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Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen · 1903
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PyTorch: An imperative style, high-performance deep learning library
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Interscaffolding additivity. association of p1 variants of eglin c and of turkey ovomucoid third domain with serine proteinases
MA Qasim, Philip J Ganz, Charles W Saunders, Katherine S Bateman, Michael NG James, and Michael Laskowski · 1997
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The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
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Recombinant staphylokinase variants with reduced antigenicity due to elimination of b-lymphocyte epitopes
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PyMOL: An open-source molecular graphics tool
Warren L DeLano et al · 2002
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Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2004
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The FoldX web server: an online force field
Joost Schymkowitz, Jesper Borg, Francois Stricher, Robby Nys, Frederic Rousseau, and Luis Serrano · 2005
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Global distribution of conformational states derived from redundant models in the pdb points to non-uniqueness of the protein structure
Prasad V Burra, Ying Zhang, Adam Godzik, and Boguslaw Stec · 2009
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror · 2009
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On the binding affinity of macromolecular interactions: daring to ask why proteins interact
Panagiotis L Kastritis and Alexandre MJJ Bonvin · 2012
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SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models
Iain H Moal and Juan Fernández-Recio · 2012
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HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Michael Remmert, Andreas Biegert, Andreas Hauser, and Johannes Söding · 2012
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BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
Yves Dehouck, Jean Marc Kwasigroch, Marianne Rooman, and Dimitri Gilis · 2013
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Targeting protein–protein interactions as an anticancer strategy
Andrei A Ivanov, Fadlo R Khuri, and Haian Fu · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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PCalign: a method to quantify physicochemical similarity of protein-protein interfaces
Shanshan Cheng, Yang Zhang, and Charles L Brooks · 2015
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Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
Thom Vreven, Iain H Moal, Anna Vangone, Brian G Pierce, Panagiotis L Kastritis, Mieczyslaw Torchala, Raphael Chaleil, Brian Jiménez-García, Paul A Bates, Juan Fernandez-Recio, et al · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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BindProfX: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts
Peng Xiong, Chengxin Zhang, Wei Zheng, and Yang Zhang · 2016
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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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Flex ddG: Rosetta ensemble-based estimation of changes in protein–protein binding affinity upon mutation
Kyle A. Barlow, Shane Ó Conchúir, Samuel Thompson, Pooja Suresh, James E. Lucas, Markus Heinonen, and Tanja Kortemme · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Topology independent structural matching discovers novel templates for protein interfaces
Claudio Mirabello and Björn Wallner · 2018
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Generalized extracellular molecule sensor platform for programming cellular behavior
Leo Scheller, Tobias Strittmatter, David Fuchs, Daniel Bojar, and Martin Fussenegger · 2018
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PyTorch Lightning, Mar 2019
William Falcon and The PyTorch Lightning team · 2019
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SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation
Justina Jankauskaitė, Brian Jiménez-García, Justas Dapkūnas, Juan Fernández-Recio, and Iain H Moal · 2019
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GEMME: a simple and fast global epistatic model predicting mutational effects
Elodie Laine, Yasaman Karami, and Alessandra Carbone · 2019
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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
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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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Prop3D: A flexible, python-based platform for machine learning with protein structural properties and biophysical data
Eli J. Draizen, Luis Felipe R. Murillo, John Readey, Cameron Mura, and Philip E. Bourne · 2022
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World Stroke Organization (WSO): global stroke fact sheet 2022
Valery L Feigin, Michael Brainin, Bo Norrving, Sheila Martins, Ralph L Sacco, Werner Hacke, Marc Fisher, Jeyaraj Pandian, and Patrice Lindsay · 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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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2022
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Calculation of accurate interatomic contact surface areas for the quantitative analysis of non-bonded molecular interactions
Judemir Ribeiro, Carlos Ríos-Vera, Francisco Melo, and Andreas Schüller · 2019
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A structure-based deep learning framework for protein engineering
Raghav Shroff, Austin W Cole, Barrett R Morrow, Daniel J Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D Ellington, and Ross Thyer · 2019
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End-to-end learning on 3d protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
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Graphein-a python library for geometric deep learning and network analysis on protein structures and interaction networks
Arian R Jamasb, Ramon Viñas, Eric J Ma, Charlie Harris, Kexin Huang, Dominic Hall, Pietro Lió, and Tom L Blundell · 2020
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Macromolecular modeling and design in rosetta: recent methods and frameworks
Julia Koehler Leman, Brian D Weitzner, Steven M Lewis, Jared Adolf-Bryfogle, Nawsad Alam, Rebecca F Alford, Melanie Aprahamian, David Baker, Kyle A Barlow, Patrick Barth, et al · 2020
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Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials
Haiying Lu, Qiaodan Zhou, Jun He, Zhongliang Jiang, Cheng Peng, Rongsheng Tong, and Jianyou Shi · 2020
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Computational design of novel protein–protein interactions–an overview on methodological approaches and applications
Anthony Marchand, Alexandra K Van Hall-Beauvais, and Bruno E Correia · 2022
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Computer-aided engineering of staphylokinase toward enhanced affinity and selectivity for plasmin
Dmitri Nikitin, Jan Mican, Martin Toul, David Bednar, Michaela Peskova, Patricia Kittova, Sandra Thalerova, Jan Vitecek, Jiri Damborsky, Robert Mikulik, et al · 2022
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Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena Hurtado, Aidan N Gomez, Debora Marks, and Yarin Gal · 2022
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Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization
Sisi Shan, Shitong Luo, Ziqing Yang, Junxian Hong, Yufeng Su, Fan Ding, Lili Fu, Chenyu Li, Peng Chen, Jianzhu Ma, et al · 2022
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US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes
Chengxin Zhang, Morgan Shine, Anna Marie Pyle, and Yang Zhang · 2022
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De novo design of protein interactions with learned surface fingerprints
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DiffDock-PP: Rigid protein-protein docking with diffusion models
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Rotamer density estimator is an unsupervised learner of the effect of mutations on protein-protein interaction
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