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Related Experiment Video

Updated: Feb 25, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Co-evolution techniques are reshaping the way we do structural bioinformatics.

Saulo de Oliveira¹, Charlotte Deane¹

¹Department of Statistics, University of Oxford, Oxford, UK.

|August 8, 2017

Summary

This summary is machine-generated.

Co-evolution techniques infer residue proximity for protein structure prediction. These methods also reveal functional relationships, proving valuable in bioinformatics.

Keywords:

Co-evolution techniques Direct Coupling Analysis structural bioinformatics

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

Structural bioinformatics
Computational biology
Biochemistry

Background:

Co-evolution analysis was initially developed for protein structure prediction.
It identifies residue pairs with spatial proximity based on correlated mutations.
The utility of co-evolution extends beyond structure prediction.

Purpose of the Study:

To highlight the broad applicability of co-evolutionary information.
To emphasize its role in extracting structural and functional insights.
To showcase its power in the era of large biological sequence datasets.

Main Methods:

Analysis of correlated mutations in protein sequence alignments.
Inferring residue-residue spatial and functional relationships.
Application of co-evolutionary data in diverse bioinformatics tasks.

Main Results:

Co-evolutionary methods accurately predict residue-residue contacts.
Extracted functional relationships aid in understanding protein mechanisms.
These techniques provide valuable insights across various bioinformatics applications.

Conclusions:

Co-evolutionary analysis is a versatile tool in structural bioinformatics.
It effectively leverages sequence data to uncover protein structure and function.
Its applications continue to expand in biological research.