The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Unified rational protein engineering with sequence-based deep representation learning
Open accessPublished version of a preprint
Abstract
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“They also learn summary representations, powerful features that can be used to better capture sequence relationships and link sequence to function via transfer learning 27,28,32-35.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [28]
In the citing paper
“Since then, various language model-based protein embedding methods have been applied to these and other protein prediction problems through transfer learning, including protein phenotype prediction 28,32-34, residue-residue contact prediction 32,70, fold recognition 33, protein-protein 71,72 and protein-drug interaction prediction 35,73.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [28]
In the citing paper
“In recent years, deep learning architectures that were originally developed for natural language processing (NLP) tasks, such as translating a sentence from one language into another, have been applied successfully to the creation of numerical protein representations from amino acid sequences 17 , 29 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [29]
In the citing paper
“These fingerprints are used together with a 1900-dimensioanl enzyme representation, called UniRep vector 29 , as the input for a gradient boosted decision tree model 34 to predict the K M value for an enzyme-substrate pair.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · cited 1×
“We used basic agglomerative clustering (implemented in Python with sklearn 73 ) with the average linkage metric and Euclidean distance for all vector representations, and a precomputed Levenshtein distance matrix using the Python Levenshtein package for the sequence-only control.”
MethodBerman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · cited 4×
“We computed the full sequence of hidden states at every position of 3,720 single-domain proteins from the SCOP database 60 (for which we had secondary structure annotation for each of the positions obtained from the PDB 80 ).”
MethodInfluential(2008) · Journal of Machine Learning Research · Cited by 33,051
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251
Learning to Forget: Continual Prediction with LSTM
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) · Neural Computation · doi:10.1162/089976600300015015 · Cited by 6,122
NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations
Valiev, M., Bylaska, E. J., Govind, N., Kowalski, K., Straatsma, T. P., van Dam, H. J. J., Wang, D., Nieplocha, J., Aprà, E., Windus, T. L., et al. (2010) · Computer Physics Communications · doi:10.1016/j.cpc.2010.04.018 · Cited by 5,849
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
An Overview of Multi-Task Learning in Deep Neural NetworksPreprint
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 3,827
Molecular dynamics simulations of biomolecules
Karplus, M., & McCammon, J. (2002) · Nature Structural Biology · doi:10.1038/nsb0902-646 · Cited by 3,401
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Multiple Sequence Alignment Using ClustalW and ClustalX
Thompson, J., Gibson, T. J., & Higgins, D. G. (2002) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0203s00 · Cited by 2,989 · cited 1×
“Remaining sequences were then multiply aligned using ClustalW (gap open penalty = 10, gap extension penalty = 0.1) 78 .”
MethodInfluentialLinguistic Regularities in Continuous Space Word Representations
Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838
Learning to forget: continual prediction with LSTM
Gers, F. A. (1999) · International Conference on Artificial Neural Networks · doi:10.1049/cp:19991218 · Cited by 2,467
Insights into the phylogeny and coding potential of microbial dark matter
Rinke, C., Schwientek, P., Sczyrba, A., Ivanova, N. N., Anderson, I. J., Cheng, J.-F., Darling, A., Malfatti, S., Swan, B. K., Gies, E. A., et al. (2013) · Nature · doi:10.1038/nature12352 · Cited by 2,394
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928
Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life
Parks, D. H., Rinke, C., Chuvochina, M., Chaumeil, P.-A., Woodcroft, B. J., Evans, P. N., Hugenholtz, P., & Tyson, G. W. (2017) · Nature Microbiology · doi:10.1038/s41564-017-0012-7 · Cited by 1,882
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813
Protein Structure Prediction Using Rosetta
Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., & Baker, D. A. (2004) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(04)83004-0 · Cited by 1,754
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569
Understanding inverse document frequency: on theoretical arguments for IDF
Robertson, S. E. (2004) · Journal of Documentation · doi:10.1108/00220410410560582 · Cited by 1,550
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532
The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., & Baker, D. (2016) · Nature · doi:10.1038/nature19946 · Cited by 1,478
Exploring protein fitness landscapes by directed evolution
Romero, P. A., & Arnold, F. H. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2805 · Cited by 1,163
Stability of Protein Pharmaceuticals: An Update
Manning, M. C., Chou, D. K., Murphy, B. M., Payne, R. W., & Katayama, D. S. (2010) · Pharmaceutical Research · doi:10.1007/s11095-009-0045-6 · Cited by 1,140
Methods for the directed evolution of proteins
Packer, M. S., & Liu, D. R. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3927 · Cited by 1,002
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
Asgari, E., & Mofrad, M. R. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141287 · Cited by 833
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775
Scalable Bayesian Optimization Using Deep Neural NetworksPreprint
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N. R., Sundaram, N., Patwary, M. A., Prabhat, & Adams, R. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.05700 · Cited by 768 · cited 1×
“While the high dimensionality and discrete nature of sequence space make sequence search a challenging and unsolved problem, we are optimistic about how UniRep-powered models may combine with ongoing work in simulated annealing 3 , adaptive sampling 43 and Bayesian optimization 44–50 .”
