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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial

Toma Aoki¹, Ryuhei Harada²

¹School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-0821, Japan.

The Journal of Physical Chemistry Letters

|March 14, 2026

Summary

Artificial intelligence (AI) models predict protein structures, aiding molecular dynamics simulations. AI-Assisted Outlier FLOODing (OFLOOD) efficiently calculates protein free-energy landscapes, validating AI-predicted structures.

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Area of Science:

Computational biology
Structural biology
Artificial intelligence

Background:

Recent advances in artificial intelligence (AI) have enabled rapid and accurate prediction of protein structures.
AI-predicted protein conformations serve as starting points for molecular dynamics (MD) simulations.
These starting points are often near transition paths, facilitating conformational sampling and structural transitions.

Purpose of the Study:

To develop and validate a framework integrating AI with enhanced conformational sampling for efficient free-energy landscape (FEL) calculations.
To quantitatively evaluate the stability of AI-predicted protein structures using calculated FELs.

Main Methods:

Integration of AI models, specifically AlphaFold2, with the enhanced conformational sampling method Outlier FLOODing (OFLOOD) to create AI-Assisted OFLOOD.
Reduction of multiple sequence alignments for AlphaFold2 input.
Application of AI-Assisted OFLOOD to both soluble and membrane proteins.

Main Results:

AI-Assisted OFLOOD successfully identified transition-like and metastable states in the FELs of both soluble and membrane proteins.
The method demonstrated efficiency in conformational sampling and FEL calculation.
The framework proved reliable for quantitatively assessing the stability of AI-predicted protein structures.

Conclusions:

AI-Assisted OFLOOD provides a reliable and efficient method for calculating protein free-energy landscapes.
This framework enables quantitative evaluation of the stability of AI-predicted protein structures.
The integration of AI with enhanced sampling methods represents a significant advancement in structural biology and computational biophysics.