Computational Modelling of the Human Islet Amyloid Polypeptide / / by Katrine Kirkeby Skeby

• Autore: Skeby Katrine Kirkeby
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• Edizione: 1st ed. 2016.
• Descrizione fisica: 1 online resource (XVI, 118 p. 58 illus., 47 illus. in color.)
• Disciplina: 540
• Soggetto topico: Medicinal chemistry; Chemoinformatics; Bioorganic chemistry; Medicinal Chemistry; Computer Applications in Chemistry; Bioorganic Chemistry
• Note generali: "Doctoral thesis accepted by the Aarhus University, Aarhus C, Denmark."
• Nota di bibliografia: Includes bibliographical references at the end of each chapters.
• Nota di contenuto: Amyloid and Amyloid Fibrils -- Computational Theory -- Imaging Agent Binding to Amyloid Protofibrils -- Determining the Aggregation Prone Structure of hiAPP -- Effect of Terminal Capping on Aggregation of Peptide Fragments -- Coarse Grained Study of Amyloid Protofibril Aggregation -- Conclusion and Perspectives.
• Sommario/riassunto: This thesis offers readers a comprehensive introduction to amyloid proteins and the computational methods used with them. Katrine Skeby critically assesses and compares both the literature and the experiments performed by other researchers, which further elevates the quality and relevance of her own work. Amyloid proteins are highly complex, and this research provides unparalleled insights, especially with regard to the origin of cytotoxicity and to developing technologies for early detection, revealing in detail the molecular mechanisms behind hIAPP behavior. Several studies within the thesis answer difficult questions which promote future research into the properties of amyloid proteins.
• Titolo autorizzato: Computational Modelling of the Human Islet Amyloid Polypeptide
• ISBN: 3-319-20040-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910254025903321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Theses, Recognizing Outstanding Ph.D. Research, . 2190-5053