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.
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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 · see citing works
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 48,293 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 13,140 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
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 5,542 · see citing works
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 · see citing works
UniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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× · see citing works
“…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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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. S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7000-1_26 · Cited by 1,192 · see citing works
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 · see citing works
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 · see citing works
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× · see citing works
“…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 · see citing works
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 · see citing works
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 817 · see citing works
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 · see citing works
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
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 · see citing works
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 710 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 446 · see citing works
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 · see citing works
Rao, 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 · see citing works
Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345 · see citing works
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 345 · see citing works
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 · see citing works
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 327 · see citing works
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 · see citing works
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 305 · see citing works
Self-citationThe 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 · see citing works
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 282 · see citing works
Learning protein sequence embeddings using information from structure
Bepler, T., & Berger, B. (2019) · arXiv · doi:10.48550/arxiv.1902.08661 · Cited by 219 · see citing works
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 202 · see citing works
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 · see citing works
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 · see citing works
Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 184 · see citing works
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 184 · see citing works
Zhang, C., Zheng, W. H., Mortuza, S. M., Li, Y., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz863 · Cited by 182 · see citing works
NMR 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 · see citing works
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 169 · see citing works
Self-citationRegional 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 · see citing works
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 158 · see citing works
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 141 · see citing works
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 141 · see citing works
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 · see citing works
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 · see citing works
Uguzzoni, 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 · see citing works
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 · see citing works
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 101 · see citing works
Self-citationProgress 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 69 · see citing works
Method of the Year: protein structure prediction
Marx, V. (2022) · Nature Methods · doi:10.1038/s41592-021-01359-1 · Cited by 62 · see citing works
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 · see citing works
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 · see citing works
Plant Bioinformatics: Methods and Protocols
Edwards, D. (2015) · Cited by 40 · see citing works
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 · see citing works
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 · see citing works
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 31 · see citing works
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 29 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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
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Cited by
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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 5,523 · see citing works
High-resolution <i>de novo</i> 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 · see citing works
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 · see citing works
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 309 · see citing works
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 · see citing works
Self-citationEmbeddings from protein language models predict conservation and variant effects
(2021) · doi:10.1007/s00439-021-02411-y · Cited by 174 · see citing works
Self-citationPre-trained Language Models in Biomedical Domain: A Systematic Survey
Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2023) · ACM Computing Surveys · doi:10.1145/3611651 · Cited by 206 · see citing works
Fang, X., Wang, F., Liu, L., He, J., Lin, D., Xiang, Y., Zhu, K., Zhang, X., Wu, H., Li, H., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00721-6 · Cited by 132 · see citing works
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 · see citing works
Self-citationProtein embeddings and deep learning predict binding residues for various ligand classes
(2021) · doi:10.1038/s41598-021-03431-4 · Cited by 120 · see citing works
Self-citationTMbed: transmembrane proteins predicted through language model embeddings
Bernhofer, M., & Rost, B. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04873-x · Cited by 105 · see citing works
Self-citationContrastive learning on protein embeddings enlightens midnight zone
(2022) · doi:10.1093/nargab/lqac043 · Cited by 105 · see citing works
Self-citationAlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96 · see citing works
Improving protein succinylation sites prediction using embeddings from protein language model
Pokharel, S., Pratyush, P., Heinzinger, M., Newman, R. H., & KC, D. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-21366-2 · Cited by 86 · see citing works
Self-citationCodon language embeddings provide strong signals for use in protein engineering
Outeiral, C., & Deane, C. M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00791-0 · Cited by 87 · see citing works
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works
Self-citationDeep learning for protein structure prediction and design—progress and applications
Jänes, J., & Beltrão, P. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00016-x · Cited by 71 · see citing works
Computational drug development for membrane protein targets
Li, H., Sun, X., Cui, W., Xu, M., Dong, J., Ekundayo, B., Ni, D., Rao, Z., Guo, L., Stahlberg, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01987-2 · Cited by 62 · see citing works
Leveraging machine learning models for peptide–protein interaction prediction
Song, Y., Mi, X., & Shukla, D. (2024) · RSC Chemical Biology · doi:10.1039/d3cb00208j · Cited by 53 · see citing works
Chen, J., Wang, J., Hu, Y., Li, X., Qian, Y., & Song, C.-L. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1506508 · Cited by 60 · see citing works
Deep learning for protein secondary structure prediction: Pre and post-AlphaFold
Ismi, D. P., Pulungan, R., & Afiahayati (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.012 · Cited by 53 · see citing works
Tertiary structure assessment at CASP15
Simpkin, A. J., Mesdaghi, S., Rodríguez, F. S., Elliott, L. G., Murphy, D., Kryshtafovych, A., Keegan, R. M., & Rigden, D. J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26593 · Cited by 43 · see citing works
Are protein language models the new universal key?
