Protein interface prediction using graph convolutional networks - Fout, Alex, author; Byrd, Jonathon, author; Shariat, Basir, author; Ben-Hur, Asa, author (Colorado State University. Libraries, 2017)
Determining the interface between two interacting proteins can help illuminate cellular biology, improve our understanding of disease, and aid pharmaceutical research. Such determination is expensive and time consuming using wet-lab experiments, which has motivated the development of computational methods. Inspired by the success of deep learning in image processing and other application areas, we adapt convolutional neural networks to work with irregularly structured data, such as proteins. We construct a novel pairwise classification architecture which is trained and tested with data from the Docking Benchmark Dataset versions 4.0 and 5.0. This outperforms the existing state-of-the-art prediction method, PAIRpred.
105 - Alex M Fout
This poster was presented at the 2017 Colorado State University Graduate Student Showcase, 9 November 2017.
Keywords: proteins; protein interface; machine learning; neural networks; convolutional neural networks; graph structured data; graphs; deep learning