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Updated: Jun 25, 2025
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
MoLPC2: improved prediction of large protein complex structures and stoichiometry using Monte Carlo Tree Search and
Ho Yeung Chim1, Arne Elofsson1
1Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 106 91, Sweden.
Motivation:
Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry.
Results:
In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information.
Availability And Implementation:
MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.
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