Fetching the paper…
Reading the bibliography…
We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs).
Long-range, small magnitude nonadditivity of mutational effects in proteins
V J LiCata and G K Ackers · 1995
Earlier work this paper cites.
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
S F Altschul, T L Madden, A A Schäffer, J Zhang, Z Zhang, W Miller, and D J Lipman · 1997
Earlier work this paper cites.
Significant speedup of database searches with HMMs by search space reduction with PSSM family models
Michael Beckstette, Robert Homann, Robert Giegerich, and Stefan Kurtz · 2009
Earlier work this paper cites.
Small insertions and deletions (INDELs) in human genomes
Julienne M Mullaney, Ryan E Mills, W Stephen Pittard, and Scott E Devine · 2010
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.
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.
Protein structure prediction from sequence variation
Debora S Marks, Thomas A Hopf, and Chris Sander · 2012
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.
Epistasis in protein evolution
Tyler N Starr and Joseph W Thornton · 2016
Earlier work this paper cites.
Potts hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness
Ronald M Levy, Allan Haldane, and William F Flynn · 2017
Earlier work this paper cites.
Mutation effects predicted from sequence co-variation
Thomas A Hopf, John B Ingraham, Frank J Poelwijk, Charlotta P I Schärfe, Michael Springer, Chris Sander, and Debora S Marks · 2017
Cited alongside, same era.
An improved escherichia coli screen for rubisco identifies a protein-protein interface that can enhance CO2-fixation kinetics
Robert H Wilson, Elena Martin-Avila, Carly Conlan, and Spencer M Whitney · 2018
Cited alongside, same era.
Deep generative models of genetic variation capture the effects of mutations
Adam J Riesselman, John B Ingraham, and Debora S Marks · 2018
Cited alongside, same era.
Machine-learning-guided directed evolution for protein engineering
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2019
Cited alongside, same era.
Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
Cited alongside, same era.
Progen: Language modeling for protein generation
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.
An evolution-based model for designing chorismate mutase enzymes
William Russ, Matteo Figliuzzi, Christian Stocker, Pierre Barrat-Charlaix, and Peter Socolich · 2020
Later among the works it cites.
Protein engineering in the design of protein–protein interactions: Sars-cov-2 inhibitors as a test case
Jiří Zahradník and Gideon Schreiber · 2021
Later among the works it cites.
Learning the protein language: Evolution, structure, and function
Tristan Bepler and Bonnie Berger · 2021
Later among the works it cites.
ProtTrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, and Burkhard Rost · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
Cited alongside, same era.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Cited alongside, same era.
Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
Cited alongside, same era.
Design by adaptive sampling
David Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
Cited alongside, same era.
Fitness effects of single amino acid insertions and deletions in tem-1 beta-lactamase
Courtney E. Gonzalez, Paul Roberts, and Marc Ostermeier · 2019
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
Later among the works it cites.
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
Later among the works it cites.
Learning the language of viral evolution and escape
Brian Hie, Ellen D Zhong, Bonnie Berger, and Bryan Bryson · 2021
Later among the works it cites.
Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alexander Rives · 2021
Later among the works it cites.
Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alexander Rives · 2021
Later among the works it cites.