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Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
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Amino acid substitution matrices from protein blocks
Steven Henikoff and Jorja G Henikoff · 1992
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Imgt unique numbering for immunoglobulin and t cell receptor variable domains and ig superfamily v-like domains
Marie-Paule Lefranc, Christelle Pommié, Manuel Ruiz, Véronique Giudicelli, Elodie Foulquier, Lisa Truong, Valérie Thouvenin-Contet, and Gérard Lefranc · 2003
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Optcdr: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding
RJ Pantazes and Costas D Maranas · 2010
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Rosetta3: an object-oriented software suite for the simulation and design of macromolecules
Andrew Leaver-Fay, Michael Tyka, Steven M Lewis, Oliver F Lange, James Thompson, Ron Jacak, Kristian W Kaufman, P Douglas Renfrew, Colin A Smith, Will Sheffler, et al · 2011
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Cellular and molecular immunology E-book
Abul K Abbas, Andrew H Lichtman, and Shiv Pillai · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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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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Optmaven–a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes
Tong Li, Robert J Pantazes, and Costas D Maranas · 2014
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Abdesign: A n algorithm for combinatorial backbone design guided by natural conformations and sequences
Gideon D Lapidoth, Dror Baran, Gabriele M Pszolla, Christoffer Norn, Assaf Alon, Michael D Tyka, and Sarel J Fleishman · 2015
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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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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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Computational design of antibodies
Sharon Fischman and Yanay Ofran · 2018
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Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L Gaunt · 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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Graphrnn: A deep generative model for graphs
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
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Cross-neutralization of sars-cov-2 by a human monoclonal sars-cov antibody
Dora Pinto, Young-Jun Park, Martina Beltramello, Alexandra C Walls, M Alejandra Tortorici, Siro Bianchi, Stefano Jaconi, Katja Culap, Fabrizia Zatta, Anna De Marco, et al · 2020
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Fast and flexible design of novel proteins using graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
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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
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In silico proof of principle of machine learning-based antibody design at unconstrained scale
Rahmad Akbar, Philippe A Robert, Cédric R Weber, Michael Widrich, Robert Frank, Milena Pavlović, Lonneke Scheffer, Maria Chernigovskaya, Igor Snapkov, Andrei Slabodkin, et al · 2021
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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
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas WA Gebauer, Michael Gastegger, and Kristof T Schütt · 2019
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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas K Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
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Five computational developability guidelines for therapeutic antibody profiling
Matthew IJ Raybould, Claire Marks, Konrad Krawczyk, Bruck Taddese, Jaroslaw Nowak, Alan P Lewis, Alexander Bujotzek, Jiye Shi, and Charlotte M Deane · 2019
Cited alongside, same era.
Antibody-protein binding and conformational changes: identifying allosteric signalling pathways to engineer a better effector response
Mohammed M Al Qaraghuli, Karina Kubiak-Ossowska, Valerie A Ferro, and Paul A Mulheran · 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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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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When attention meets fast recurrence: Training language models with reduced compute
Tao Lei · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Cov-abdab: the coronavirus antibody database
Matthew IJ Raybould, Aleksandr Kovaltsuk, Claire Marks, and Charlotte M Deane · 2021
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Antibody design using lstm based deep generative model from phage display library for affinity maturation
Koichiro Saka, Taro Kakuzaki, Shoichi Metsugi, Daiki Kashiwagi, Kenji Yoshida, Manabu Wada, Hiroyuki Tsunoda, and Reiji Teramoto · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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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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