.

Keywords:

allostery fluctuation covariance matrix ligand binding molecular dynamics multivariate normal distribution mutation perturbation-response ubiquitin

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Area of Science:

Computational biology
Protein dynamics
Statistical mechanics

Background:

Understanding protein residue correlations is crucial for predicting mutation effects and allosteric mechanisms.
Existing models often lack universality or require extensive computational resources.
The role of higher-order correlations, like triple correlations, in protein dynamics remains underexplored.

Purpose of the Study:

To develop a model-independent computational method for predicting residue-pair correlations upon perturbation.
To establish a universal framework applicable to various proteins and biological processes.
To highlight the significance of triple correlations in understanding protein allostery.

Main Methods:

Utilizing the fluctuation covariance matrix as the sole input.
Applying conditional probabilities from a multivariate normal distribution to calculate perturbation responses.
Integrating the Gaussian Network Model (GNM) with a novel computational algorithm.

Main Results:

The model accurately predicts correlations between residue pairs by perturbing others, based solely on the covariance matrix.
Demonstrated the universality of the approach, applicable to predicting mutation consequences, allosteric activity, and ligand binding.
Showcased the utility of triple correlations for controlling distant residue interactions.
Validated the model using ubiquitin (UBQ), aligning with millisecond molecular dynamics and NMR data.

Conclusions:

The developed computational model offers a rapid and accurate method for predicting protein residue correlations.
The model's universality and reliance on the covariance matrix simplify complex dynamic predictions.
Further investigation into triple correlations is recommended for a deeper understanding of protein allostery and function.