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Updated: Jan 10, 2026

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

StoPred: Accurate Stoichiometry Prediction for Protein Complexes Using Protein Language Models and Graph Attention.

Quancheng Liu¹, Chunxiang Peng², Wei Zheng^3,1

¹Gilbert S Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, 48109-2218, MI, U.S..

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

This summary is machine-generated.

StoPred accurately predicts protein complex stoichiometry using protein language models and graph attention networks. This novel method advances computational biology by enabling accurate prediction for both homomeric and heteromeric protein assemblies.

Keywords:

deep learning graph neural network protein language model stoichiometry prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Protein complexes are crucial for biological functions, but determining their subunit stoichiometry is experimentally challenging.
Existing computational methods for stoichiometry prediction have limitations, especially for proteins lacking close homologs or known assembly states.
Current protein language model (pLM) approaches predict homo-oligomer stoichiometry but fail with hetero-oligomeric complexes and do not fully model inter-subunit relationships.

Purpose of the Study:

To develop a novel computational method, StoPred, for accurate prediction of protein complex stoichiometry.
To address the limitations of existing methods by enabling prediction for both homomeric and heteromeric complexes without requiring templates or predefined compositions.
To leverage advancements in pLMs and graph attention networks for modeling subunit interactions.

Main Methods:

Integrated protein language model (pLM) embeddings with a graph attention network (GAT).
Modeled subunit-level interactions within protein complexes.
Inferred stoichiometry directly from sequence or structure features for both homo- and hetero-oligomers.

Main Results:

StoPred demonstrated improved accuracy and efficiency compared to deep learning-based and template-based methods.
Achieved up to 16% higher top-1 accuracy for homomeric and 41% higher for heteromeric complexes on a held-out test dataset.
StoPred is the first deep learning method capable of accurately predicting hetero-oligomeric complex stoichiometry.

Conclusions:

StoPred offers a significant advancement in predicting protein complex stoichiometry, particularly for hetero-oligomeric assemblies.
The method's ability to predict stoichiometry from sequence or structure without prior knowledge enhances its applicability.
StoPred provides a powerful new tool for computational biology and structural biology research.