Research graph
References from From sequence to function through structure: Deep learning for protein design. Local targets link to admitted publications; unresolved targets remain external evidence.
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10.1073/pnas.1616903114
10.1073/pnas.1616903114 · External reference
10.1038/s41586-022-04599-z
10.1038/s41586-022-04599-z · External reference
FunFam protein families improve residue level molecular function prediction
10.1186/s12859-019-2988-x · 2019 · External reference
10.1016/j.jmb.2021.167160
10.1016/j.jmb.2021.167160 · External reference
10.1093/protein/15.10.779
10.1093/protein/15.10.779 · External reference
10.1080/01621459.1949.10483310
10.1080/01621459.1949.10483310 · External reference
10.1038/s41580-019-0163-x
10.1038/s41580-019-0163-x · External reference
OpenFold
2021 · External reference
10.1038/s41586-021-03819-2
10.1038/s41586-021-03819-2 · External reference
High-resolution de novo structure prediction from primary sequence
2022 · External reference
10.1126/science.abm4805
10.1126/science.abm4805 · External reference
10.1093/nar/gkab1061
10.1093/nar/gkab1061 · External reference
Protein sequence design with a learned potential
10.1038/s41467-022-28313-9 · 2022 · External reference
10.1101/2022.06.03.494563
10.1101/2022.06.03.494563 · External reference
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10.1038/s41467-022-32007-7
10.1038/s41467-022-32007-7 · External reference
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Design in the DARK: learning deep generative models for De Novo protein design
2022 · External reference
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10.1038/s41592-019-0496-6
10.1038/s41592-019-0496-6 · External reference
10.3390/ijms222111741
10.3390/ijms222111741 · External reference
10.1109/tpami.2021.3095381
10.1109/tpami.2021.3095381 · External reference
10.1016/j.sbi.2016.03.006
10.1016/j.sbi.2016.03.006 · External reference
Direct-coupling analysis of residue coevolution captures native contacts across many protein families
10.1073/pnas.1111471108 · 2011 · External reference
10.1146/annurev.biochem.77.062906.171838
10.1146/annurev.biochem.77.062906.171838 · External reference
10.1038/nature19946
10.1038/nature19946 · External reference
10.1126/science.2382133
10.1126/science.2382133 · External reference
10.1126/science.8446897
10.1126/science.8446897 · External reference
10.1126/science.abm1183
10.1126/science.abm1183 · External reference
10.1038/s41586-019-1923-7
10.1038/s41586-019-1923-7 · External reference
Critical assessment of methods of protein structure prediction (CASP)—Round XIII
10.1002/prot.25823 · 2019 · External reference
A watershed moment for protein structure prediction
10.1038/d41586-019-03951-0 · 2020 · External reference
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · 2021 · External reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · 2019 · External reference
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
2022 · External reference
TMbed: transmembrane proteins predicted through language model embeddings
10.1186/s12859-022-04873-x · 2022 · External reference
Embeddings from protein language models predict conservation and variant effects
2021 · External reference
SETH predicts nuances of residue disorder from protein embeddings
2022 · External reference
Embeddings from deep learning transfer GO annotations beyond homology
10.1038/s41598-020-80786-0 · 2021 · External reference
Contrastive learning on protein embeddings enlightens midnight zone
10.1093/nargab/lqac043 · 2022 · External reference
Light attention predicts protein location from the language of life
2021 · External reference
Protein embeddings and deep learning predict binding residues for various ligand classes
10.1038/s41598-021-03431-4 · 2021 · External reference
10.1093/nar/gkac278
10.1093/nar/gkac278 · External reference
10.1093/nar/gkac439
10.1093/nar/gkac439 · External reference
Controllable protein design with language models
10.1038/s42256-022-00499-z · 2022 · External reference
10.1101/2022.03.04.483009
10.1101/2022.03.04.483009 · External reference
The generative capacity of probabilistic protein sequence models
10.1038/s41467-021-26529-9 · 2021 · External reference
10.1371/journal.pcbi.1008085
10.1371/journal.pcbi.1008085 · External reference
10.1016/j.sbi.2016.11.004
10.1016/j.sbi.2016.11.004 · External reference
PDBench: evaluating computational methods for protein sequence design
2021 · External reference
10.1002/prot.24620
10.1002/prot.24620 · External reference
10.1002/prot.25489
10.1002/prot.25489 · External reference
10.1021/acs.jcim.9b00438
10.1021/acs.jcim.9b00438 · External reference
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10.1002/prot.25868
10.1002/prot.25868 · External reference
10.1109/cvpr.2017.243
10.1109/cvpr.2017.243 · External reference
10.1021/acs.jcim.0c00043
10.1021/acs.jcim.0c00043 · External reference
10.1007/s10867-021-09593-6
10.1007/s10867-021-09593-6 · External reference
TIMED-design: efficient protein sequence design with deep learning
2022 · External reference
TERMinator: A neural framework for structure-based protein design using tertiary repeating motifs
2022 · External reference
10.1101/2022.08.02.501736
10.1101/2022.08.02.501736 · External reference
10.1016/j.cels.2020.08.016
10.1016/j.cels.2020.08.016 · External reference
Rotamer-free protein sequence design based on deep learning and self-consistency
2022 · External reference
Masked inverse folding with sequence transfer for protein representation learning
2022 · External reference
Accurate and efficient protein sequence design through learning concise local environment of residues