Weißenow, K., & Rost, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102997 · Cited by 51 · see citing works
Self-citationNallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad029 · Cited by 47 · see citing works
Self-citationPrevalent bee venom genes evolved before the aculeate stinger and eusociality
Koludarov, I., Velasque, M., Senoner, T., Timm, T., Greve, C., Hamadou, A. B., Gupta, D. K., Lochnit, G., Heinzinger, M., Vilcinskas, A., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01656-5 · Cited by 44 · see citing works
Self-citationProtein structure prediction via deep learning: an in-depth review
Meng, Y., Zhang, Z., Zhou, C., Tang, X., Hu, X., Tian, G., Yang, J., & Yao, Y. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1498662 · Cited by 49 · see citing works
Nearest neighbor search on embeddings rapidly identifies distant protein relations
Schütze, K., Heinzinger, M., Steinegger, M., & Rost, B. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1033775 · Cited by 39 · see citing works
Self-citationParthiban, S., Vijeesh, T., Thashanamoorthi, G., Shanmugaraj, B., Sharma, A. K., & Ramalingam, S. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1252166 · Cited by 40 · see citing works
Pakhrin, S. C., Pokharel, S., Aoki-Kinoshita, K. F., Beck, M. R., Dam, T. K., Caragea, D., & KC, D. B. (2023) · Glycobiology · doi:10.1093/glycob/cwad033 · Cited by 39 · see citing works
Meng, Q., Guo, F., & Tang, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad217 · Cited by 36 · see citing works
Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily
Koludarov, I., Senoner, T., Jackson, T., Dashevsky, D., Heinzinger, M., Aird, S. D., & Rost, B. (2023) · Nature Communications · doi:10.1038/s41467-023-40550-0 · Cited by 38 · see citing works
Self-citationAlphaFold’s new rival? Meta AI predicts shape of 600 million proteins
Callaway, E. (2022) · Nature · doi:10.1038/d41586-022-03539-1 · Cited by 35 · see citing works
Pre-trained Language Models in Biomedical Domain: A Systematic SurveyPreprint
Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05006 · Cited by 34 · see citing works
Superior protein thermophilicity prediction with protein language model embeddings
Haselbeck, F., John, M., Zhang, Y., Pirnay, J., Werner, J., Costa, R. D., & Grimm, D. G. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad087 · Cited by 33 · see citing works
Vaccine development using artificial intelligence and machine learning: A review
Asediya, V., Anjaria, P. A., Mathakiya, R. A., KORINGA, P. G., Nayak, J. B., Bisht, D., Fulmali, D., Patel, V. A., & Desai, D. N. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136643 · Cited by 35 · see citing works
Beyond sequence: Structure-based machine learning
Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 31 · see citing works
TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions
Sledzieski, S., Devkota, K., Singh, R., Cowen, L., & Berger, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad663 · Cited by 26 · see citing works
Yeung, W., Zhou, Z., Mathew, L., Gravel, N., Taujale, R., O’Boyle, B. M., Salcedo, M. V., Venkat, A., Lanzilotta, W. N., Li, S., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac619 · Cited by 28 · see citing works
Ultra-fast protein structure prediction to capture effects of sequence variation in mutation moviesPreprint
Weißenow, K., Heinzinger, M., Steinegger, M., & Rost, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516473 · Cited by 28 · see citing works
Self-citationProtein embeddings predict binding residues in disordered regions
(2024) · doi:10.1038/s41598-024-64211-4 · Cited by 27 · see citing works
Self-citationLee, J.-W., Won, J.-H., Jeon, S., Choo, Y., Yeon, Y., Oh, J.-S., Kim, M., Kim, S., Joung, I., Jang, C., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad712 · Cited by 27 · see citing works
Pratyush, P., Bahmani, S., Pokharel, S., Ismail, H. D., & KC, D. B. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae290 · Cited by 24 · see citing works
Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes
Wang, X., Xu, K., Tan, Y., Liu, S., & Zhou, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043827 · Cited by 22 · see citing works
MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction
Glukhov, E., Kalitin, D., Stepanenko, D., Zhu, Y., Nguyen, T., Jones, G. S., Patsahan, T., Simmerling, C. L., Mitchell, J. C., Vajda, S., et al. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.05.011 · Cited by 22 · see citing works
Insights into the inner workings of transformer models for protein function prediction
Wenzel, M. A., Grüner, E., & Strodthoff, N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae031 · Cited by 21 · see citing works
Assessing the role of evolutionary information for enhancing protein language model embeddings
Erckert, K., & Rost, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-71783-8 · Cited by 20 · see citing works
