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Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural

Chunxiang Peng¹, Xiaogen Zhou¹, Jun Liu¹

¹College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Fundamental Research

|February 6, 2026

Summary

Multidomain protein modeling is challenging. M-SADA, a new method, accurately assembles multiple protein conformational states, outperforming AlphaFold2 in capturing diverse structures.

Keywords:

Deep learning Domain assembly Evolutionary algorithm Multiple conformational states Protein structure prediction

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

AlphaFold2 revolutionized single-domain protein structure prediction.
Accurate modeling of multidomain proteins, especially their multiple conformational states, remains a significant challenge.

Purpose of the Study:

To develop an advanced method, M-SADA, for assembling multidomain proteins with multiple conformational states.
To improve the accuracy and completeness of protein structure modeling for complex biological systems.

Main Methods:

Developed M-SADA, a multidomain protein assembly method utilizing a multiple population-based evolutionary algorithm.
Integrated homologous and analogous templates with deep learning-predicted inter-domain distances to guide energy function construction.
Created benchmark datasets for evaluating performance on both multiple and single conformational states.

Main Results:

M-SADA significantly outperforms AlphaFold2 in modeling multiple conformational states of multidomain proteins (40.3% achieved TM-score > 0.90 for distinct states).
M-SADA shows a 5.2% higher average TM-score (0.913 vs. 0.868) than AlphaFold2 for single conformational state modeling on a large benchmark dataset.
M-SADA successfully predicted novel domain arrangements for CASP15 multidomain targets when individual domain structures were accurate.

Conclusions:

M-SADA represents a significant advancement in computational protein structure modeling, particularly for multidomain proteins with dynamic conformational ensembles.
The method offers improved accuracy for both single and multiple conformational state predictions, addressing limitations of existing tools like AlphaFold2.
M-SADA has the potential to accelerate research in areas requiring precise understanding of protein dynamics and function.