BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

330 related articles for article (PubMed ID: 36108050)

  • 1. Robust deep learning-based protein sequence design using ProteinMPNN.
    Dauparas J; Anishchenko I; Bennett N; Bai H; Ragotte RJ; Milles LF; Wicky BIM; Courbet A; de Haas RJ; Bethel N; Leung PJY; Huddy TF; Pellock S; Tischer D; Chan F; Koepnick B; Nguyen H; Kang A; Sankaran B; Bera AK; King NP; Baker D
    Science; 2022 Oct; 378(6615):49-56. PubMed ID: 36108050
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.
    de Haas RJ; Brunette N; Goodson A; Dauparas J; Yi SY; Yang EC; Dowling Q; Nguyen H; Kang A; Bera AK; Sankaran B; de Vries R; Baker D; King NP
    Proc Natl Acad Sci U S A; 2024 Mar; 121(13):e2314646121. PubMed ID: 38502697
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels.
    Dolorfino M; Samanta R; Vorobieva A
    bioRxiv; 2024 Feb; ():. PubMed ID: 38352434
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Protein sequence design with a learned potential.
    Anand N; Eguchi R; Mathews II; Perez CP; Derry A; Altman RB; Huang PS
    Nat Commun; 2022 Feb; 13(1):746. PubMed ID: 35136054
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.
    de Haas RJ; Brunette N; Goodson A; Dauparas J; Yi SY; Yang EC; Dowling Q; Nguyen H; Kang A; Bera AK; Sankaran B; de Vries R; Baker D; King NP
    bioRxiv; 2023 Aug; ():. PubMed ID: 37577478
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improving de novo protein binder design with deep learning.
    Bennett NR; Coventry B; Goreshnik I; Huang B; Allen A; Vafeados D; Peng YP; Dauparas J; Baek M; Stewart L; DiMaio F; De Munck S; Savvides SN; Baker D
    Nat Commun; 2023 May; 14(1):2625. PubMed ID: 37149653
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Hallucinating symmetric protein assemblies.
    Wicky BIM; Milles LF; Courbet A; Ragotte RJ; Dauparas J; Kinfu E; Tipps S; Kibler RD; Baek M; DiMaio F; Li X; Carter L; Kang A; Nguyen H; Bera AK; Baker D
    Science; 2022 Oct; 378(6615):56-61. PubMed ID: 36108048
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps.
    Si D; Moritz SA; Pfab J; Hou J; Cao R; Wang L; Wu T; Cheng J
    Sci Rep; 2020 Mar; 10(1):4282. PubMed ID: 32152330
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Cyclic oligomer design with de novo αβ-proteins.
    Lin YR; Koga N; Vorobiev SM; Baker D
    Protein Sci; 2017 Nov; 26(11):2187-2194. PubMed ID: 28801928
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.
    Giri N; Roy RS; Cheng J
    Curr Opin Struct Biol; 2023 Apr; 79():102536. PubMed ID: 36773336
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Reengineering of a flavin-binding fluorescent protein using ProteinMPNN.
    Nikolaev A; Kuzmin A; Markeeva E; Kuznetsova E; Ryzhykau YL; Semenov O; Anuchina A; Remeeva A; Gushchin I
    Protein Sci; 2024 Apr; 33(4):e4958. PubMed ID: 38501498
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A general computational approach for repeat protein design.
    Parmeggiani F; Huang PS; Vorobiev S; Xiao R; Park K; Caprari S; Su M; Seetharaman J; Mao L; Janjua H; Montelione GT; Hunt J; Baker D
    J Mol Biol; 2015 Jan; 427(2):563-75. PubMed ID: 25451037
    [TBL] [Abstract][Full Text] [Related]  

  • 13. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.
    Terashi G; Wang X; Prasad D; Nakamura T; Kihara D
    Nat Methods; 2024 Jan; 21(1):122-131. PubMed ID: 38066344
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A suite of designed protein cages using machine learning and protein fragment-based protocols.
    Meador K; Castells-Graells R; Aguirre R; Sawaya MR; Arbing MA; Sherman T; Senarathne C; Yeates TO
    Structure; 2024 Jun; 32(6):751-765.e11. PubMed ID: 38513658
    [TBL] [Abstract][Full Text] [Related]  

  • 15. AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.
    Cummins MC; Jacobs TM; Teets FD; DiMaio F; Tripathy A; Kuhlman B
    Protein Sci; 2022 Jul; 31(7):e4368. PubMed ID: 35762713
    [TBL] [Abstract][Full Text] [Related]  

  • 16. De novo protein design by inversion of the AlphaFold structure prediction network.
    Goverde CA; Wolf B; Khakzad H; Rosset S; Correia BE
    Protein Sci; 2023 Jun; 32(6):e4653. PubMed ID: 37165539
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness.
    Wang T; Jin X; Lu X; Min X; Ge S; Li S
    Front Genet; 2023; 14():1347667. PubMed ID: 38274106
    [No Abstract]   [Full Text] [Related]  

  • 18. Deep Learning-Based Advances in Protein Structure Prediction.
    Pakhrin SC; Shrestha B; Adhikari B; Kc DB
    Int J Mol Sci; 2021 May; 22(11):. PubMed ID: 34074028
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An end-to-end deep learning method for protein side-chain packing and inverse folding.
    McPartlon M; Xu J
    Proc Natl Acad Sci U S A; 2023 Jun; 120(23):e2216438120. PubMed ID: 37253017
    [TBL] [Abstract][Full Text] [Related]  

  • 20. De novo design of protein structure and function with RFdiffusion.
    Watson JL; Juergens D; Bennett NR; Trippe BL; Yim J; Eisenach HE; Ahern W; Borst AJ; Ragotte RJ; Milles LF; Wicky BIM; Hanikel N; Pellock SJ; Courbet A; Sheffler W; Wang J; Venkatesh P; Sappington I; Torres SV; Lauko A; De Bortoli V; Mathieu E; Ovchinnikov S; Barzilay R; Jaakkola TS; DiMaio F; Baek M; Baker D
    Nature; 2023 Aug; 620(7976):1089-1100. PubMed ID: 37433327
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 17.