Self-citationLambdaPP : Fast and accessible protein‐specific phenotype predictions
(2022) · doi:10.1002/pro.4524 · Cited by 19 · see citing works
Self-citationPTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings
Xu, Z., Zhong, H., He, B., Wang, X., & Lu, T. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3377362 · Cited by 18 · see citing works
Enzyme functional classification using artificial intelligence
Kim, H. R., Ji, H., Kim, G. B., & Lee, S. Y. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.03.003 · Cited by 17 · see citing works
Palacios, A. V., Acharya, P., Peidl, A. S., Beck, M. R., Blanco, E., Mishra, A., Bawa‐Khalfe, T., & Pakhrin, S. C. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae011 · Cited by 17 · see citing works
cdsBERT - Extending Protein Language Models with Codon AwarenessPreprint
Hallee, L., Rafailidis, N., & Gleghorn, J. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.15.558027 · Cited by 17 · see citing works
Language model and its interpretability in biomedicine: A scoping review
Lyu, D., Wang, X., Chen, Y., & Wang, F. (2024) · iScience · doi:10.1016/j.isci.2024.109334 · Cited by 17 · see citing works
Zhao, L., Li, J., Zhan, W., Jiang, X., & Zhang, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-67403-0 · Cited by 17 · see citing works
AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning
Breimann, S., Kamp, F., Steiner, H., & Frishman, D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168717 · Cited by 15 · see citing works
Mehdiabadi, M., Conte, A. D., Nugnes, M. V., Aspromonte, M. C., Tosatto, S. C. E., & Piovesan, D. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70045 · Cited by 19 · see citing works
Tubiana, J., Schneidman‐Duhovny, D., & Wolfson, H. J. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167758 · Cited by 14 · see citing works
Protein-Protein Interaction Prediction is Achievable with Large Language ModelsPreprint
Hallee, L., & Gleghorn, J. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.07.544109 · Cited by 14 · see citing works
Pang, Y. T., Yang, L., Kuo, K. M., & Gumbart, J. C. (2026) · Chemical Science · doi:10.1039/d5sc08253f · Cited by 13 · see citing works
ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs
Hao, X., & Fan, L. (2024) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2024.106705 · Cited by 13 · see citing works
Yang, S., & Xu, P. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115523 · Cited by 13 · see citing works
Lim, P. K., Julca, I., & Mutwil, M. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.013 · Cited by 12 · see citing works
Alternative Reading Frames are an Underappreciated Source of Protein Sequence Novelty
Ardern, Z. (2023) · Journal of Molecular Evolution · doi:10.1007/s00239-023-10122-3 · Cited by 12 · see citing works
Krokidis, M. G., Dimitrakopoulos, G. Ν., Vrahatis, A. G., Exarchos, T. P., & Vlamos, P. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1323182 · Cited by 13 · see citing works
Codon language embeddings provide strong signals for protein engineeringPreprint
Outeiral, C., & Deane, C. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.15.519894 · Cited by 12 · see citing works
BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction
Xie, X., Wu, C., Qi, Y., Liu, S., Huang, J., Lv, H., Dao, F., & Lin, H. (2025) · BMC Biology · doi:10.1186/s12915-025-02312-w · Cited by 11 · see citing works
Natesan, R., & Agrawal, N. J. (2023) · Scientific Reports · doi:10.1038/s41598-023-32067-9 · Cited by 11 · see citing works
Qi, D., Chen, S., & Liu, T. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115603 · Cited by 11 · see citing works
Huang, H., Shi, X., Lei, H., Hu, F., & Cai, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01345 · Cited by 11 · see citing works
Pratyush, P., Carrier, C., Pokharel, S., Ismail, H. D., Chaudhari, M., & KC, D. B. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf124 · Cited by 11 · see citing works
Predictive and therapeutic applications of protein language models
Furui, K., Sakano, K., & Ohue, M. (2025) · Allergology International · doi:10.1016/j.alit.2025.08.004 · Cited by 12 · see citing works
Asim, M. N., Asif, T., Hassan, F., & Dengel, A. (2025) · Database · doi:10.1093/database/baaf027 · Cited by 11 · see citing works
Exploration of protein sequence embeddings for protein-ligand binding site detection
Hoksza, D., & Gamouh, H. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995025 · Cited by 8 · see citing works
Charting γ-secretase substrates by explainable AI
Breimann, S., Kamp, F., Basset, G., Abou‐Ajram, C., Güner, G., Yanagida, K., Okochi, M., Müller, S. A., Lichtenthaler, S. F., Langosch, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60638-z · Cited by 9 · see citing works
A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction
Niazi, S. K., & Yang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-17022-0 · Cited by 9 · see citing works
