Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations.

Dominic, Anthony J; Sayer, Thomas; Cao, Siqin; Markland, Thomas E; Huang, Xuhui; Montoya-Castillo, Andrés

Dominic, Anthony J; Sayer, Thomas; Cao, Siqin; Markland, Thomas E; Huang, Xuhui; Montoya-Castillo, Andrés.

Afiliación

Dominic AJ; Department of Chemistry, University of Colorado, Boulder, CO 80309.
Sayer T; Department of Chemistry, University of Colorado, Boulder, CO 80309.
Cao S; Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706.
Markland TE; Department of Chemistry, Stanford University, Stanford, CA 94305.
Huang X; Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706.
Montoya-Castillo A; Department of Chemistry, University of Colorado, Boulder, CO 80309.

Proc Natl Acad Sci U S A ; 120(12): e2221048120, 2023 03 21.

Article en En | MEDLINE | ID: mdl-36920924

Asunto(s)

Dipéptidos; Simulación de Dinámica Molecular; Humanos; Cadenas de Markov

Palabras clave

Markov state models; biomolecular dynamics; generalized master equations; memory effects; protein folding

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_financiamento_saude Asunto principal: Dipéptidos / Simulación de Dinámica Molecular Tipo de estudio: Health_economic_evaluation / Prognostic_studies Límite: Humans Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2023 Tipo del documento: Article

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