Molecular-dynamics simulation methods for macromolecular crystallography.

conformational ensembles molecular-dynamics simulations protein kinases water structure

Journal

Acta crystallographica. Section D, Structural biology
ISSN: 2059-7983
Titre abrégé: Acta Crystallogr D Struct Biol
Pays: United States
ID NLM: 101676043

Informations de publication

Date de publication:
01 Jan 2023
Historique:
received: 08 09 2022
accepted: 13 12 2022
entrez: 5 1 2023
pubmed: 6 1 2023
medline: 7 1 2023
Statut: ppublish

Résumé

It is investigated whether molecular-dynamics (MD) simulations can be used to enhance macromolecular crystallography (MX) studies. Historically, protein crystal structures have been described using a single set of atomic coordinates. Because conformational variation is important for protein function, researchers now often build models that contain multiple structures. Methods for building such models can fail, however, in regions where the crystallographic density is difficult to interpret, for example at the protein-solvent interface. To address this limitation, a set of MD-MX methods that combine MD simulations of protein crystals with conventional modeling and refinement tools have been developed. In an application to a cyclic adenosine monophosphate-dependent protein kinase at room temperature, the procedure improved the interpretation of ambiguous density, yielding an alternative water model and a revised protein model including multiple conformations. The revised model provides mechanistic insights into the catalytic and regulatory interactions of the enzyme. The same methods may be used in other MX studies to seek mechanistic insights.

Identifiants

pubmed: 36601807
pii: S2059798322011871
doi: 10.1107/S2059798322011871
pmc: PMC9815100
doi:

Substances chimiques

Proteins 0
Solvents 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

50-65

Subventions

Organisme : NIH HHS
ID : GM108889
Pays : United States
Organisme : NIH HHS
ID : GM124270
Pays : United States
Organisme : NIH HHS
ID : GM123159
Pays : United States
Organisme : NIH HHS
ID : GM124149
Pays : United States
Organisme : NIH HHS
ID : GM132386
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM132386
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM130389
Pays : United States
Organisme : NIH HHS
ID : GM130389
Pays : United States
Organisme : NIH HHS
ID : T32 CA009523/CA/NCI
Pays : United States

Informations de copyright

open access.

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Auteurs

David C Wych (DC)

Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

Phillip C Aoto (PC)

Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA.

Lily Vu (L)

Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA.

Alexander M Wolff (AM)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.

David L Mobley (DL)

Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA 92697, USA.

James S Fraser (JS)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.

Susan S Taylor (SS)

Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA.

Michael E Wall (ME)

Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

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Classifications MeSH