Predicting protein-protein interactions through sequence-based deep learning.

Motivation: High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.

Results: We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone.

NETAL: a new graph-based method for global alignment of protein-protein interaction networks.

Motivation: The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment.