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Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design.
Cath–a hierarchic classification of protein domain structures
Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thornton · 1997
Earlier work this paper cites.
On the relationship between sequence and structure similarities in proteomics
Evgeny Krissinel · 2007
Earlier work this paper cites.
Sequence co-evolution gives 3d contacts and structures of protein complexes
Thomas A Hopf, Charlotta PI Schärfe, João PGLM Rodrigues, Anna G Green, Oliver Kohlbacher, Chris Sander, Alexandre MJJ Bonvin, and Debora S Marks · 2014
Earlier work this paper cites.
The coming of age of de novo protein design
Po-Ssu Huang, Scott E Boyken, and David Baker · 2016
Earlier work this paper cites.
Epistasis in protein evolution
Tyler N Starr and Joseph W Thornton · 2016
Earlier work this paper cites.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
Earlier work this paper cites.
Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes, 2021
Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, and Chris G. Willcocks · 2021
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons, 2021
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, and Ron Dror · 2021
Cited alongside, same era.
Robust deep learning–based protein sequence design using proteinmpnn
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker · 2022
Cited alongside, same era.
Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, and Stan Z Li · 2022
Cited alongside, same era.
Learning inverse folding from millions of predicted structures
Protein structure generation via folding diffusion
Kevin Eric Wu, Kevin K Yang, Rianne van den Berg, James Zou, Alex Xijie Lu, and Ava P Amini · 2022
Later among the works it cites.
Masked inverse folding with sequence transfer for protein representation learning
Kevin K. Yang, Hugh Yeh, and Niccolò Zanichelli · 2022
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, and Alexander Rives · 2023
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Improved vector quantized diffusion models, 2023
Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives · 2022
Cited alongside, same era.
Hallucinating symmetric protein assemblies
BIM Wicky, LF Milles, A Courbet, RJ Ragotte, J Dauparas, E Kinfu, S Tipps, RD Kibler, M Baek, F DiMaio, et al · 2022
Cited alongside, same era.
Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, and Yu Guang Wang · 2023
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