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Updated: Feb 7, 2026

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

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

Published on: August 9, 2019

Evolutionary couplings and sequence variation effect predict protein binding sites.

Maria Schelling¹, Thomas A Hopf^1,2, Burkhard Rost^1,3,4,5

¹TUM (Technical University of Munich) Department of Informatics, Bioinformatics, & Computational Biology - i12, Garching/Munich, Germany.

|July 19, 2018

Summary

This summary is machine-generated.

Predicting protein binding sites is crucial for understanding molecular function. This study uses evolutionary couplings and machine learning to identify these sites, improving accuracy over random prediction.

Keywords:

binding site coevolution evolutionary couplings machine learning neural network prediction sequence variation

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Last Updated: Feb 7, 2026

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

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Published on: August 9, 2019

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Area of Science:

Computational biology
Structural bioinformatics
Molecular modeling

Background:

Protein binding sites are essential for molecular function but often lack experimental annotation.
Accurate prediction of per-residue binding sites is a significant challenge in bioinformatics.
Evolutionary couplings derived from coevolving residue pairs offer insights into protein function.

Purpose of the Study:

To predict protein binding sites using evolutionary couplings and sequence variation data.
To evaluate the performance of a machine learning approach for binding site prediction.
To provide a computational tool for identifying unannotated protein binding sites.

Main Methods:

Utilized evolutionary couplings derived from a maximum entropy statistical model.
Incorporated sequence variation data into the prediction model.
Developed a weighted sum scoring method and trained a neural network on eight scores, solvent accessibility, and conservation data.

Main Results:

The weighted sum method significantly outperformed random prediction (F1 score 19.3% vs 2%).
A neural network model further improved prediction accuracy (F1 score 26.2%).
Predicted binding sites showed spatial clustering, suggesting biological relevance.

Conclusions:

Evolutionary couplings combined with machine learning provide a powerful approach for predicting protein binding sites.
The developed method offers a valuable tool for annotating uncharacterized protein binding sites.
Further improvements are possible with larger datasets and refined annotation strategies.