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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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Updated: Mar 16, 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

Correlated positions in protein evolution and engineering.

Jorick Franceus¹, Tom Verhaeghe¹, Tom Desmet²

¹Department of Biochemical and Microbial Technology, Centre for Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.

Journal of Industrial Microbiology & Biotechnology

|August 13, 2016

Summary

Statistical analysis reveals correlated positions in proteins, where interdependent mutations maintain function. Understanding these evolutionary fingerprints aids protein engineering and mutagenesis strategies.

Keywords:

Coevolution Correlated mutation analysis Correlated positions Protein engineering

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Last Updated: Mar 16, 2026

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Area of Science:

Biochemistry
Molecular Biology
Evolutionary Biology

Background:

Statistical analysis of protein multiple sequence alignments identifies positions with interdependent mutations.
These correlated positions suggest evolutionary constraints on amino acid combinations for protein stability and function.

Purpose of the Study:

To review recent findings on correlated positions in proteins.
To explore opportunities and challenges in utilizing correlated positions for protein engineering and mutagenesis.

Main Methods:

Review of existing literature on statistical analysis of protein sequence alignments.
Analysis of evolutionary fingerprints and their implications for protein structure and function.

Main Results:

Correlated positions highlight evolutionary pressures on protein sequences.
These positions offer potential targets for semi-rational protein engineering and mutagenesis.

Conclusions:

Correlated position networks provide insights into protein evolution, structure, and function.
Further research is needed to fully understand and practically apply these networks in protein engineering.