MoLPC2: improved prediction of large protein complex structures and stoichiometry using Monte Carlo Tree Search and AlphaFold2.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38781500.
- Also identified by DOI 10.1093/bioinformatics/btae329 and PMC identifier 11194477.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. 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. MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.
Medical subject headings
- Monte Carlo Method
- Algorithms
- Software
- Proteins