Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Meller, Artur; De Oliveira, Saulo; Davtyan, Aram; Abramyan, Tigran; Bowman, Gregory R; van den Bedem, Henry

Meller, Artur; De Oliveira, Saulo; Davtyan, Aram; Abramyan, Tigran; Bowman, Gregory R; van den Bedem, Henry.

Affiliation

Meller A; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, MO, United States.
De Oliveira S; Medical Scientist Training Program, Washington University in St. Louis, St. Louis, MO, United States.
Davtyan A; Atomwise, Inc., San Francisco, CA, United States.
Abramyan T; Atomwise, Inc., San Francisco, CA, United States.
Bowman GR; Atomwise, Inc., San Francisco, CA, United States.
van den Bedem H; Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, United States.

Front Mol Biosci ; 10: 1171143, 2023.

Article de En | MEDLINE | ID: mdl-37143823

Mots clés

allosteric inhibition; cryptic site; deep learning; markov state models; molecular dynamics simulation; virtual high throughput screening (vHTS)

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Front Mol Biosci Année: 2023 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: Suisse

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