...
Thursday, January 8, 2026
.
1M+
.
website counter widget
.
.
More
    Thursday, January 8, 2026
    1M+ Hits
    ...
    website counter
    ...
    More
      28,500FansLike
      400FollowersFollow
      600SubscribersSubscribe

      Approaches for accelerating microbial gene function discovery using artificial intelligence – Microbiology Research


    • 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
       



    • Read more about this post…

      Credits: Source

      Disclaimer

      Join us

      28,500FansLike
      400FollowersFollow
      600SubscribersSubscribe

      Local Weather

      New York
      broken clouds
      6.2 ° C
      7.3 °
      4 °
      71 %
      4.6kmh
      75 %
      Thu
      13 °
      Fri
      8 °
      Sat
      9 °
      Sun
      8 °
      Mon
      5 °

      Web Hits

      website counter

      Visitor Count

      hit counter

      In-Service

      AF.com AI Powered 7-years

      Latest Posts

      spot_imgspot_img

      Your Gut Microbes May Be Quietly Transforming How Your Brain Works – Science News

      A pioneering study provides new evidence that gut microbes vary across primate species and can shape physiology in ways associated with differences in brain...

      Related articles

      Leave a reply

      Please enter your comment!
      Please enter your name here

      spot_imgspot_img
      Privacy Overview

      This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.