BackgroundFox, N. K., Brenner, S. E., & Chandonia, J.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1240 · Cited by 762
The Growing and Glowing Toolbox of Fluorescent and Photoactive Proteins
Rodriguez, E. A., Campbell, R. E., Lin, J. Y., Lin, M. Z., Miyawaki, A., Palmer, A. E., Shu, X., Zhang, J., & Tsien, R. Y. (2016) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2016.09.010 · Cited by 651
Deep generative models of genetic variation capture the effects of mutations
Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601
Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., et al. (2016) · Nature · doi:10.1038/nature17995 · Cited by 591
Park, H., Bradley, P. H., Greisen, P. J., Liu, Y., Mulligan, V. K., Kim, D. E., Baker, D. A., & DiMaio, F. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00819 · Cited by 564
Improving catalytic function by ProSAR-driven enzyme evolution
Fox, R. J., Davis, S. C., Mundorff, E. C., Newman, L. M., Gavrilovic, V., Ma, S. K., Chung, L., Ching, C., Tam, S., Muley, S., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1286 · Cited by 540
Bommarius, A. S., & Paye, M. F. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60137d · Cited by 504
HOMSTRAD: A database of protein structure alignments for homologous families
Mizuguchi, K., Deane, C. M., Blundell, T. L., & Overington, J. P. (1998) · Protein Science · doi:10.1002/pro.5560071126 · Cited by 501
Global analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A., et al. (2017) · Science · doi:10.1126/science.aan0693 · Cited by 501
Zdobnov, E. M., Tegenfeldt, F., Kuznetsov, D., Waterhouse, R. M., Simão, F. A., Ioannidis, P., Seppey, M., Loetscher, A., & Kriventseva, E. V. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1119 · Cited by 438
Resurrecting ancient genes: experimental analysis of extinct molecules
Thornton, J. W. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1324 · Cited by 399
Diversity and Evolution of Coral Fluorescent Proteins
Alieva, N. O., Konzen, K. A., Field, S. F., Meleshkevitch, E. A., Hunt, M. E., Ramirez, V. B., Miller, D. J., Wiedenmann, J., Salih, A., & Matz, M. V. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002680 · Cited by 378
Epistasis as the primary factor in molecular evolution
Breen, M. S., Kemena, C., Vlasov, P. K., Notredame, C., & Kondrashov, F. A. (2012) · Nature · doi:10.1038/nature11510 · Cited by 374
End-to-End Differentiable Learning of Protein Structure
AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371
Self-citationDe novo design of a fluorescence-activating β-barrel
Dou, J., Vorobieva, A. A., Sheffler, W., Doyle, L. A., Park, H., Bick, M. J., Mao, B., Foight, G. W., Lee, M. Y., Gagnon, L. A., et al. (2018) · Nature · doi:10.1038/s41586-018-0509-0 · Cited by 351
Navigating the protein fitness landscape with Gaussian processes
Romero, P. A., Krause, A., & Arnold, F. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 346
Simon, J. R., Carroll, N. J., Rubinstein, M., Chilkoti, A., & López, G. P. (2017) · Nature Chemistry · doi:10.1038/nchem.2715 · Cited by 344
Protein tolerance to random amino acid change.
Guo, H., Choe, J., & Loeb, L. (2004) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0403255101 · Cited by 341
Learning to Generate Reviews and Discovering Sentiment
Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330
Engineering an allosteric transcription factor to respond to new ligands
Taylor, N. D., Garruss, A. S., Moretti, R., Chan, S., Arbing, M. A., Cascio, D., Rogers, J. K., Isaacs, F. J., Kosuri, S., Baker, D., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3696 · Cited by 328
Self-citationSTRUM: structure-based prediction of protein stability changes upon single-point mutation
Quan, L., Lv, Q., & Zhang, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw361 · Cited by 317
Learned protein embeddings for machine learning
Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316
Large-scale determination of previously unsolved protein structures using evolutionary information
Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V., & Baker, D. (2015) · eLife · doi:10.7554/elife.09248 · Cited by 222
OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy
Raghava, G. P. S., Searle, S. M. J., Audley, P. C., Barber, J. D., & Barton, G. J. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-47 · Cited by 213 · cited 3×
“To confirm our cluster assignments, we picked a small set of three cytochromeoxidase (1,2,3) families from OXBench, eight proteins in total.”
InfluentialQuantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data
Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., & Fowler, D. M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.11.003 · Cited by 210
Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument
Nutiu, R., Friedman, R. C., Luo, S., Khrebtukova, I., Silva, D., Li, R., Zhang, L., Schroth, G. P., & Burge, C. B. (2011) · Nature Biotechnology · doi:10.1038/nbt.1882 · Cited by 194
Multiplicative LSTM for sequence modellingPreprint
Krause, B., Lu, L., Murray, I., & Renals, S. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.07959 · Cited by 170 · cited 2×
“We considered the mLSTM 61 , LSTM 62 and gated recurrent unit 63 for candidate RNN representation learners.”
BackgroundMethodNGLview–interactive molecular graphics for Jupyter notebooks
Nguyen, H., Case, D. A., & Rose, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx789 · Cited by 164
ProteinNet: a standardized data set for machine learning of protein structure
AlQuraishi, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2932-0 · Cited by 147
Self-citationMultiplexed gene synthesis in emulsions for exploring protein functional landscapes
Plesa, C., Sidore, A. M., Lubock, N. B., Zhang, D., & Kosuri, S. (2018) · Science · doi:10.1126/science.aao5167 · Cited by 128
Deep Recurrent Neural Network for Protein Function Prediction from SequencePreprint
Liu, X. L. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/103994 · Cited by 120
Bedbrook, C. N., Yang, K., Rice, A. J., Gradinaru, V., & Arnold, F. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005786 · Cited by 113
Multiplex single-molecule interaction profiling of DNA-barcoded proteins
Gu, L., Li, C., Aach, J., HILL, D. E., Vidal, M., & Church, G. M. (2014) · Nature · doi:10.1038/nature13761 · Cited by 80
Self-citationJuárez, J. F., Lecube-Azpeitia, B., Brown, S. L., Johnston, C. D., & Church, G. M. (2018) · Nature Communications · doi:10.1038/s41467-018-05525-6 · Cited by 71 · cited 1×
“14), evoking deep protein design, similar to previous work with small molecules 48 and proteins 43 .”
ResultSelf-citationProtein remote homology detection based on bidirectional long short-term memory
Li, S., Chen, J., & Liu, B. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1842-2 · Cited by 65
Computational protein design: a review
Coluzza, I. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa5c76 · Cited by 54
Motif kernel generated by genetic programming improves remote homology and fold detection
Håndstad, T., Hestnes, A. J., & Sætrom, P. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-23 · Cited by 51
Unified rational protein engineering with sequence-only deep representation learningPreprint
Alley, E. C., Khimulya, G., Biswas, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/589333 · Cited by 50
Self-citationThe Future of Multiplexed Eukaryotic Genome Engineering
Thompson, D. B., Aboulhouda, S., Hysolli, E., Smith, C. J., Wang, S., Castanon, O., & Church, G. M. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00842 · Cited by 40
Self-citationKwon, W. S., Silva, N. A. D., & Kellis, J. T. (1996) · Protein Engineering Design and Selection · doi:10.1093/protein/9.12.1197 · Cited by 37
End-to-end differentiable learning of protein structurePreprint
AlQuraishi, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/265231 · Cited by 33 · cited 9×
“Instead of using the predicted structures of the RGN, we extracted the internal state learned by the model for each sequence + PSSM combination (3,200 dimensions corresponding to the last outputs of two bidirectional LSTMs, each comprised 800 units per direction).”
BackgroundMethodInfluentialSelf-citationA Model of Substitution Trajectories in Sequence Space and Long-Term Protein Evolution
Usmanova, D. R., Luca Ferretti, A., Povolotskaya, I., Vlasov, P. K., & Kondrashov, F. A. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu318 · Cited by 32
Toward machine-guided design of proteinsPreprint
Biswas, S., Kuznetsov, G., Ogden, P. J., Conway, N. J., Adams, R. P., & Church, G. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/337154 · Cited by 29 · cited 7×
“…representation learning for proteins via RNNs. Unlike other approaches to representing proteins, namely as one-hot-encoded matrices as in Biswas et al. 3 , RNNs produce fixed-length representations for arbitrary-length proteins by extracting the hidden state passed forward along a sequence.”
