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Protein inverse folding-that is, predicting an amino acid sequence that will fold into the desired 3D structure-is an important problem for structure-based protein design.
Amino acid substitution matrices from protein blocks
S. Henikoff and J. G. Henikoff · 1992
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Inverse protein folding problem: designing polymer sequences
Kaizhi Yue and Ken A Dill · 1992
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PISCES: a protein sequence culling server
Guoli Wang and Jr Dunbrack, Roland L · 2003
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Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2004
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How significant is a protein structure similarity with tm-score= 0.5?
Jinrui Xu and Yang Zhang · 2010
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Gearnet: Grammatical evolution with artificial regulatory networks
Rui L Lopes and Ernesto Costa · 2013
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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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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2019
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Format and geometries matter: Structure-based design defines the functionality of bispecific antibodies
Steffen Dickopf, Guy J Georges, and Ulrich Brinkmann · 2020
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A novel score for highly accurate and efficient prediction of native protein structures
Lu-yun Wu, Xia-yu Xia, and Xian-ming Pan · 2020
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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, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Design of protein-binding proteins from the target structure alone
Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, William Sheffler, Joon Sung Park, Kevin M Jude, Iva Marković, Rameshwar U Kadam, Koen HG Verschueren, et al · 2022
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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
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Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Pablo Chacón, and Stan Z Li · 2022
Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics
Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Lirong Wu, and Stan Z. Li · 2023
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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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Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization
Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, et al · 2024
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Training diffusion models with reinforcement learning, 2024
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2024
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Evaluating generalizability of artificial intelligence models for molecular datasets
Yasha Ektefaie, Andrew Shen, Daria Bykova, Maximillian Marin, Marinka Zitnik, and Maha Farhat · 2024
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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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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, Nikita Smetanin, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 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, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola · 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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Structured denoising diffusion models in discrete state-spaces, 2023
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2023
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Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
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From sequence to function through structure: Deep learning for protein design
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, and Christian Dallago · 2023
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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
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Alphafold protein structure database
European Bioinformatics Institute (EMBL-EBI) · 2024
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Reinforcement learning for fine-tuning text-to-image diffusion models
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KW-design: Pushing the limit of protein design via knowledge refinement
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https://huggingface.co/facebook/esmfold_v1
Facebook AI Research · 2024
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Feedback efficient online fine-tuning of diffusion models, 2024
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Fast and accurate protein structure search with foldseek
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