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Multistep Modeling Strategy To Improve the Binding Affinity Prediction of PET Tracers to A beta(42): Case Study with Styrylbenzoxazole Derivatives
KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.
KTH, School of Biotechnology (BIO), Theoretical Chemistry (closed 20110512). KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.ORCID iD: 0000-0003-0185-5724
KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.ORCID iD: 0000-0002-1763-9383
2016 (English)In: ACS CHEMICAL NEUROSCIENCE, ISSN 1948-7193, Vol. 7, no 12, p. 1698-1705Article in journal (Refereed) Published
Abstract [en]

Positron emission tomography (PET) tracers play an important role in the diagnosis of Alzheimer's disease, a condition that leads to progressive dementia and memory loss. A high binding affinity and specificity of the PET tracers to amyloid oligomers and fibrils are crucial for their successful application as diagnostic agents. In this sense, it is essential to design PET tracers with enhanced binding affinities, which can lead to more precise and earlier detection of Alzheimer's disease conditions. The application of in silico methodology for the design and development of efficient PET tracers may serve as an important route to improved Alzheimer's disease diagnosis. In this work, the performance of widely used computational methods is explored for predicting experimental binding affinities of styrylbenzoxazole (SB) derivatives against a common amyloid protofibril. By performing docking, molecular dynamics, and quantum chemistry calculations in sequence their combined predictive performance is explored. The present work emphasizes the merits as well as limitations of these simulation strategies in the realm of designing PET tracers for Alzheimer's disease diagnosis.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2016. Vol. 7, no 12, p. 1698-1705
Keywords [en]
Alzheimer's disease, amyloid-beta peptide, PET tracers, molecular dynamics, density functional theory
National Category
Interaction Technologies
Identifiers
URN: urn:nbn:se:kth:diva-200219DOI: 10.1021/acschemneuro.6b00216ISI: 000390731500011Scopus ID: 2-s2.0-85006931094OAI: oai:DiVA.org:kth-200219DiVA, id: diva2:1074143
Note

QC 20170214

Available from: 2017-02-14 Created: 2017-02-14 Last updated: 2017-02-14Bibliographically approved

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Natarajan Arul, MuruganÅgren, Hans

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