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  1. Artikel ; Online: Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.

    Wang, Jue / Watson, Joseph L / Lisanza, Sidney L

    Cold Spring Harbor perspectives in biology

    2024  

    Abstract: Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design ... ...

    Abstract Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
    Sprache Englisch
    Erscheinungsdatum 2024-03-04
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1943-0264
    ISSN (online) 1943-0264
    DOI 10.1101/cshperspect.a041472
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Scaffolding protein functional sites using deep learning.

    Wang, Jue / Lisanza, Sidney / Juergens, David / Tischer, Doug / Watson, Joseph L / Castro, Karla M / Ragotte, Robert / Saragovi, Amijai / Milles, Lukas F / Baek, Minkyung / Anishchenko, Ivan / Yang, Wei / Hicks, Derrick R / Expòsit, Marc / Schlichthaerle, Thomas / Chun, Jung-Ho / Dauparas, Justas / Bennett, Nathaniel / Wicky, Basile I M /
    Muenks, Andrew / DiMaio, Frank / Correia, Bruno / Ovchinnikov, Sergey / Baker, David

    Science (New York, N.Y.)

    2022  Band 377, Heft 6604, Seite(n) 387–394

    Abstract: The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without ... ...

    Abstract The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.
    Mesh-Begriff(e) Binding Sites ; Catalysis ; Deep Learning ; Protein Binding ; Protein Engineering/methods ; Protein Folding ; Protein Structure, Secondary ; Proteins/chemistry
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2022-07-21
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.abn2100
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Spatiotemporal dynamics of GEF-H1 activation controlled by microtubule- and Src-mediated pathways.

    Azoitei, Mihai L / Noh, Jungsik / Marston, Daniel J / Roudot, Philippe / Marshall, Christopher B / Daugird, Timothy A / Lisanza, Sidney L / Sandí, María-José / Ikura, Mitsu / Sondek, John / Rottapel, Robert / Hahn, Klaus M / Danuser, Gaudenz

    The Journal of cell biology

    2019  Band 218, Heft 9, Seite(n) 3077–3097

    Abstract: Rho family GTPases are activated with precise spatiotemporal control by guanine nucleotide exchange factors (GEFs). Guanine exchange factor H1 (GEF-H1), a RhoA activator, is thought to act as an integrator of microtubule (MT) and actin dynamics in ... ...

    Abstract Rho family GTPases are activated with precise spatiotemporal control by guanine nucleotide exchange factors (GEFs). Guanine exchange factor H1 (GEF-H1), a RhoA activator, is thought to act as an integrator of microtubule (MT) and actin dynamics in diverse cell functions. Here we identify a GEF-H1 autoinhibitory sequence and exploit it to produce an activation biosensor to quantitatively probe the relationship between GEF-H1 conformational change, RhoA activity, and edge motion in migrating cells with micrometer- and second-scale resolution. Simultaneous imaging of MT dynamics and GEF-H1 activity revealed that autoinhibited GEF-H1 is localized to MTs, while MT depolymerization subadjacent to the cell cortex promotes GEF-H1 activation in an ~5-µm-wide peripheral band. GEF-H1 is further regulated by Src phosphorylation, activating GEF-H1 in a narrower band ~0-2 µm from the cell edge, in coordination with cell protrusions. This indicates a synergistic intersection between MT dynamics and Src signaling in RhoA activation through GEF-H1.
    Mesh-Begriff(e) Animals ; Biosensing Techniques ; COS Cells ; Chlorocebus aethiops ; HEK293 Cells ; Humans ; Microtubules/genetics ; Microtubules/metabolism ; Rho Guanine Nucleotide Exchange Factors/genetics ; Rho Guanine Nucleotide Exchange Factors/metabolism ; Signal Transduction ; rhoA GTP-Binding Protein/genetics ; rhoA GTP-Binding Protein/metabolism ; src-Family Kinases/genetics ; src-Family Kinases/metabolism
    Chemische Substanzen ARHGEF2 protein, human ; Rho Guanine Nucleotide Exchange Factors ; RHOA protein, human (124671-05-2) ; src-Family Kinases (EC 2.7.10.2) ; rhoA GTP-Binding Protein (EC 3.6.5.2)
    Sprache Englisch
    Erscheinungsdatum 2019-08-16
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 218154-x
    ISSN 1540-8140 ; 0021-9525
    ISSN (online) 1540-8140
    ISSN 0021-9525
    DOI 10.1083/jcb.201812073
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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