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  1. Artikel ; Online: Structural basis of efficacy-driven ligand selectivity at GPCRs.

    Powers, Alexander S / Pham, Vi / Burger, Wessel A C / Thompson, Geoff / Laloudakis, Yianni / Barnes, Nicholas W / Sexton, Patrick M / Paul, Steven M / Christopoulos, Arthur / Thal, David M / Felder, Christian C / Valant, Celine / Dror, Ron O

    Nature chemical biology

    2023  Band 19, Heft 7, Seite(n) 805–814

    Abstract: A drug's selectivity for target receptors is essential to its therapeutic utility, but achieving selectivity between similar receptors is challenging. The serendipitous discovery of ligands that stimulate target receptors more strongly than closely ... ...

    Abstract A drug's selectivity for target receptors is essential to its therapeutic utility, but achieving selectivity between similar receptors is challenging. The serendipitous discovery of ligands that stimulate target receptors more strongly than closely related receptors, despite binding with similar affinities, suggests a solution. The molecular mechanism of such 'efficacy-driven selectivity' has remained unclear, however, hindering design of such ligands. Here, using atomic-level simulations, we reveal the structural basis for the efficacy-driven selectivity of a long-studied clinical drug candidate, xanomeline, between closely related muscarinic acetylcholine receptors (mAChRs). Xanomeline's binding mode is similar across mAChRs in their inactive states but differs between mAChRs in their active states, with divergent effects on active-state stability. We validate this mechanism experimentally and use it to design ligands with altered efficacy-driven selectivity. Our results suggest strategies for the rational design of ligands that achieve efficacy-driven selectivity for many pharmaceutically important G-protein-coupled receptors.
    Mesh-Begriff(e) Ligands ; Receptors, Muscarinic/chemistry ; Receptors, Muscarinic/metabolism ; Pyridines ; Thiadiazoles/chemistry ; Receptors, G-Protein-Coupled/chemistry
    Chemische Substanzen Ligands ; xanomeline (9ORI6L73CJ) ; Receptors, Muscarinic ; Pyridines ; Thiadiazoles ; Receptors, G-Protein-Coupled
    Sprache Englisch
    Erscheinungsdatum 2023-02-13
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2202962-X
    ISSN 1552-4469 ; 1552-4450
    ISSN (online) 1552-4469
    ISSN 1552-4450
    DOI 10.1038/s41589-022-01247-5
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Author Correction: Structural basis of efficacy-driven ligand selectivity at GPCRs.

    Powers, Alexander S / Pham, Vi / Burger, Wessel A C / Thompson, Geoff / Laloudakis, Yianni / Barnes, Nicholas W / Sexton, Patrick M / Paul, Steven M / Christopoulos, Arthur / Thal, David M / Felder, Christian C / Valant, Celine / Dror, Ron O

    Nature chemical biology

    2023  Band 19, Heft 4, Seite(n) 529

    Sprache Englisch
    Erscheinungsdatum 2023-03-05
    Erscheinungsland United States
    Dokumenttyp Published Erratum
    ZDB-ID 2202962-X
    ISSN 1552-4469 ; 1552-4450
    ISSN (online) 1552-4469
    ISSN 1552-4450
    DOI 10.1038/s41589-023-01297-3
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Xanomeline displays concomitant orthosteric and allosteric binding modes at the M

    Burger, Wessel A C / Pham, Vi / Vuckovic, Ziva / Powers, Alexander S / Mobbs, Jesse I / Laloudakis, Yianni / Glukhova, Alisa / Wootten, Denise / Tobin, Andrew B / Sexton, Patrick M / Paul, Steven M / Felder, Christian C / Danev, Radostin / Dror, Ron O / Christopoulos, Arthur / Valant, Celine / Thal, David M

    Nature communications

    2023  Band 14, Heft 1, Seite(n) 5440

    Abstract: ... The ... ...

    Abstract The M
    Mesh-Begriff(e) Humans ; Allosteric Site ; Behavior, Addictive ; Brain ; Cognition
    Chemische Substanzen xanomeline (9ORI6L73CJ)
    Sprache Englisch
    Erscheinungsdatum 2023-09-06
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-41199-5
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: Molecular mechanism of biased signaling at the kappa opioid receptor.

