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We propose an energy-based model (EBM) of protein conformations that operates at atomic scale.
Medium-and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins
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CA McPhalen and MNG James · 1987
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Manfred J Sippl · 1990
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Fast and simple monte carlo algorithm for side chain optimization in proteins: application to model building by homology
Lisa Holm and Chris Sander · 1992
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A second generation force field for the simulation of proteins, nucleic acids, and organic molecules
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William L Jorgensen, David S Maxwell, and Julian Tirado-Rives · 1996
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Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool
Michael J Bower, Fred E Cohen, and Roland L Dunbrack Jr · 1997
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Cooperative hydrogen bonding and enzyme catalysis
Hong Guo and Dennis R Salahub · 1998
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All-atom empirical potential for molecular modeling and dynamics studies of proteins
Alex D MacKerell Jr, Donald Bashford, MLDR Bellott, Roland Leslie Dunbrack Jr, Jeffrey D Evanseck, Martin J Field, Stefan Fischer, Jiali Gao, H Guo, Sookhee Ha, et al · 1998
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Effective energy functions for protein structure prediction
Themis Lazaridis and Martin Karplus · 2000
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Structure of ccmg/dsbe at 1.14 å resolution: high-fidelity reducing activity in an indiscriminately oxidizing environment
Melissa A Edeling, Luke W Guddat, Renata A Fabianek, Linda Thöny-Meyer, and Jennifer L Martin · 2002
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Force field validation using protein side chain prediction
Matthew P Jacobson, George A Kaminski, Richard A Friesner, and Chaya S Rapp · 2002
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Design of a novel globular protein fold with atomic-level accuracy
Brian Kuhlman, Gautam Dantas, Gregory C Ireton, Gabriele Varani, Barry L Stoddard, and David Baker · 2003
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Pisces: a protein sequence culling server
G. Wang and Jr. R. L. Dunbrack · 2003
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On contrastive divergence learning
Miguel A Carreira-Perpinan and Geoffrey E Hinton · 2005
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Role of hydrogen bond networks and dynamics in positive and negative cooperative stabilization of a protein
Jasmina S Redzic and Bruce E Bowler · 2005
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Computational design of a single amino acid sequence that can switch between two distinct protein folds
Xavier I Ambroggio and Brian Kuhlman · 2006
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Potential energy functions for protein design
F Edward Boas and Pehr B Harbury · 2007
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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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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 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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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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De novo computational design of retro-aldol enzymes
Lin Jiang, Eric A Althoff, Fernando R Clemente, Lindsey Doyle, Daniela Röthlisberger, Alexandre Zanghellini, Jasmine L Gallaher, Jamie L Betker, Fujie Tanaka, Carlos F Barbas, et al · 2008
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Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family
Dennis R Livesay, Dang H Huynh, Sargis Dallakyan, and Donald J Jacobs · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Four small puzzles that Rosetta doesn’t solve
R. Das · 2011
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A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions
Maxim V Shapovalov and Roland L Dunbrack Jr · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, and Vijay S. Pande · 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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Rapid sampling of hydrogen bond networks for computational protein design
Jack B Maguire, Scott E Boyken, David Baker, and Brian Kuhlman · 2018
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AlphaFold: Using AI for scientific discovery, 12 2018
Andrew Senior, John Jumper, and Demis Hassabis · 2018
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Distance-based protein folding powered by deep learning
Jinbo Xu · 2018
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Unified rational protein engineering with sequence-only deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church · 2019
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End-to-end differentiable learning of protein structure
Mohammed AlQuraishi · 2019
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 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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Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2019
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Machine-learning-guided directed evolution for protein engineering
Kevin K. Yang, Zachary Wu, and Frances H. Arnold · 2019
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Computational protein design with deep learning neural networks
Jingxue Wang, Huali Cao, John Z. H. Zhang, and Yifei Qi · 2045
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