
PPIscreenML, a machine learning classifier trained to distinguish AlphaFold2 models of interacting protein pairs from compelling decoy pairings, outperforms existing methods for identifying protein-protein interactions based on structural information.


coremsg

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

### Competing Interest Statement

The authors have declared no competing interest.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

leveraging-alphafold2-to-develop-a-machine-learning-classifier-for-identifying-protein-protein-interactions


Leveraging AlphaFold2 to Develop a Machine Learning Classifier for Identifying Protein-Protein Interactions
