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Related Experiment Video

Updated: Jul 11, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Predicting multiple conformations via sequence clustering and AlphaFold2.

Hannah K Wayment-Steele¹, Adedolapo Ojoawo¹, Renee Otten^1,2

¹Department of Biochemistry, Brandeis University and Howard Hughes Medical Institute, Waltham, MA, USA.

|November 13, 2023

Summary

This summary is machine-generated.

AF-Cluster enables AlphaFold2 to predict alternative protein states by clustering sequence alignments. This method confirmed a metamorphic protein

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein function relies on multiple conformational states.
Point mutations can alter the population of these states.
AlphaFold2 accurately predicts single protein structures.

Purpose of the Study:

To develop a method for sampling alternative protein conformational states.
To investigate the evolutionary distribution of protein conformations.
To test the impact of mutations on protein states.

Main Methods:

Clustering multiple sequence alignments by sequence similarity.
Applying AlphaFold2 to sample alternative states.
Using nuclear magnetic resonance spectroscopy for validation.
Designing and verifying mutations to induce state switching.

Main Results:

AF-Cluster successfully sampled alternative states for metamorphic proteins.
Predicted conformations for KaiB were distributed across clusters.
Experimental validation confirmed AF-Cluster predictions for KaiB variants and mutations.
A putative alternative state was identified for Mpt53.

Conclusions:

AF-Cluster is a powerful tool for exploring protein conformational landscapes.
This method aids in understanding protein function and disease mechanisms.
Integrating computational and experimental approaches will advance protein science.