Hutchison, C. A. I. et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016).<\/p>\n
Article<\/a>\u00a0 Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871\u2013876 (2021).<\/p>\n Article<\/a>\u00a0 Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).<\/p>\n Article<\/a>\u00a0 Lim, Y. et al. In silico protein interaction screening uncovers DONSON\u2019s role in replication initiation. Science 381, eadi3448 (2023).<\/p>\n Article<\/a>\u00a0 Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070\u20131078 (2023).<\/p>\n Article<\/a>\u00a0 Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: exploring the boundaries of protein language models. Cell Syst. 14, 968\u2013978.e3 (2023).<\/p>\n Article<\/a>\u00a0 Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089\u20131100 (2023).<\/p>\n Article<\/a>\u00a0 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\u201321.24.14 (2012).<\/p>\n Article<\/a>\u00a0 Gao, Y. et al. Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo. Nucleic Acids Res. 49, 9696\u20139710 (2021).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0 Gao, Y. et al. Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655. Nucleic Acids Res. 46, 10682\u201310696 (2018).<\/p>\n Article<\/a>\u00a0 Perez-Rueda, E. & Collado-Vides, J. The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12. Nucleic Acids Res. 28, 1838\u20131847 (2000).<\/p>\n Article<\/a>\u00a0 Mejia-Almonte, C. et al. Redefining fundamental concepts of transcription initiation in bacteria. Nat. Rev. Genet. 21, 699\u2013714 (2020).<\/p>\n Article<\/a>\u00a0 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\u20132074 (2016).<\/p>\n Article<\/a>\u00a0 Sastry, A. V. et al. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat. Commun. 10, 5536 (2019).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0 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\u201317239 (2020).<\/p>\n Article<\/a>\u00a0 Miller, H. K. et al. The extracytoplasmic function sigma factor \u03c3S protects against both intracellular and extracytoplasmic stresses in Staphylococcus aureus. J. Bacteriol. 194, 4342\u20134354 (2012).<\/p>\n Article<\/a>\u00a0 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\u2013D106 (2025).<\/p>\n Article<\/a>\u00a0 Yu, C., Zavaljevski, N., Desai, V. & Reifman, J. Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases. Proteins 74, 449\u2013460 (2009).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0 Claudel-Renard, C., Chevalet, C., Faraut, T. & Kahn, D. Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res. 31, 6633\u20136639 (2003).<\/p>\n Article<\/a>\u00a0 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\u201314001 (2019).<\/p>\n Article<\/a>\u00a0 Kim, G. B. et al. Functional annotation of enzyme-encoding genes using deep learning with transformer layers. Nat. Commun. 14, 7370 (2023).<\/p>\n Article<\/a>\u00a0 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\u2013W234 (2022).<\/p>\n Article<\/a>\u00a0 Yu, T. et al. Enzyme function prediction using contrastive learning. Science 379, 1358\u20131363 (2023).<\/p>\n Article<\/a>\u00a0 Zhang, C., Freddolino, L. & Zhang, Y. COFACTOR: improved protein function prediction by combining structure, sequence and protein\u2013protein interaction information. Nucleic Acids Res. 45, W291\u2013W299 (2017).<\/p>\n Article<\/a>\u00a0 Sanderson, T., Bileschi, M. L., Belanger, D. & Colwell, L. J. ProteInfer, deep neural networks for protein functional inference. eLife 12, e80942 (2023).<\/p>\n Article<\/a>\u00a0 Wang, T. et al. Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform. Sci. Adv. 11, eadt0402 (2025).<\/p>\n Article<\/a>\u00a0 Mateus, A. et al. The functional proteome landscape of Escherichia coli. Nature 588, 473\u2013478 (2020).<\/p>\n Article<\/a>\u00a0 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\u2013668 (2018).<\/p>\n Article<\/a>\u00a0 Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40, 932\u2013937 (2022).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0 Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).<\/p>\n Article<\/a>\u00a0 Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493\u2013500 (2024).<\/p>\n Article<\/a>\u00a0 Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594\u2013602 (2023).<\/p>\n Article<\/a>\u00a0 Barrio-Hernandez, I. et al. Clustering predicted structures at the scale of the known protein universe. Nature 622, 637\u2013645 (2023).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0 Shi, Z. et al. Enzyme Commission number prediction and benchmarking with hierarchical dual-core multitask learning framework. Research 6, 0153 (2023).<\/p>\n Article<\/a>\u00a0 Nguyen, T. B., de S\u00e1, 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).<\/p>\n Article<\/a>\u00a0 Buton, N., Coste, F. & Le Cunff, Y. Predicting enzymatic function of protein sequences with attention. Bioinformatics 39, btad620 (2023).<\/p>\n Article<\/a>\u00a0 Han, S. R. et al. Evidential deep learning for trustworthy prediction of Enzyme Commission number. Brief. Bioinform. 25, bbad401 (2023).<\/p>\n Article<\/a>\u00a0 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).<\/p>\n Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
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