    El Daibani, Amal / Paggi, Joseph M / Kim, Kuglae / Laloudakis, Yianni D / Popov, Petr / Bernhard, Sarah M / Krumm, Brian E / Olsen, Reid H J / Diberto, Jeffrey / Carroll, F Ivy / Katritch, Vsevolod / Wünsch, Bernhard / Dror, Ron O / Che, Tao

    Nature communications

    2023  Band 14, Heft 1, Seite(n) 1338

    Abstract: The κ-opioid receptor (KOR) has emerged as an attractive drug target for pain management without addiction, and biased signaling through particular pathways of KOR may be key to maintaining this benefit while minimizing side-effect liabilities. As for ... ...

    Abstract The κ-opioid receptor (KOR) has emerged as an attractive drug target for pain management without addiction, and biased signaling through particular pathways of KOR may be key to maintaining this benefit while minimizing side-effect liabilities. As for most G protein-coupled receptors (GPCRs), however, the molecular mechanisms of ligand-specific signaling at KOR have remained unclear. To better understand the molecular determinants of KOR signaling bias, we apply structure determination, atomic-level molecular dynamics (MD) simulations, and functional assays. We determine a crystal structure of KOR bound to the G protein-biased agonist nalfurafine, the first approved KOR-targeting drug. We also identify an arrestin-biased KOR agonist, WMS-X600. Using MD simulations of KOR bound to nalfurafine, WMS-X600, and a balanced agonist U50,488, we identify three active-state receptor conformations, including one that appears to favor arrestin signaling over G protein signaling and another that appears to favor G protein signaling over arrestin signaling. These results, combined with mutagenesis validation, provide a molecular explanation of how agonists achieve biased signaling at KOR.
    Mesh-Begriff(e) Receptors, Opioid, kappa/metabolism ; Morphinans ; GTP-Binding Proteins/metabolism ; Arrestin/metabolism ; Analgesics, Opioid
    Chemische Substanzen TRK 820 (25CC4N0P8J) ; Receptors, Opioid, kappa ; Morphinans ; GTP-Binding Proteins (EC 3.6.1.-) ; Arrestin ; Analgesics, Opioid
    Sprache Englisch
    Erscheinungsdatum 2023-03-11
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-37041-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Selective posttranslational inhibition of Ca

    Morgenstern, Travis J / Nirwan, Neha / Hernández-Ochoa, Erick O / Bibollet, Hugo / Choudhury, Papiya / Laloudakis, Yianni D / Ben Johny, Manu / Bannister, Roger A / Schneider, Martin F / Minor, Daniel L / Colecraft, Henry M

    Nature communications

    2022  Band 13, Heft 1, Seite(n) 7556

    Abstract: ... ...

    Abstract Ca
    Mesh-Begriff(e) Calcium Channels/metabolism ; Myocytes, Cardiac/metabolism ; Neurons/metabolism ; src Homology Domains ; Protein Isoforms/genetics ; Protein Isoforms/metabolism ; Calcium Channels, L-Type/genetics ; Calcium Channels, L-Type/metabolism ; Calcium/metabolism
    Chemische Substanzen Calcium Channels ; Protein Isoforms ; Calcium Channels, L-Type ; Calcium (SY7Q814VUP)
    Sprache Englisch
    Erscheinungsdatum 2022-12-09
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-35025-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Buch ; Online: ATOM3D

    Townshend, Raphael J. L. / Vögele, Martin / Suriana, Patricia / Derry, Alexander / Powers, Alexander / Laloudakis, Yianni / Balachandar, Sidhika / Anderson, Brandon / Eismann, Stephan / Kondor, Risi / Altman, Russ B. / Dror, Ron O.

    Tasks On Molecules in Three Dimensions

    2020  

    Abstract: Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry. In particular deep neural networks have recently gained significant attention. In this work ... ...

    Abstract Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry. In particular deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance relative to one- and two-dimensional methods. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information. Furthermore, equivariant networks show significant promise. Our results indicate many molecular problems stand to gain from three-dimensional molecular learning. All code and datasets can be accessed via https://www.atom3d.ai .
    Schlagwörter Computer Science - Machine Learning ; Physics - Biological Physics ; Physics - Computational Physics ; Quantitative Biology - Biomolecules
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-12-07
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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