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Updated: Aug 26, 2025
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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
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Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
Patrick Bryant1,2, Gabriele Pozzati3,4, Wensi Zhu3,4
1Science for Life Laboratory, 172 21, Solna, Sweden. patrick.bryant@scilifelab.se.
Nature Communications
|October 12, 2022
Summary
Predicting large protein complexes is challenging. This study shows assembling them from smaller predicted parts improves accuracy, especially for symmetrical structures, using a new scoring function.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- AlphaFold accurately predicts single- and multi-chain protein structures.
- Predictive accuracy diminishes with increasing chain numbers.
- GPU memory limitations restrict the size of predictable protein complexes.
Purpose of the Study:
- To develop a method for predicting the structure of large protein complexes.
- To overcome limitations of direct prediction for large assemblies.
- To assess the accuracy and completeness of assembled complexes.
Main Methods:
- Assembling large complexes (10-30 chains) from predicted subcomponents.
- Utilizing Monte Carlo tree search for assembly.
- Developing a scoring function (mpDockQ) to evaluate assembly completeness and accuracy.
Main Results:
- Successfully assembled 91 out of 175 complexes.
- Achieved a median TM-score of 0.51 for assembled complexes.
- Identified 30 highly accurate assemblies (TM-score ≥0.8), representing 33% of complete assemblies.
- Demonstrated accurate assembly for complexes with symmetry.
- Highlighted challenges in assembling asymmetrical complexes.
Conclusions:
- Predicting large protein complex structures is feasible by assembling subcomponent predictions.
- The mpDockQ scoring function aids in assessing assembly quality.
- Symmetrical complexes are more amenable to accurate assembly using this method.
- The method is publicly available as a Colab notebook.
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