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Updated: Sep 11, 2025

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Estimating protein complex model accuracy using graph transformers and pairwise similarity graphs.

Jian Liu1, Pawan Neupane1, Jianlin Cheng1

  • 1Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, United States.

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Summary

We developed GATE, a new method using graph transformers to accurately assess protein complex structural model quality. GATE outperforms existing methods, aiding in selecting reliable models for protein function analysis and drug design.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Accurate estimation of protein complex structure is crucial for applications like protein function analysis and drug design.
  • Selecting high-quality structural models from large pools generated by methods like AlphaFold2 and AlphaFold3 remains a significant challenge.

Purpose of the Study:

  • To introduce GATE, a novel method for predicting the quality of protein complex structural models.
  • To improve the selection of accurate structural models for downstream biological applications.

Main Methods:

  • GATE utilizes graph transformers applied to pairwise model similarity graphs.
  • It integrates single-model and multi-model quality features to capture intrinsic and geometric similarities.
  • The method leverages graph neural networks for robust quality prediction.

Main Results:

  • GATE achieved the highest Pearson's correlation (0.748) and lowest ranking loss (0.1191) on the CASP15 dataset.
  • In the CASP16 blind experiment, GATE demonstrated strong performance across multiple metrics, including first place in Pearson's correlation (0.7076).
  • The method showed consistent performance on large in-house datasets, confirming its robustness and practical applicability.

Conclusions:

  • GATE offers a robust and accurate solution for assessing protein complex structural model quality.
  • Its performance suggests significant utility in real-world scenarios for selecting reliable structural models.
  • The method aids in advancing structural biology applications by improving model selection accuracy.