Song, J., Qiao, J., Cheng, Z., Guo, J., Wang, Q., Zhou, Z., & Han, L. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.151400 · Cited by 8 · see citing works
Classifying alkaliphilic proteins using embeddings from protein language model
Susanty, M., Mursalim, M. K. N., Hertadi, R., Purwarianti, A., & Mengko, T. L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108385 · Cited by 9 · see citing works
Which pLM to choose?Preprint
Senoner, T., Koludarov, I., Guenther, J., Shehu, A., Rost, B., & Bromberg, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.30.685515 · Cited by 7 · see citing works
Self-citationPokharel, S., Barasa, K., Pratyush, P., & KC, D. B. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf245 · Cited by 9 · see citing works
Informatic challenges and advances in illuminating the druggable proteome
Taujale, R., Gravel, N., Zhou, Z., Yeung, W., Kochut, K., & Kannan, N. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103894 · Cited by 7 · see citing works
From obstacle to design advantage: activity cliff aware modeling for small-molecule drug discovery
Szostek, T., & Szulczyk, D. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104589 · Cited by 7 · see citing works
Poretsky, E., Andorf, C. M., & Sen, T. Z. (2023) · Plant Direct · doi:10.1002/pld3.554 · Cited by 6 · see citing works
Chatzimiltis, S., Agathocleous, M., Promponas, V. J., & Christodoulou, C. C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.022 · Cited by 5 · see citing works
Clark, J. D., Mi, X., Mitchell, D. A., & Shukla, D. (2024) · Digital Discovery · doi:10.1039/d4dd00170b · Cited by 5 · see citing works
Benchmarking recent computational tools for DNA-binding protein identification
Luo, X., Yi, A. S., HuiKai, A. L., Ong, T. J., Wong, L., & Rahman, C. R. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae634 · Cited by 5 · see citing works
Predicting protein structure from single sequences
Shen, Y. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00378-y · Cited by 5 · see citing works
Similarity-Adaptive interaction routing for Drug–Target affinity prediction
Zhou, Z., Chen, M., 周文剑, & Xiao, Q. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105100 · Cited by 4 · see citing works
IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network
Cao, R., Li, Q., Wei, P.-J., Ding, Y., Bin, Y., & Zheng, C.-H. (2025) · Biomolecules · doi:10.3390/biom15010099 · Cited by 4 · see citing works
Pokharel, S., Pratyush, P., Heinzinger, M., Newman, R. H., & KC, D. B. (2022) · Research Square · doi:10.21203/rs.3.rs-1953874/v1 · Cited by 4 · see citing works
Self-citationArtificial Intelligence Learns Protein PredictionPreprint
Heinzinger, M., & Rost, B. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041458 · Cited by 4 · see citing works
Self-citationAn efficient machine-learning framework for predicting protein post-translational modification sites
Elreify, H. M., El‐Samie, F. E. A., Dessouky, M. I., Torkey, H., El‐Khamy, S. E., & Shalaby, W. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-13178-x · Cited by 3 · see citing works
Advancements in characterization of protein dynamics with machine learning
Madani, M., & Tarakanova, A. (2026) · npj Soft Matter · doi:10.1038/s44431-026-00025-w · Cited by 3 · see citing works
Niu, B., Lee, B., Wang, L., Chen, W., & Johnson, J. (2024) · Antibodies · doi:10.3390/antib13030074 · Cited by 3 · see citing works
Kong, G., Wang, J., Fan, Y., & Wang, J. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822575 · Cited by 3 · see citing works
Homologues not needed: Structure prediction from a protein language model
Ben‐Tal, N., & Kolodny, R. (2022) · Structure · doi:10.1016/j.str.2022.07.002 · Cited by 3 · see citing works
Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model
Ülker, E. D. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140345 · Cited by 3 · see citing works
Nguyen, T. H., Wriggers, W. R., & He, J. J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5087-0_5 · Cited by 3 · see citing works
Human O-linked Glycosylation Site Prediction Using Pretrained Protein Language ModelPreprint
Pakhrin, S. C., Chauhan, N., Khan, S., Upadhyaya, J., Keller, C., Neuman, L. N., Beck, M. R., & Blanco, E. (2023) · Research Square · doi:10.21203/rs.3.rs-3587524/v1 · Cited by 3 · see citing works
Liu, Y., Wang, X., Zhong, H., Zhai, J., Gong, X., & Lu, T. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf242 · Cited by 2 · see citing works
Chen, G., Liao, J., Yu, Y., Le, K., Zhao, H., Qin, Y., Cai, L., & Sheng, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01753 · Cited by 4 · see citing works
Dock2D: Synthetic Data for the Molecular Recognition Problem
Bhadra-Lobo, S., Derevyanko, G., & Lamoureux, G. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3407477 · Cited by 2 · see citing works
- 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
- Physical Sciences › Materials Science › Materials Chemistry › 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
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