BackgroundResultInfluentialSelf-citationDeep Semantic Protein Representation for Annotation, Discovery, and EngineeringPreprint
Schwartz, A. S., Hannum, G., Dwiel, Z., Smoot, M. E., Grant, A. R., Knight, J. M., Becker, S. A., Eads, J. R., LaFave, M. C., Eavani, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/365965 · Cited by 25
Experimental assay of a fitness landscape on a macroevolutionary scalePreprint
Pokusaeva, V. O., Usmanova, D., Putintseva, E., Espinar, L., Sarkisyan, K., Mishin, A., Bogatyreva, N. S., Ivankov, D., Akopyan, A., Avvakumov, S. Y., et al. (2017) · bioRxiv · doi:10.1101/222778 · Cited by 12
Soft Ngram Representation and Modeling for Protein Remote Homology Detection
Lovato, P., Cristani, M., & Bicego, M. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2595575 · Cited by 9
Doan, A., Halevy, A. Y., & Ives, Z. G. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-416044-6.00004-1 · Cited by 8
Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes
Chua, S.-L., & Foo, L. K. (2017) · Sensors · doi:10.3390/s17081902 · Cited by 6
De novo design of a fluorescence-activating beta barrel - BB1
Dou, J., Vorobieva, A. A., Sheffler, W., Doyle, L. A., Park, H., Bick, M. J., Mao, B., Foight, G., Lee, M., Carter, L., et al. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb6d0t/pdb · Cited by 5
tlambert03/FPbase: Initial Release
Lambert, T., & Bot, P. I. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1244328 · Cited by 3
Research works printed but not linked: 8
- Brookes, D. H., Park, H. & Listgarten, J. Conditioning by adaptive sampling for robust design. Proc. Machine Learn. Res. 97, 773–782 (2019).
- Hernández-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O. & Aspuru-Guzik, A. Parallel and distributed thompson sampling for large-scale accelerated exploration of chemical space. Preprint at arXiv https://arxiv.org/abs/1706.01825 (2017). · arXiv
- Snoek, J., Larochelle, H. & Adams, R. P. in Advances in Neural Information Processing Systems Vol. 25 (eds. Pereira, F. et al.) 2951–2959 (Curran Associates, Inc., 2012).
- Griffiths, R.-R. & Hernández-Lobato, J. M. Constrained Bayesian optimization for automaticchemical design. Preprint at arXiv https://arxiv.org/abs/1709.05501 (2017). · arXiv
- Yang, K. K., Chen, Y., Lee, A. & Yue, Y. Batched stochastic Bayesian optimization via combinatorial constraints design. Preprint at arXiv https://arxiv.org/abs/1904.08102 (2019). · arXiv
- González, J., Longworth, J., James, D. C. & Lawrence, N. D. Bayesian optimization for synthetic gene design. Preprint at arXiv https://arxiv.org/abs/1505.01627 (2015). · arXiv
- Jouppi, N. P. et al. In-datacenter performance analysis of a tensorprocessing unit. In Proc. 44th Annual International Symposium of Computer Architecture Vol. 45, 1–12 (ACM, 2017).
- Salimans, T. & Kingma, D. P. Weight normalization: a simple reparameterization to accelerate training of deep neural networks. Preprint at arXiv https://arxiv.org/abs/1602.07868 (2016). · arXiv
Cited by
Sorted by citations
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
“Alley et al. also made strides in the area of protein analysis, creating statistical summaries that capture key properties of proteins and help neural networks learn with less data 43 .”
BackgroundSignalP 6.0 predicts all five types of signal peptides using protein language models
Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01156-3 · Cited by 3,088
Metadata
- Authors
- Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Church, George M.
- Venue
- Nature Methods
- Published
- 2019-10-21
- Volume
- 16
- Issue
- 12
- Pages
- 1315-1322
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7067682
- OpenAlex
- W2980789587
- Cited by
- 1,131
- References
- 80
- Citations by year
- 2026: 130 · 2025: 170 · 2024: 201 · 2023: 197 · 2022: 188 · 2021: 159 · 2020: 69 · 2019: 17
- FWCI
- 35.71
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Amazon Web Services Cloud Credits for Research
- Center for Effective Altruism
- U.S. Department of Health & Human Services | National Institutes of Health (T32HG002295, U54-CA225088)
- National Science Foundation GRFP Fellowship
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (P50GM107618)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- rational protein design, protein stability prediction, de novo protein design, protein mutants, recurrent neural networks, protein function prediction
- MeSH terms
- Deep Learning, Amino Acid Sequence, Mutation, Protein Engineering, Protein Stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
