Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model

Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions

Gregory B Gloor, Louise C Martin, Lindi M Wahl, and Stanley D Dunn · 2005

Earlier work this paper cites.

Modular protein engineering in emerging cancer therapies

Esther Vazquez, Neus Ferrer-Miralles, Ramon Mangues, Jose L Corchero, Simo Schwartz Jr, Antonio Villaverde, et al · 2009

Earlier work this paper cites.

Learning generative models for protein fold families

Sivaraman Balakrishnan, Hetunandan Kamisetty, Jaime G Carbonell, Su-In Lee, and Christopher James Langmead · 2011

Earlier work this paper cites.

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Faruck Morcos, Andrea Pagnani, Bryan Lunt, Arianna Bertolino, Debora S Marks, Chris Sander, Riccardo Zecchina, José N Onuchic, Terence Hwa, and Martin Weigt · 2011

Earlier work this paper cites.

Predicting the functional impact of protein mutations: application to cancer genomics

Boris Reva, Yevgeniy Antipin, and Chris Sander · 2011

Earlier work this paper cites.

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

David T Jones, Daniel WA Buchan, Domenico Cozzetto, and Massimiliano Pontil · 2012

Earlier work this paper cites.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

Timothy A Whitehead, Aaron Chevalier, Yifan Song, Cyrille Dreyfus, Sarel J Fleishman, Cecilia De Mattos, Chris A Myers, Hetunandan Kamisetty, Patrick Blair, Ian A Wilson, et al · 2012

Earlier work this paper cites.

Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models

Magnus Ekeberg, Cecilia Lövkvist, Yueheng Lan, Martin Weigt, and Erik Aurell · 2013

Earlier work this paper cites.

Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era

Hetunandan Kamisetty, Sergey Ovchinnikov, and David Baker · 2013

Earlier work this paper cites.

The fitness landscape of hiv-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing

Jaclyn K Mann, John P Barton, Andrew L Ferguson, Saleha Omarjee, Bruce D Walker, Arup Chakraborty, and Thumbi Ndung’u · 2014

Earlier work this paper cites.

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

Luciano G Martelotto, Charlotte KY Ng, Maria R De Filippo, Yan Zhang, Salvatore Piscuoglio, Raymond S Lim, Ronglai Shen, Larry Norton, Jorge S Reis-Filho, and Britta Weigelt · 2014

Earlier work this paper cites.

CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

Stefan Seemayer, Markus Gruber, and Johannes Söding · 2014

Earlier work this paper cites.

Unexpected features of the dark proteome

Nelson Perdigão, Julian Heinrich, Christian Stolte, Kenneth S. Sabir, Michael J. Buckley, Bruce Tabor, Beth Signal, Brian S. Gloss, Christopher J. Hammang, Burkhard Rost, Andrea Schafferhans, and Seán I. O’Donoghue · 2015

Earlier work this paper cites.

Evfold. org: Evolutionary couplings and protein 3d structure prediction

Robert Sheridan, Robert J Fieldhouse, Sikander Hayat, Yichao Sun, Yevgeniy Antipin, Li Yang, Thomas Hopf, Debora S Marks, and Chris Sander · 2015

Earlier work this paper cites.

Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature

Jeffrey I Boucher, Daniel NA Bolon, and Dan S Tawfik · 2016

Earlier work this paper cites.

Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase tem-1

Matteo Figliuzzi, Hervé Jacquier, Alexander Schug, Oliver Tenaillon, and Martin Weigt · 2016

Earlier work this paper cites.

Mutation effects predicted from sequence co-variation

Thomas A Hopf, John B Ingraham, Frank J Poelwijk, Charlotta PI Schärfe, Michael Springer, Chris Sander, and Debora S Marks · 2017

Earlier work this paper cites.

Deep generative models of genetic variation capture mutation effects

Original

Adam J Riesselman, John B Ingraham, and Debora S Marks · 2017

Cited alongside, same era.

Attention is all you need

Original

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017

Cited alongside, same era.

Accurate de novo prediction of protein contact map by ultra-deep learning model

Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu · 2017

Cited alongside, same era.

How pairwise coevolutionary models capture the collective residue variability in proteins?

Matteo Figliuzzi, Pierre Barrat-Charlaix, and Martin Weigt · 2018

Cited alongside, same era.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

David T Jones and Shaun M Kandathil · 2018

Single layers of attention suffice to predict protein contacts

Nicholas Bhattacharya, Neil Thomas, Roshan Rao, Justas Daupras, Peter Koo, David Baker, Yun S Song, and Sergey Ovchinnikov · 2020

Later among the works it cites.

Learning mutational semantics

Brian Hie, Ellen Zhong, Bryan Bryson, and Bonnie Berger · 2020

Later among the works it cites.

Self-supervised contrastive learning of protein representations by mutual information maximization

Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, and Alan M Moses · 2020

Later among the works it cites.

ProGen: Language modeling for protein generation

Original

Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher · 2020

Later among the works it cites.

Transforming the language of life: transformer neural networks for protein prediction tasks

Ananthan Nambiar, Maeve Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, and Anna Ritz · 2020

Later among the works it cites.