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Showing 1–11 of 11 results
Advanced filters: Author: Justas Dauparas Clear advanced filters
  • Predictive protein design and experiments are combined to develop anisotropic bifaceted protein nanomaterials using pseudosymmetric hetero-oligomeric building blocks.

    • Sanela Rankovic
    • Kenneth D. Carr
    • Neil P. King
    ResearchOpen Access
    Nature Materials
    Volume: 24, P: 1635-1643
  • This study presents a comprehensive modelling framework that jointly optimizes sequence and structure to generate de novo proteins with improved folding stability, providing large-scale experimental benchmarking across multiple computational design methods

    • Yehlin Cho
    • Justas Dauparas
    • Sergey Ovchinnikov
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-10
  • A deep learning approach enables accurate computational design of soluble and functional analogues of membrane proteins, expanding the soluble protein fold space and facilitating new approaches to drug screening and design.

    • Casper A. Goverde
    • Martin Pacesa
    • Bruno E. Correia
    ResearchOpen Access
    Nature
    Volume: 631, P: 449-458
  • A study describes an approach using designed building blocks that are far more regular in geometry than natural proteins to construct modular multicomponent protein assemblies.

    • Timothy F. Huddy
    • Yang Hsia
    • David Baker
    ResearchOpen Access
    Nature
    Volume: 627, P: 898-904
  • Here, the authors constructed a deep-learning approach to design closed repeat proteins with central binding pockets—a step towards designing proteins to specifically bind small molecules.

    • Linna An
    • Derrick R. Hicks
    • David Baker
    ResearchOpen Access
    Nature Structural & Molecular Biology
    Volume: 30, P: 1755-1760
  • Recently, a pipeline for the design of protein-binding proteins using only the structure of the target protein was reported. Here, the authors report that the incorporation of deep learning methods into the original pipeline increases experimental success rate by ten-fold.

    • Nathaniel R. Bennett
    • Brian Coventry
    • David Baker
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-9
  • 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
  • Motile and non-motile subpopulations often coexist in bacterial communities. Here, Xu et al. show that motile cells in colonies of common flagellated bacteria can self-organize into two adjacent motile rings, driving stable flows of fluid and materials around the colony.

    • Haoran Xu
    • Justas Dauparas
    • Yilin Wu
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-12
  • Large-scale assays using cDNA display proteolysis are used to measure the folding stabilities of protein domains, providing a method to quantify the effects of mutations on protein folding, with applications in protein design.

    • Kotaro Tsuboyama
    • Justas Dauparas
    • Gabriel J. Rocklin
    ResearchOpen Access
    Nature
    Volume: 620, P: 434-444