The DOI matches its Crossref record; 88 of the 91 references it cites were found in a registry or an index.
Protein language model embeddings for fast, accurate, alignment-free protein structure prediction
Open accessPreprint — published as 10.1016/j.str.2022.05.001
Abstract
All state-of-the-art (SOTA) protein structure predictions rely on evolutionary information captured in multiple sequence alignments (MSAs), primarily on evolutionary couplings (co-evolution). Such information is not available for all proteins and is computationally expensive to generate. Prediction models based on Artificial Intelligence (AI) using only single sequences as input are easier and cheaper but perform so poorly that speed becomes irrelevant. Here, we described the first competitive AI solution exclusively inputting embeddings extracted from pre-trained protein Language Models (pLMs), namely from the transformer pLM ProtT5, from single sequences into a relatively shallow (few free parameters) convolutional neural network (CNN) trained on inter-residue distances, i.e. protein structure in 2D. The major advance originated from processing the attention heads learned by ProtT5. Although these models required at no point any MSA, they matched the performance of methods relying on co-evolution. Although not reaching the very top, our lean approach came close at substantially lower costs thereby speeding up development and each future prediction. By generating protein-specific rather than family-averaged predictions, these new solutions could distinguish between structural features differentiating members of the same family of proteins with similar structure predicted alike by all other top methods.
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.
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826
Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844
Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct
Pereira, R., Martin, M., Roussel, A., Carribault, P., & Gautier, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40744-4_5 · Cited by 13,143
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840
UniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378
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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · cited 9×
“…using no other annotation than the amino acid sequences of 10^7-10^9 proteins (Alley et al., 2019; Asgari & Mofrad, 2015; Bepler & Berger, 2019, 2021; Elnaggar et al., 2021; Heinzinger et al., 2019; Madani et al., 2020; Ofer et al., 2021; Rao et al., 2019; Rives et al., 2021; Wu et al., 2021).”
BackgroundMethodInfluentialSelf-citationUniRef 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
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421
Deep mutational scanning: a new style of protein science
Fowler, D. M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3027 · Cited by 1,287
Malard, F., Wolter, A. C., Marquevielle, J., Morvan, E., Ecoutin, A., Rüdisser, S., Allain, F. H. ‐., & Campagne, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae201 · Cited by 1,284
HH-suite3 for fast remote homology detection and deep protein annotation
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144
Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive
Burley, S. K., Berman, H. M., Kleywegt, G. J., Markley, J. L., Nakamura, H., & Velankar, S. (2017) · Methods in Molecular Biology · doi:10.1007/978-1-4939-7000-1_26 · Cited by 1,141
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · cited 2×
“…methods predicting aspects of protein structure and function without any further optimization of the pLM using a second step of supervised training (Alley et al., 2019; Asgari & Mofrad, 2015; Elnaggar et al., 2021; Heinzinger et al., 2019; Madani et al., 2020; Rao et al., 2019; Rives et al.,…”
MethodAccurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931
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
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., Bansal, P., Bridge, A., Poux, S., Bougueleret, L., & Xenarios, I. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3167-5_2 · Cited by 824
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 787
PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta
Chaudhury, S., Lyskov, S., & Gray, J. J. (2010) · Bioinform. · doi:10.1093/bioinformatics/btq007 · Cited by 772
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Mirdita, M., von den Driesch, L., Galiez, C., Martin, M. J., Söding, J., & Steinegger, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1081 · Cited by 726
Evaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719
Protein structure prediction from sequence variation
Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622
Modeling aspects of the language of life through transfer-learning protein sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606
Self-citationDeep 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
A large‐scale experiment to assess protein structure prediction methods
Moult, J., Pedersen, J. T., Judson, R., & Fidelis, K. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230303 · Cited by 555
