ABSTRACT
Motivation Computational methods for the prediction of protein-protein interactions, while important tools for researchers, are plagued by challenges in generalising to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases.
Results In this study, we introduce RAPPPID, a method for the Regularised Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin AWD-LSTM network which employs multiple regularisation methods during training time to learn generalised weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID’s performance holds regardless of the particular proteins in the testing set and its performance is higher for biologically supported edges. This study serves to demonstrate that appropriate regularisation is an important component of overcoming the challenges of creating models for protein-protein interaction prediction that generalise to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future. Availability and Implementation: Code and datasets are freely available at https://github.com/jszym/rapppid.
Contact amin.emad{at}mcgill.ca
Supplementary Information Online-only supplementary data is available at the journal’s website.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated to address reviewer comments. Among the changes, the following sections were added: - Negative Examples - Effect of different components on RAPPPID's performance - Transfer Learning on Protein-Ligand Data from X-Ray Crystallography Experiments - RAPPPID predicts interaction of HER2 with Trastuzumab and Pertuzumab As well as the following: - Additional information regarding hyper-parameter selection - Additional information regarding the false-positive rate - Analysis on the relative degree of proteins - Statistics on the dataset - Details regarding training time - Discussion on the availability of high-quality datasets appropriate for training deep methods on the PPI prediction task
