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Showing 1–13 of 13 results
Advanced filters: Author: Inna Goreshnik 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
  • Immune receptors regulate immune responses and are key cancer immunotherapy targets. Here, the authors designed helical concave scaffolds to bind convex sites in immune receptors, creating high-affinity protein binders for TGFβRII, CTLA-4, and PD-L1. Co-crystal structures confirmed their therapeutic potential.

    • Wei Yang
    • Derrick R. Hicks
    • David Baker
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12
  • This study demonstrates the capability of deep learning protein design models in generating functionally validated β-strand pairing interfaces, expanding the structural diversity of de novo binding proteins and accessible target surfaces.

    • Isaac Sappington
    • Martin Toul
    • David Baker
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-15
  • The combination of computational design, laboratory-based screening and biophysical validation enables the de novo generation of variable heavy-chain antibody fragments and antibodies that precisely target chosen disease-related molecules.

    • Nathaniel R. Bennett
    • Joseph L. Watson
    • David Baker
    ResearchOpen Access
    Nature
    Volume: 649, P: 183-193
  • The authors develop a computational method to design small DNA-binding proteins (DBPs) that target specific sequences. Designed DBPs show structural accuracy and function in both bacterial and mammalian cells for transcriptional regulation.

    • Cameron J. Glasscock
    • Robert J. Pecoraro
    • David Baker
    ResearchOpen Access
    Nature Structural & Molecular Biology
    Volume: 32, P: 2252-2261
  • Computationally designed genetically encoded proteins can be used to target surface proteins, thereby triggering endocytosis and subsequent intracellular degradation, activating signalling or increasing cellular uptake in specific tissues.

    • Buwei Huang
    • Mohamad Abedi
    • David Baker
    ResearchOpen Access
    Nature
    Volume: 638, P: 796-804
  • A design pipeline is presented whereby binding proteins can be designed de novo without the need for prior information on binding hotspots or fragments from structures of complexes with binding partners.

    • Longxing Cao
    • Brian Coventry
    • David Baker
    ResearchOpen Access
    Nature
    Volume: 605, P: 551-560
  • 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 massively parallel computational and experimental approach for de novo designing and screening small hyperstable proteins targeting influenza haemagglutinin and botulinum neurotoxin B identifies new therapeutic candidates more robust than traditional antibody therapies.

    • Aaron Chevalier
    • Daniel-Adriano Silva
    • David Baker
    Research
    Nature
    Volume: 550, P: 74-79
  • Computational design enables the generation of a chimeric construct of the RAS exchange factor SOS that is specifically activated by a small molecule. The expression of this construct in different cell types reveals distinct phosphorylation kinetics.

    • John C Rose
    • Po-Ssu Huang
    • Dustin J Maly
    Research
    Nature Chemical Biology
    Volume: 13, P: 119-126