  Predicting protein-protein interactions (PPIs) by learning informative
representations from amino acid sequences is a challenging yet important
problem in biology. Although various deep learning models in Siamese
architecture have been proposed to model PPIs from sequences, these methods are
computationally expensive for a large number of PPIs due to the pairwise
encoding process. Furthermore, these methods are difficult to interpret because
of non-intuitive mappings from protein sequences to their sequence
representation. To address these challenges, we present a novel deep framework
to model and predict PPIs from sequence alone. Our model incorporates a
bidirectional gated recurrent unit to learn sequence representations by
leveraging contextualized and sequential information from sequences. We further
employ a sparse regularization to model long-range dependencies between amino
acids and to select important amino acids (protein motifs), thus enhancing
interpretability. Besides, the novel design of the encoding process makes our
model computationally efficient and scalable to an increasing number of
interactions. Experimental results on up-to-date interaction datasets
demonstrate that our model achieves superior performance compared to other
state-of-the-art methods. Literature-based case studies illustrate the ability
of our model to provide biological insights to interpret the predictions.
