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|a Ingraham, John
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Garg, Vikas Kamur
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|a Barzilay, Regina
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|a Jaakkola, Tommi S
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|a Generative models for graph-based protein design
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|b Neural Information Processing Systems Foundation, Inc.,
|c 2021-09-09T14:43:09Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/129731.2
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|a 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.
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|a en
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|a Article
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|t Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
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