Growing ecosystem of deep learning methods for modeling protein-protein interactions

Area of Science:

• Computational biology
• Biophysics
• Machine learning

Background:

• Protein-protein interactions are crucial for cellular functions but challenging to characterize due to diverse recognition mechanisms.
• Existing methods struggle with the complexity and scale of mapping the entire proteome's interactions.

Approach:

• This review explores the landscape of deep learning (DL) approaches for modeling protein interactions.
• We highlight DL methods that leverage experimental data and biophysical knowledge for enhanced prediction accuracy.
• Specific DL techniques discussed include representation learning, geometric deep learning, and generative modeling.

Key Points:

• Representation learning captures complex features for predicting protein interactions and binding sites.
• Geometric deep learning analyzes protein structures to predict complex formations.
• Generative models are being used for de novo design of protein assemblies.

Conclusions:

• Deep learning offers powerful tools to discover novel protein interactions and elucidate their physical underpinnings.
• These methods can be used to engineer proteins that modulate cellular functions.
• Advancements in DL promise a deeper understanding of how protein interactions drive cellular behavior.