Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition

Department of Drug Design and Pharmacology

Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition. / Trillo, Isabel Fatima Herranz; Jensen, Minna Grønning; van Maarschalkerweerd, Andreas; Tauler, Romà; Vestergaard, Bente; Bernadó, Pau.

In: Structure, Vol. 25, No. 1, 03.01.2017, p. 5–15.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Trillo, IFH, Jensen, MG, van Maarschalkerweerd, A, Tauler, R, Vestergaard, B & Bernadó, P 2017, 'Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition', Structure, vol. 25, no. 1, pp. 5–15. https://doi.org/10.1016/j.str.2016.10.013

APA

Trillo, I. F. H., Jensen, M. G., van Maarschalkerweerd, A., Tauler, R., Vestergaard, B., & Bernadó, P. (2017). Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition. Structure, 25(1), 5–15. https://doi.org/10.1016/j.str.2016.10.013

Vancouver

Trillo IFH, Jensen MG, van Maarschalkerweerd A, Tauler R, Vestergaard B, Bernadó P. Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition. Structure. 2017 Jan 3;25(1): 5–15. https://doi.org/10.1016/j.str.2016.10.013

Author

Trillo, Isabel Fatima Herranz ; Jensen, Minna Grønning ; van Maarschalkerweerd, Andreas ; Tauler, Romà ; Vestergaard, Bente ; Bernadó, Pau. / Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition. In: Structure. 2017 ; Vol. 25, No. 1. pp. 5–15.

Bibtex

@article{b754a59278db41ca8763b3473f3b26b0,

title = "Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition",

abstract = "Formation of amyloids is the hallmark of several neurodegenerative pathologies. Structural investigation of these complex transformation processes poses significant experimental challenges due to the co-existence of multiple species. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the evolution of these mixtures of oligomeric states, but the decomposition of SAXS data into species-specific spectra and relative concentrations is burdened by ambiguity. We present an objective SAXS data decomposition method by adapting the multivariate curve resolution alternating least squares (MCR-ALS) chemometric method. The approach enables rigorous and robust decomposition of synchrotron SAXS data by simultaneously introducing these data in different representations that emphasize molecular changes at different time and structural resolution ranges. The approach has allowed the study of fibrillogenic forms of insulin and the familial mutant E46K of α-synuclein, and is generally applicable to any macromolecular mixture that can be probed by SAXS.",

author = "Trillo, {Isabel Fatima Herranz} and Jensen, {Minna Gr{\o}nning} and {van Maarschalkerweerd}, Andreas and Rom{\`a} Tauler and Bente Vestergaard and Pau Bernad{\'o}",

year = "2017",

month = jan,

day = "3",

doi = "10.1016/j.str.2016.10.013",

language = "English",

volume = "25",

pages = " 5–15",

journal = "Structure",

issn = "0969-2126",

publisher = "Cell Press",

number = "1",

}

RIS

TY - JOUR

T1 - Structural Analysis of Multi-component Amyloid Systems by Chemometric SAXS Data Decomposition

AU - Trillo, Isabel Fatima Herranz

AU - Jensen, Minna Grønning

AU - van Maarschalkerweerd, Andreas

AU - Tauler, Romà

AU - Vestergaard, Bente

AU - Bernadó, Pau

PY - 2017/1/3

Y1 - 2017/1/3

N2 - Formation of amyloids is the hallmark of several neurodegenerative pathologies. Structural investigation of these complex transformation processes poses significant experimental challenges due to the co-existence of multiple species. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the evolution of these mixtures of oligomeric states, but the decomposition of SAXS data into species-specific spectra and relative concentrations is burdened by ambiguity. We present an objective SAXS data decomposition method by adapting the multivariate curve resolution alternating least squares (MCR-ALS) chemometric method. The approach enables rigorous and robust decomposition of synchrotron SAXS data by simultaneously introducing these data in different representations that emphasize molecular changes at different time and structural resolution ranges. The approach has allowed the study of fibrillogenic forms of insulin and the familial mutant E46K of α-synuclein, and is generally applicable to any macromolecular mixture that can be probed by SAXS.

AB - Formation of amyloids is the hallmark of several neurodegenerative pathologies. Structural investigation of these complex transformation processes poses significant experimental challenges due to the co-existence of multiple species. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the evolution of these mixtures of oligomeric states, but the decomposition of SAXS data into species-specific spectra and relative concentrations is burdened by ambiguity. We present an objective SAXS data decomposition method by adapting the multivariate curve resolution alternating least squares (MCR-ALS) chemometric method. The approach enables rigorous and robust decomposition of synchrotron SAXS data by simultaneously introducing these data in different representations that emphasize molecular changes at different time and structural resolution ranges. The approach has allowed the study of fibrillogenic forms of insulin and the familial mutant E46K of α-synuclein, and is generally applicable to any macromolecular mixture that can be probed by SAXS.

U2 - 10.1016/j.str.2016.10.013

DO - 10.1016/j.str.2016.10.013

M3 - Journal article

C2 - 27889205

VL - 25

SP - 5

EP - 15

JO - Structure

JF - Structure

SN - 0969-2126

IS - 1

ER -

ID: 169354297