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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: May 3, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Bioinformatics identification of coevolving residues.

Russell J Dickson¹, Gregory B Gloor

¹Department of Biochemistry, University Of Western Ontario, London, ON, Canada.

Methods in Molecular Biology (Clifton, N.J.)

|February 11, 2014

Summary

Protein positions coevolve, maintaining structure and function. Detecting coevolution using statistical methods on sequence alignments offers valuable protein engineering insights, but requires careful alignment quality control to avoid errors.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Protein bioinformatics
Evolutionary biology
Structural biology

Background:

Protein positions are hypothesized to coevolve, preserving critical structural and functional interactions throughout evolutionary history.
Identifying coevolving positions offers insights analogous to recognizing conserved features in protein families.

Purpose of the Study:

To explore the detection of putative coevolving positions in proteins.
To highlight the importance of high-quality multiple sequence alignments for accurate coevolution inference.
To discuss potential confounding factors and sources of error in coevolution analysis.

Main Methods:

Statistical methods for identifying covarying positions within protein sequence alignments.
Emphasis on the critical role of multiple sequence alignment quality.

Main Results:

Covarying positions can be detected using statistical approaches.
Coevolution inference is highly sensitive to the quality of multiple sequence alignments.
Understanding and accounting for confounding factors are essential for reliable results.

Conclusions:

Coevolution analysis, when performed with high-quality data and appropriate methods, serves as a valuable resource for understanding protein function and guiding protein engineering efforts.