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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Improved the heterodimer protein complex prediction with protein language models.

Bo Chen¹, Ziwei Xie², Jiezhong Qiu³

¹Department of Computer Science and Technology, Tsinghua University, Beijing, China.

Briefings in Bioinformatics

|June 16, 2023

Summary

ESMPair, a new method using protein language models, identifies better interacting protein homologs (interologs) for complex structure prediction. This improves upon AlphaFold-Multimer, especially for low-confidence predictions and eukaryotic complexes.

Keywords:

alphafold-multimer protein complex structure prediction protein language model

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Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Protein complex structure prediction is crucial for understanding biological functions.
AlphaFold-Multimer is a leading tool, but its accuracy relies on the quality of multiple sequence alignments (MSAs) from interacting homologs (interologs).

Purpose of the Study:

To develop a novel method, ESMPair, for identifying high-quality interologs using protein language models.
To enhance the accuracy of protein complex structure prediction.

Main Methods:

Utilized protein language models to identify interologs for protein complexes.
Compared ESMPair's interolog identification with AlphaFold-Multimer's default method.
Evaluated the impact of interolog MSA diversity on prediction accuracy.

Main Results:

ESMPair generated superior interologs compared to AlphaFold-Multimer's default method.
ESMPair significantly improved protein complex structure prediction accuracy (+10.7% DockQ).
Combining multiple MSA generation methods further boosted accuracy (+22% DockQ).
Interolog MSA diversity was identified as a key factor influencing prediction accuracy.
ESMPair demonstrated particular efficacy for eukaryotic complexes.

Conclusions:

ESMPair offers a more effective approach for identifying interologs, leading to enhanced protein complex structure prediction.
The diversity of interolog MSAs is critical for accurate structure prediction.
ESMPair shows promise for advancing structural biology, especially in eukaryotic systems.