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Updated: Oct 24, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Co-evolutionary distance predictions contain flexibility information.

Dominik Schwarz¹, Guy Georges², Sebastian Kelm³

¹Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.

Bioinformatics (Oxford, England)

|August 12, 2021

Summary

This summary is machine-generated.

Predicting residue-residue distances reveals protein flexibility. Analysis of predicted distance distributions shows distinct patterns for rigid versus flexible protein regions, offering insights into dynamic structures.

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Area of Science:

Computational biology
Structural bioinformatics
Protein dynamics

Background:

Co-evolution analysis and machine learning have advanced static protein structure prediction.
Current methods like AlphaFold2 achieve near-experimental accuracy for static models.
The next frontier in protein structure research is predicting multiple conformations and flexibility.

Purpose of the Study:

To investigate if residue-residue distance predictions can inform about protein flexibility.
To differentiate between rigid and flexible protein regions using predicted distance distributions.

Main Methods:

Utilized DMPfold to predict distance distributions for residue pairs in proteins exhibiting varied flexibility.
Classified residue pairs as rigid or flexible based on contact in reference structures.
Analyzed the shape of predicted distance distributions, specifically looking for local probability maxima.

Main Results:

Rigid residue pairs typically showed a single maximum in their predicted distance distributions.
Flexible residue pairs more frequently exhibited multiple local maxima.
The characteristics of predicted distance distributions correlate with protein rigidity and flexibility.

Conclusions:

The shape of predicted residue-residue distance distributions contains valuable information about protein flexibility.
This approach offers a new avenue for studying protein dynamics beyond static structure prediction.