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Recent studies have shown competitive performance in protein design that aims to find the amino acid sequence folding into the desired structure.
Molecular technology: designing proteins and peptides
Carl Pabo · 1983
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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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The limits of protein sequence comparison?
William R Pearson and Michael L Sierk · 2005
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3d representations of amino acids—applications to protein sequence comparison and classification
Jie Li and Patrice Koehl · 2014
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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
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Spherical convolutions and their application in molecular modelling
Wouter Boomsma and Jes Frellsen · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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3d deep convolutional neural networks for amino acid environment similarity analysis
Wen Torng and Russ B Altman · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Design of metalloproteins and novel protein folds using variational autoencoders
Joe G Greener, Lewis Moffat, and David T Jones · 2018
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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
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Computational protein design with deep learning neural networks
Jingxue Wang, Huali Cao, John ZH Zhang, and Yifei Qi · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 2018
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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
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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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Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror · 2020
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De novo protein design for novel folds using guided conditional wasserstein generative adversarial networks
Mostafa Karimi, Shaowen Zhu, Yue Cao, and Yang Shen · 2020
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Densecpd: improving the accuracy of neural-network-based computational protein sequence design with densenet
Yifei Qi and John ZH Zhang · 2020
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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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Protein design via deep learning
Wenze Ding, Kenta Nakai, and Haipeng Gong · 2022
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Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design
Baldwin Dumortier, Antoine Liutkus, Clément Carré, and Gabriel Krouk · 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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Accurate and efficient protein sequence design through learning concise local environment of residues
Bin Huang, Tingwe Fan, Kaiyue Wang, Haicang Zhang, Chungong Yu, Shuyu Nie, Yangshuo Qi, Wei-Mou Zheng, Jian Han, Zheng Fan, et al · 2022
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Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
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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
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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
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Xenet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers
Jack B Maguire, Daniele Grattarola, Vikram Khipple Mulligan, Eugene Klyshko, and Hans Melo · 2021
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Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alexander Rives · 2021
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Structure-based protein design with deep learning
Sergey Ovchinnikov and Po-Ssu Huang · 2021
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 2022
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Rotamer-free protein sequence design based on deep learning and self-consistency
Yufeng Liu, Lu Zhang, Weilun Wang, Min Zhu, Chenchen Wang, Fudong Li, Jiahai Zhang, Houqiang Li, Quan Chen, and Haiyan Liu · 2022
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A deep se (3)-equivariant model for learning inverse protein folding
Matt McPartlon, Ben Lai, and Jinbo Xu · 2022
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Colabfold: making protein folding accessible to all
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger · 2022
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Equibind: Geometric deep learning for drug binding structure prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola · 2022
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Deep generative modeling for protein design
Alexey Strokach and Philip M Kim · 2022
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Helixfold: An efficient implementation of alphafold2 using paddlepaddle
Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, Dianhai Yu, Fan Wang, and Yanjun Ma · 2022
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High-resolution de novo structure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al · 2022
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Data-efficient protein 3d geometric pretraining via refinediff
Zhiyuan Chen, Zuobai Zhang, and Jian Tang · 2023
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Structure-informed language models are protein designers
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, and Quanquan Gu · 2023
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2023
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