# MoLPC2: improved prediction of large protein complex structures and stoichiometry using Monte Carlo Tree Search and AlphaFold2

## TL;DR

MoLPC2 improves the prediction of large protein complex structures and their composition without needing prior knowledge of their stoichiometry.

## Contribution

MoLPC2 introduces Monte Carlo Tree Search and AlphaFold2 to predict protein complex structures and stoichiometry simultaneously.

## Key findings

- MoLPC2 accurately predicted 50 out of 175 nonredundant protein complexes with a TM-score ≥ 0.8.
- The method enables structure prediction without prior knowledge of the complex's stoichiometry.
- A notebook is provided for easy use of MoLPC2.

## 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.