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Showing 1–9 of 9 results
Advanced filters: Author: Ivan Anishchenko Clear advanced filters
  • Computationally designing proteins with interfaces that bind small molecules has posed a long-standing challenge. Here, authors combine deep learning and physics-based approaches to design proteins that bind small molecules, and demonstrate their approach by designing a cortisol biosensor.

    • Gyu Rie Lee
    • Samuel J. Pellock
    • David Baker
    ResearchOpen Access
    Nature Communications
    P: 1-12
  • The trRosetta neural network was used to iteratively optimise model proteins from random 100-amino-acid sequences, resulting in ‘hallucinated’ proteins, which when expressed in bacteria closely resembled the model structures.

    • Ivan Anishchenko
    • Samuel J. Pellock
    • David Baker
    Research
    Nature
    Volume: 600, P: 547-552
  • RoseTTAFold2-Lite uses residue–residue coevolution and protein structure prediction to identify and structurally characterize protein–protein interactions in bacterial pathogens.

    • Ian R. Humphreys
    • Jing Zhang
    • David Baker
    ResearchOpen Access
    Nature Microbiology
    Volume: 9, P: 2642-2652
  • A deep-learning-based strategy is used to design artificial luciferases that catalyse the oxidative chemiluminescence of diphenylterazine with high substrate specificity and catalytic efficiency.

    • Andy Hsien-Wei Yeh
    • Christoffer Norn
    • David Baker
    ResearchOpen Access
    Nature
    Volume: 614, P: 774-780
  • Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.

    • Naozumi Hiranuma
    • Hahnbeom Park
    • David Baker
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-11
  • The trRosetta server, a web-based platform for fast and accurate protein structure prediction, is powered by deep learning and Rosetta. This protocol includes procedures for using the web-based server as well as the standalone package.

    • Zongyang Du
    • Hong Su
    • Jianyi Yang
    Protocols
    Nature Protocols
    Volume: 16, P: 5634-5651