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Poster
Generative Models for Graph-Based Protein Design
John Ingraham · Vikas Garg · Regina Barzilay · Tommi Jaakkola

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #80
in Applications -- Computational Biology and Bioinformatics »

Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. In this work, we introduce a conditional generative model for protein sequences given 3D structures based on graph representations. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. This graph-based approach improves in both speed and reliability over conventional and other neural network-based approaches, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

Keywords: Attention Models · Generative Models · Deep Learning · Applications -> Computational Biology and Bioinformatics; Deep Learning
[ Paper]  [ Slides] 

Author Information

John Ingraham (MIT)
Vikas Garg (MIT)
Regina Barzilay (Massachusetts Institute of Technology)
Tommi Jaakkola (MIT)

Tommi Jaakkola is a professor of Electrical Engineering and Computer Science at MIT. He received an M.Sc. degree in theoretical physics from Helsinki University of Technology, and Ph.D. from MIT in computational neuroscience. Following a Sloan postdoctoral fellowship in computational molecular biology, he joined the MIT faculty in 1998. His research interests include statistical inference, graphical models, and large scale modern estimation problems with predominantly incomplete data.

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