DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology. The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.

Automated summary

DeepRank is a deep learning framework for data mining large sets of 3D protein-protein interfaces, enabling efficient training with millions of PPIs and supporting both classification and regression. By addressing challenges such as distinguishing biological versus crystallographic PPIs and ranking docking models, DeepRank proves to be competitive with or outperform state-of-the-art methods, demonstrating its versatility in structural biology research.