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AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of

Ishan Taneja¹, Manuel A Llanos¹, Monica L Fernández-Quintero¹

¹Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States.

Journal of Chemical Information and Modeling

|December 31, 2025

Summary

New machine learning methods, AlphaFold-RandomWalk and AlphaFold-Ensemble, generate diverse protein conformations. Combining these with molecular dynamics simulations efficiently probes protein conformational heterogeneity.

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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

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein conformational diversity is crucial for biological function.
Machine learning (ML) methods, like AlphaFold, can predict protein structures.
The ability of ML to independently generate diverse protein conformations is still under investigation.

Purpose of the Study:

To develop novel ML-based methods for generating diverse protein conformations.
To create a computational pipeline integrating ML with molecular dynamics (MD) simulations.
To assess the utility of ML-generated conformations in exploring protein conformational heterogeneity.

Main Methods:

Development of AlphaFold-RandomWalk (AF-RW) by adding noise to model weights.
Development of AlphaFold-Ensemble (AF-Ensemble) by fine-tuning model ensembles.
Integration into a multistage pipeline seeding unbiased MD simulations from ML conformations.

Main Results:

AF-RW significantly increased conformational diversity compared to conventional methods.
The pipeline generated biologically meaningful alternative conformations for ten proteins.
Simulations from ML-generated conformations approximated free energy landscapes for K-Ras and ribose-binding protein.

Conclusions:

ML methods can effectively generate diverse protein conformations.
Combining ML-predicted diverse conformations with MD simulations efficiently probes protein conformational heterogeneity.
This approach provides a powerful tool for studying protein dynamics and function.