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Encoding the space of protein-protein binding interfaces by artificial intelligence.

Zhaoqian Su1, Kalyani Dhusia2, Yinghao Wu2

  • 1Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN 37212, USA.

Computational Biology and Chemistry
|April 21, 2024
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Artificial intelligence can predict protein-protein interactions by learning structural features of binding interfaces. This machine learning approach helps assemble protein complexes into native conformations, aiding in the discovery of new interactions.

Keywords:
Artificial intelligenceComplexesProtein-protein interactionsStructural modeling of protein

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Area of Science:

  • Structural biology
  • Computational biology
  • Artificial intelligence in bioinformatics

Background:

  • Protein-protein interactions are crucial for cellular functions.
  • The structural properties of protein binding interfaces dictate interaction specificity.
  • Identifying and predicting these interactions is a key challenge in molecular biology.

Purpose of the Study:

  • To investigate if artificial intelligence can capture structural properties of protein-protein binding interfaces.
  • To develop a machine learning method for predicting protein complex conformations.
  • To explore the degeneracy of conformational space at protein-protein binding interfaces.

Main Methods:

  • Decomposition of protein-protein binding interfaces into interacting fragment pairs.
  • Utilizing a generative model to encode interface fragment pairs in a latent space.
  • Generating novel conformations of interface fragment pairs using the trained model.

Main Results:

  • Generated artificial intelligence interface fragment pairs guided protein complex assembly into native conformations.
  • Demonstrated high degeneracy in the conformational space of protein-protein binding interface fragment pairs.
  • Showcased that artificial intelligence can effectively characterize features within this degenerate space.

Conclusions:

  • The conformational space of protein-protein binding interfaces is highly degenerate and learnable by AI.
  • AI-driven characterization of interface features can predict protein complex conformations.
  • This machine learning method offers a potential tool for discovering and predicting unknown protein-protein interactions.