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Human protein interactome structure prediction at scale with Boltz-2.

Alexander M Ille1,2, Christopher Markosian1,2, Stephen K Burley3,4,5,6,7

  • 1Rutgers Cancer Institute, Newark, NJ, USA.

Biorxiv : the Preprint Server for Biology
|July 9, 2025
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Summary

Artificial intelligence models like Boltz-2 can now predict human protein interaction structures. This study used Boltz-2 to model 1,394 protein complexes, offering new insights into biological functions and diseases.

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Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein-protein interactions are crucial for human biology and disease.
  • Existing databases catalog many interactions, but structural data is limited.
  • Experimental structure determination is challenging for large-scale interactome analysis.

Purpose of the Study:

  • To assess the utility of the AI model Boltz-2 for predicting human protein interaction structures.
  • To generate 1,394 de novo structural models of binary human protein interactions.
  • To explore the functional and disease-related implications of these predicted structures.

Main Methods:

  • Utilized Boltz-2, an AI/ML model, for protein complex structure prediction.
  • Sourced interaction data from the IntAct database for 1,394 binary human protein interactions.
  • Assessed prediction confidence using structural metrics and multiple sequence alignment (MSA) depth.
  • Analyzed protein domains within predicted complexes and their proximity to interaction interfaces.

Main Results:

  • Successfully generated 1,394 predicted human protein interaction structures.
  • Prediction confidence was higher for smaller complexes and improved with increased MSA depth.
  • Identified 679 complexes with protein domains potentially involved in interactions.
  • Revealed complex interaction networks relevant to biological function and cancer.

Conclusions:

  • Boltz-2 is a valuable tool for in silico structural modeling of the human protein interactome.
  • The study highlights the strengths and limitations of AI-driven structure prediction.
  • Generated models provide novel functional contextualization and insights for biomedical research.