Hutchison, C. A. I. et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016).

Article 
PubMed 

Google Scholar
 

Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Lim, Y. et al. In silico protein interaction screening uncovers DONSON’s role in replication initiation. Science 381, eadi3448 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: exploring the boundaries of protein language models. Cell Syst. 14, 968–978.e3 (2023).

Article 
CAS 
PubMed 

Google Scholar
 

Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Rhee, H. S. & Pugh, B. F. ChIP-exo method for identifying genomic location of DNA-binding proteins with near-single-nucleotide accuracy. Curr. Protoc. Mol. Biol. 100, 21.24.1–21.24.14 (2012).

Article 

Google Scholar
 

Gao, Y. et al. Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo. Nucleic Acids Res. 49, 9696–9710 (2021).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Kim, G. B., Gao, Y., Palsson, B. O. & Lee, S. Y. DeepTFactor: a deep learning-based tool for the prediction of transcription factors. Proc. Natl Acad. Sci. USA 118, e2021171118 (2021).

Article 
CAS 
PubMed 

Google Scholar
 

Gao, Y. et al. Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655. Nucleic Acids Res. 46, 10682–10696 (2018).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Perez-Rueda, E. & Collado-Vides, J. The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12. Nucleic Acids Res. 28, 1838–1847 (2000).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Mejia-Almonte, C. et al. Redefining fundamental concepts of transcription initiation in bacteria. Nat. Rev. Genet. 21, 699–714 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Ishihama, A., Shimada, T. & Yamazaki, Y. Transcription profile of Escherichia coli: genomic SELEX search for regulatory targets of transcription factors. Nucleic Acids Res. 44, 2058–2074 (2016).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Sastry, A. V. et al. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat. Commun. 10, 5536 (2019).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Rodionova, I. A. et al. Identification of a transcription factor, PunR, that regulates the purine and purine nucleoside transporter punC in E. coli. Commun. Biol. 4, 991 (2021).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Poudel, S. et al. Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators, and role in key physiological response. Proc. Natl Acad. Sci. USA 117, 17228–17239 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Miller, H. K. et al. The extracytoplasmic function sigma factor σS protects against both intracellular and extracytoplasmic stresses in Staphylococcus aureus. J. Bacteriol. 194, 4342–4354 (2012).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Catoiu, E. A. et al. iModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets. Nucleic Acids Res. 53, D99–D106 (2025).

Article 
PubMed 

Google Scholar
 

Yu, C., Zavaljevski, N., Desai, V. & Reifman, J. Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases. Proteins 74, 449–460 (2009).

Article 
CAS 
PubMed 

Google Scholar
 

Desai, D. K., Nandi, S., Srivastava, P. K. & Lynn, A. M. ModEnzA: accurate identification of metabolic enzymes using function specific profile HMMs with optimised discrimination threshold and modified emission probabilities. Adv. Bioinform 2011, 743782 (2011).

Article 

Google Scholar
 

Claudel-Renard, C., Chevalet, C., Faraut, T. & Kahn, D. Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res. 31, 6633–6639 (2003).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proc. Natl Acad. Sci. USA 116, 13996–14001 (2019).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Kim, G. B. et al. Functional annotation of enzyme-encoding genes using deep learning with transformer layers. Nat. Commun. 14, 7370 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Nielsen, H. & Winther, O. DeepLoc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic Acids Res. 50, W228–W234 (2022).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Yu, T. et al. Enzyme function prediction using contrastive learning. Science 379, 1358–1363 (2023).

Article 
CAS 
PubMed 

Google Scholar
 

Zhang, C., Freddolino, L. & Zhang, Y. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Res. 45, W291–W299 (2017).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Sanderson, T., Bileschi, M. L., Belanger, D. & Colwell, L. J. ProteInfer, deep neural networks for protein functional inference. eLife 12, e80942 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Wang, T. et al. Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform. Sci. Adv. 11, eadt0402 (2025).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Mateus, A. et al. The functional proteome landscape of Escherichia coli. Nature 588, 473–478 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Kulmanov, M., Khan, M. A., Hoehndorf, R. & Wren, J. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34, 660–668 (2018).

Article 
CAS 
PubMed 

Google Scholar
 

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40, 932–937 (2022).

Article 
CAS 
PubMed 

Google Scholar
 

Abdin, O., Nim, S., Wen, H. & Kim, P. M. PepNN: a deep attention model for the identification of peptide binding sites. Commun. Biol. 5, 503 (2022).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).

Article 
CAS 
PubMed 

Google Scholar
 

Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Barrio-Hernandez, I. et al. Clustering predicted structures at the scale of the known protein universe. Nature 622, 637–645 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Dalkiran, A. et al. ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinform. 19, 334 (2018).

Article 
CAS 

Google Scholar
 

Shi, Z. et al. Enzyme Commission number prediction and benchmarking with hierarchical dual-core multitask learning framework. Research 6, 0153 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Nguyen, T. B., de Sá, A. G. C., Rodrigues, C. H. M., Pires, D. E. V. & Ascher, D. B. LEGO-CSM: a tool for functional characterization of proteins. Bioinformatics 39, btad402 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Buton, N., Coste, F. & Le Cunff, Y. Predicting enzymatic function of protein sequences with attention. Bioinformatics 39, btad620 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

Han, S. R. et al. Evidential deep learning for trustworthy prediction of Enzyme Commission number. Brief. Bioinform. 25, bbad401 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Watanabe, N., Yamamoto, M., Murata, M., Kuriya, Y. & Araki, M. EnzymeNet: residual neural networks model for Enzyme Commission number prediction. Bioinform. Adv. 3, vbad173 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
Â