2022 · External reference
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Protein sequence sampling and prediction from structural data
2021 · External reference
10.1101/2022.04.15.488492
10.1101/2022.04.15.488492 · External reference
Fold2Seq: A joint sequence (1D)-Fold (3D) embedding-based generative model for protein design
2021 · External reference
10.1101/2022.04.10.487779
10.1101/2022.04.10.487779 · External reference
PiFold: Toward effective and efficient protein inverse folding
2022 · External reference
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10.1021/acs.jctc.7b00125
10.1021/acs.jctc.7b00125 · External reference
10.1371/journal.pcbi.1010271
10.1371/journal.pcbi.1010271 · External reference
10.1101/2022.07.09.499440
10.1101/2022.07.09.499440 · External reference
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network
2020 · External reference
Generating tertiary protein structures via interpretable graph variational autoencoders
10.1093/bioadv/vbab036 · 2021 · External reference
10.1038/s41586-021-04383-5
10.1038/s41586-021-04383-5 · External reference
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ProteinSGM: Score-based generative modeling for de novo protein design
2022 · External reference
Protein structure generation via folding diffusion
2022 · External reference
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10.1016/j.csbj.2021.03.022 · Admitted local publication
10.1038/s41592-019-0598-1
10.1038/s41592-019-0598-1 · External reference
10.1038/s41592-021-01100-y
10.1038/s41592-021-01100-y · External reference
10.1038/s42256-021-00310-5
10.1038/s42256-021-00310-5 · External reference
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UniProt: the universal protein knowledgebase in 2021
10.1093/nar/gkaa1100 · 2021 · External reference
Deep neural language modeling enables functional protein generation across families
2021 · External reference
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10.1038/s41586-021-04043-8
10.1038/s41586-021-04043-8 · External reference
10.1038/s42256-022-00532-1
10.1038/s42256-022-00532-1 · External reference
10.1038/s41586-021-04184-w
10.1038/s41586-021-04184-w · External reference
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Design of proteins presenting discontinuous functional sites using deep learning
2020 · External reference
Protein sequence design by conformational landscape optimization
10.1073/pnas.2017228118 · 2021 · External reference
10.1126/science.abn2100
10.1126/science.abn2100 · External reference
10.1126/science.abj8754
10.1126/science.abj8754 · External reference
State-of-the-art estimation of protein model accuracy using AlphaFold
2022 · External reference
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10.1101/2022.08.04.502750
10.1101/2022.08.04.502750 · External reference
ColabFold: making protein folding accessible to all
10.1038/s41592-022-01488-1 · 2022 · External reference
10.1101/2022.02.07.479398
10.1101/2022.02.07.479398 · External reference
10.3389/fbioe.2019.00050
10.3389/fbioe.2019.00050 · External reference
10.1093/nar/gky995
10.1093/nar/gky995 · External reference
10.1101/2022.07.22.500861
10.1101/2022.07.22.500861 · External reference
10.1021/acs.jnatprod.5b01055
10.1021/acs.jnatprod.5b01055 · External reference
10.1016/j.cell.2020.12.020
10.1016/j.cell.2020.12.020 · External reference
Jasmonate suppresses seedling soil emergence in Arabidopsis thaliana
10.1080/15592324.2017.1330239 · 2017 · External reference
10.1126/science.aat0572
10.1126/science.aat0572 · External reference
10.1016/j.sbi.2016.01.004
10.1016/j.sbi.2016.01.004 · External reference
10.1073/pnas.2006560117
10.1073/pnas.2006560117 · External reference
10.1021/jacs.7b07939
10.1021/jacs.7b07939 · External reference
10.1158/2159-8290.cd-21-0260
10.1158/2159-8290.cd-21-0260 · External reference
10.1093/nar/gkz882
10.1093/nar/gkz882 · External reference
Complementation of the Saccharomyces cerevisiae plasma membrane H+-ATPase by a plant H+-ATPase generates a highly abundant fusicoccin binding site
10.1074/jbc.273.45.30018 · 1998 · External reference
The 14–3-3 protein interacts directly with the C-terminal region of the plant plasma membrane H(+)-ATPase
1997 · External reference
The surprising story of fusicoccin: A wilt-inducing phytotoxin, a tool in plant physiology and a 14-3-3-targeted drug
10.3390/biom11091393 · 2021 · External reference
10.1021/ar960017f
10.1021/ar960017f · External reference
Multivalent designed proteins protect against SARS-CoV-2 variants of concern
2021 · External reference
10.1002/3527600647.ch10
10.1002/3527600647.ch10 · 2002 · External reference
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2022 · External reference
The automated lab
10.1038/516131a · 2014 · External reference
An operating system for the biology lab
10.1038/d41586-019-02875-z · 2019 · External reference
Cloud labs: where robots do the research
10.1038/d41586-022-01618-x · 2022 · External reference
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Make more digital twins
10.1038/d41586-019-02849-1 · 2019 · External reference
Digital twins: the convergence of multimedia technologies
10.1109/mmul.2018.023121167 · 2018 · External reference
10.1038/s44161-022-00044-6
10.1038/s44161-022-00044-6 · External reference
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
10.1186/s13059-019-1835-8 · 2019 · External reference
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Constructing benchmark test sets for biological sequence analysis using independent set algorithms
10.1371/journal.pcbi.1009492 · 2022 · External reference
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