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Proteins are macromolecules that perform essential functions in all living organisms.
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch and Christian Sander · 1983
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Basic local alignment search tool
Stephen F Altschul, Warren Gish, Webb Miller, Eugene W Myers, and David J Lipman · 1990
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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
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Quaternions and rotations
Yan-Bin Jia · 2008
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Structure quality and target parameters
RA Engh and R Huber · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Scientific benchmarks for guiding macromolecular energy function improvement
Andrew Leaver-Fay, Matthew J O’Meara, Mike Tyka, Ron Jacak, Yifan Song, Elizabeth H Kellogg, James Thompson, Ian W Davis, Roland A Pache, Sergey Lyskov, et al · 2013
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Sabdab: the structural antibody database
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M Deane · 2014
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The coming of age of de novo protein design
Po-Ssu Huang, Scott E Boyken, and David Baker · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Rosettaantibodydesign (rabd): A general framework for computational antibody design
Jared Adolf-Bryfogle, Oleks Kalyuzhniy, Michael Kubitz, Brian D Weitzner, Xiaozhen Hu, Yumiko Adachi, William R Schief, and Roland L Dunbrack Jr · 2018
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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De novo design of a fluorescence-activating β \beta -barrel
Jiayi Dou, Anastassia A Vorobieva, William Sheffler, Lindsey A Doyle, Hahnbeom Park, Matthew J Bick, Binchen Mao, Glenna W Foight, Min Yen Lee, Lauren A Gagnon, et al · 2018
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Computational design of antibodies
Sharon Fischman and Yanay Ofran · 2018
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De novo design of self-assembling helical protein filaments
Hao Shen, Jorge A Fallas, Eric Lynch, William Sheffler, Bradley Parry, Nicholas Jannetty, Justin Decarreau, Michael Wagenbach, Juan Jesus Vicente, Jiajun Chen, et al · 2018
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
Cited alongside, same era.
Protein design and variant prediction using autoregressive generative models
Jung-Eun Shin, Adam J Riesselman, Aaron W Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C Kruse, and Debora S Marks · 2021
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Deep learning methods for designing proteins scaffolding functional sites
Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Ivan Anishchenko, Minkyung Baek, Joseph L Watson, Jung Ho Chun, Lukas F Milles, Justas Dauparas, et al · 2021
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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Protein sequence design with a learned potential
Namrata Anand, Raphael Eguchi, Irimpan I Mathews, Carla P Perez, Alexander Derry, Russ B Altman, and Po-Ssu Huang · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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Design of proteins presenting discontinuous functional sites using deep learning
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, Ivan Anishchenko, Lukas F Milles, Sergey Ovchinnikov, and David Baker · 2020
Cited alongside, same era.
Improved protein structure prediction using predicted interresidue orientations
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker · 2020
Cited alongside, same era.
De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J Pellock, Tamuka M Chidyausiku, Theresa A Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K Bera, et al · 2021
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al · 2021
Cited alongside, same era.
Iterative refinement graph neural network for antibody sequence-structure co-design
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola · 2021
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
Cited alongside, same era.
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Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation
Raphael R. Eguchi, Christian A Choe, and Po-Ssu Huang · 2022
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Protgpt2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clement Vignac, and Max Welling · 2022
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Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives · 2022
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Conditional antibody design as 3d equivariant graph translation
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
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An end-to-end deep learning method for rotamer-free protein side-chain packing
Matt McPartlon and Jinbo Xu · 2022
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Antibody structure prediction using interpretable deep learning
Jeffrey A Ruffolo, Jeremias Sulam, and Jeffrey J Gray · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2022
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Scannet: An interpretable geometric deep learning model for structure-based protein binding site prediction
Jérôme Tubiana, Dina Schneidman-Duhovny, and Haim J Wolfson · 2022
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Foldseek: fast and accurate protein structure search
Michel van Kempen, Stephanie Kim, Charlotte Tumescheit, Milot Mirdita, Johannes Söding, and Martin Steinegger · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Direct molecular conformation generation
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, et al · 2022
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