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The linear sequence of amino acids determines protein structure and function.
“Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles,”
Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, and Yaoqi Zhou, · 2014
Earlier work this paper cites.
“Exploring the repeat protein universe through computational protein design,”
TJ Brunette, Fabio Parmeggiani, Po-Ssu Huang, Gira Bhabha, Damian C Ekiert, Susan E Tsutakawa, Greg L Hura, John A Tainer, and David Baker, · 2015
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.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“Spin2: Predicting sequence profiles from protein structures using deep neural networks,”
James O’Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, and Yaoqi Zhou, · 2018
Earlier work this paper cites.
“De novo design of bioactive protein switches,”
Robert A Langan, Scott E Boyken, Andrew H Ng, Jennifer A Samson, Galen Dods, Alexandra M Westbrook, Taylor H Nguyen, Marc J Lajoie, Zibo Chen, Stephanie Berger, et al., · 2019
Earlier work this paper cites.
“Critical assessment of methods of protein structure prediction (casp)—round xiii,”
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, and John Moult, · 2019
Cited alongside, same era.
“Machine-learning-guided directed evolution for protein engineering,”
Kevin K Yang, Zachary Wu, and Frances H Arnold, · 2019
Cited alongside, same era.
“Generative models for graph-based protein design,”
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola, · 2019
Cited alongside, same era.
“To improve protein sequence profile prediction through image captioning on pairwise residue distance map,”
Sheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, and Yuedong Yang, · 2019
Cited alongside, same era.
“Deep learning in protein structural modeling and design,”
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray, · 2020
Cited alongside, same era.
“Modeling global and local node contexts for text generation from knowledge graphs,”
Leonardo FR Ribeiro, Yue Zhang, Claire Gardent, and Iryna Gurevych, · 2020
Later among the works it cites.
“Progen: Language modeling for protein generation,”
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher, · 2020
Later among the works it cites.
“Prodconn: Protein design using a convolutional neural network,”
Yuan Zhang, Yang Chen, Chenran Wang, Chun-Chao Lo, Xiuwen Liu, Wei Wu, and Jinfeng Zhang, · 2020
Later among the works it cites.
“Learning from protein structure with geometric vector perceptrons,”
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror, · 2021
Later among the works it cites.
“Fold2seq: A joint sequence(1d)-fold(3d) embedding-based generative model for protein design,”
Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, and Yang Shen, · 2021
Later among the works it cites.
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Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M. Kim, · 2020
Cited alongside, same era.
“Mol* viewer: modern web app for 3d visualization and analysis of large biomolecular structures,”
David Sehnal, Sebastian Bittrich, Mandar Deshpande, Radka Svobodová, Karel Berka, Václav Bazgier, Sameer Velankar, Stephen K Burley, Jaroslav Koča, and Alexander S Rose, · 2021
Later among the works it cites.