Critical assessment of methods of protein structure prediction (CASP)—Round XIII
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25823 · Cited by 506
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483
Structure determination of the small ubiquitin-related modifier SUMO-1
Bayer, P., Arndt, A., Metzger, S., Mahajan, R. K., Melchior, F., Jaenicke, R., & Becker, J. D. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.1839 · Cited by 467
CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
Seemayer, S., Gruber, M., & Söding, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu500 · Cited by 416
ProGen: Language Modeling for Protein GenerationPreprint
Madani, A., McCann, B., Naik, N., Keskar, N. S., Anand, N., Eguchi, R. R., Huang, P., & Socher, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.07.982272 · Cited by 383
The language of proteins: NLP, machine learning & protein sequences
Ofer, D., Brandes, N., & Linial, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.022 · Cited by 365
Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Fehér, T., Angerer, C. M., Steinegger, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06225 · Cited by 364
Self-citationRao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350
Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345
Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341
Critical assessment of methods of protein structure prediction (CASP)—Round XII
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25415 · Cited by 325
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316
The EVcouplings Python framework for coevolutionary sequence analysis
Hopf, T. A., Green, A. G., Schubert, B., Mersmann, S., Schärfe, C., Ingraham, J., Tóth-Petróczy, Á., Brock, K. P., Riesselman, A. J., Palmedo, P., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty862 · Cited by 286
Learning protein sequence embeddings using information from structure
Bepler, T., & Berger, B. (2019) · arXiv · doi:10.48550/arxiv.1902.08661 · Cited by 219
Lysine activation and functional analysis of E2-mediated conjugation in the SUMO pathway
Yunus, A. A., & Lima, C. D. (2006) · Nature Structural & Molecular Biology · doi:10.1038/nsmb1104 · Cited by 201
A framework for exhaustively mapping functional missense variants
Weile, J., Sun, S., Cote, A. G., Knapp, J., Verby, M., Mellor, J. C., Wu, Y., Pons, C., Wong, C., van Lieshout, N., et al. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177908 · Cited by 187
Type 9 secretion system structures reveal a new protein transport mechanism
Lauber, F., Deme, J. C., Lea, S. M., & Berks, B. C. (2018) · Nature · doi:10.1038/s41586-018-0693-y · Cited by 186
Origins of coevolution between residues distant in protein 3D structures
Anishchenko, I., Ovchinnikov, S., Kamisetty, H., & Baker, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702664114 · Cited by 185
Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 183
Zhang, C., Zheng, W. H., Mortuza, S. M., Li, Y., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz863 · Cited by 178
Embeddings from deep learning transfer GO annotations beyond homology
Littmann, M., Heinzinger, M., Dallago, C., Olenyi, T., & Rost, B. (2021) · Scientific Reports · doi:10.1038/s41598-020-80786-0 · Cited by 176
Self-citationNMR Structure of the Integral Membrane Protein OmpX
Fernández, C. F., Hilty, C., Wider, G., Güntert, P., & Kurt Wüthrich, A. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.09.014 · Cited by 175
De novo protein design by citizen scientists
Koepnick, B., Flatten, J., Husain, T., Ford, A., Silva, D., Bick, M. J., Bauer, A., Liu, G., Ishida, Y., Boykov, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1274-4 · Cited by 169
Regional Polysterism in the GTP-Bound Form of the Human c-Ha-Ras Protein,
Ito, Y., Yamasaki, K., Iwahara, J., Terada, T., Kamiya, A., Shirouzu, M., Muto, Y., Kawai, G., Yokoyama, S., Laue, E. D., et al. (1997) · Biochemistry · doi:10.1021/bi970296u · Cited by 163
Protein sequence design with deep generative models
Wu, Z., Johnston, K. E., Arnold, F. H., & Yang, K. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.004 · Cited by 152
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
Light attention predicts protein location from the language of life
Stärk, H., Dallago, C., Heinzinger, M., & Rost, B. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab035 · Cited by 130
Self-citationDeconstruction of the Ras switching cycle through saturation mutagenesis
Bandaru, P., Shah, N. H., Bhattacharyya, M., Barton, J. P., Kondo, Y., Cofsky, J. C., Gee, C. L., Chakraborty, A. K., Kortemme, T., Ranganathan, R., et al. (2017) · eLife · doi:10.7554/elife.27810 · Cited by 117
Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets
Dallago, C., Schütze, K., Heinzinger, M., Olenyi, T., Littmann, M., Lu, A. X., Yang, K., Min, S., Yoon, S., Morton, J. T., et al. (2021) · Current Protocols · doi:10.1002/cpz1.113 · Cited by 109
Self-citationUguzzoni, G., Lovis, S. J., Oteri, F., Schug, A. H., Szurmant, H., & Weigt, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615068114 · Cited by 108
rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments
Mirabello, C., & Wallner, B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0220182 · Cited by 105
Progress of 1D protein structure prediction at last
Rost, B., & Sander, C. (1995) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340230304 · Cited by 93
Self-citationMavor, D., Barlow, K., Thompson, S., Barad, B. A., Bonny, A. R., Cario, C. L., Gaskins, G., Liu, Z., Deming, L., Axen, S. D., et al. (2016) · eLife · doi:10.7554/elife.15802 · Cited by 89
Arnold, T., Graewert, M. A., Nußberger, S., Lupas, A. N., & Linke, D. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.12.029 · Cited by 85
Ju, F., Zhu, J., Shao, B., Kong, L., Liu, T., Zheng, W., & Bu, D. (2021) · Nature Communications · doi:10.1038/s41467-021-22869-8 · Cited by 84
Protein Folding Rates Estimated from Contact Predictions
Punta, M., & Rost, B. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.02.068 · Cited by 81
Self-citationRNA Structural Determinants of Optimal Codons Revealed by MAGE-Seq
Kelsic, E. D., Chung, H., Cohen, N., Park, J., Wang, H. H., & Kishony, R. (2016) · Cell Systems · doi:10.1016/j.cels.2016.11.004 · Cited by 70
Method of the Year: protein structure prediction
Marx, V. (2022) · Nature Methods · doi:10.1038/s41592-021-01359-1 · Cited by 66
Coordinating the impact of structural genomics on the human α-helical transmembrane proteome
Pieper, U., Schlessinger, A., Kloppmann, E., Chang, G. A., Chou, J. J., Dumont, M. E., Fox, B. G., Fromme, P., Hendrickson, W. A., Malkowski, M. G., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2508 · Cited by 60
Self-citationCrystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8
Flower, T. G., & Hurley, J. H. (2021) · Protein Science · doi:10.1002/pro.4050 · Cited by 49
Plant Bioinformatics: Methods and Protocols
Edwards, D. (2015) · Cited by 40
pH-Dependent Mechanisms of Influenza Infection Mediated by Hemagglutinin
Caffrey, M. S., & Lavie, A. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.777095 · Cited by 39
Structural genomics plucks high-hanging membrane proteins
Kloppmann, E., Punta, M., & Rost, B. (2012) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.05.002 · Cited by 34
Self-citationSingle Layers of Attention Suffice to Predict Protein ContactsPreprint
Bhattacharya, N., Thomas, N., Rao, R., Dauparas, J., Koo, P. K., Baker, D. A., Song, Y. S., & Ovchinnikov, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.423882 · Cited by 33
Jain, A., Terashi, G., Kagaya, Y., Subramaniya, S. R. M. V., Christoffer, C. W., & Kihara, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-87204-z · Cited by 30
Clustering FunFams using sequence embeddings improves EC purity
Littmann, M., Bordin, N., Heinzinger, M., Schütze, K., Dallago, C., Orengo, C. A., & Rost, B. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab371 · Cited by 27
Self-citationManne, K., Chattopadhyay, D., Agarwal, V., Blom, A. M., Khare, B., Chakravarthy, S., Chang, C., Ton‐That, H., & Narayana, S. V. L. (2020) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798320008116 · Cited by 10
Kim, D., Kang, S., Lee, K.-Y., Jang, S., Kang, S., & Lee, B. (2016) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2016.10.009 · Cited by 8
Cited by
Sorted by citations
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
High-resolution de novo structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392
Bilingual language model for protein sequence and structure
Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae150 · Cited by 311
Self-citationBefore and after AlphaFold2: An overview of protein structure prediction
Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 330
Fine-tuning protein language models boosts predictions across diverse tasks
Schmirler, R., Heinzinger, M., & Rost, B. (2024) · Nature Communications · doi:10.1038/s41467-024-51844-2 · Cited by 229
Self-citation
Metadata
- Authors
- Weissenow, K., Heinzinger, M., & Rost, B.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Weissenow, Konstantin
- Venue
- Structure
- Publisher
- openRxiv
- Published
- 2021-08-02
- Volume
- 30
- Issue
- 8
- Pages
- 1169-1177.e4
- ISSN
- 0969-2126 (ISSN-L)
- OpenAlex
- W4281291878
- Cited by
- 139
- References
- 91
- Citations by year
- 2026: 21 · 2025: 30 · 2024: 34 · 2023: 30 · 2022: 19 · 2021: 5
- FWCI
- 9.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Structure (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Alexander von Humboldt-Stiftung
- Nvidia
- Deutsche Forschungsgemeinschaft (DFG-GZ: RO1320/4-1, RO1320/4\u20131, RO1320/4‐1)
- Bundesministerium für Bildung und Forschung
- 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 › Structural Biology › Enzyme Structure and Function
- Keywords
- protein structure prediction, protein language models, ProtT5, attention heads, convolutional neural networks, protein engineering, deep mutational scanning, coevolution
- MeSH terms
- Artificial Intelligence, Language, Proteins, Sequence Alignment, Computational Biology
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
