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Issue 9, 2017

Ultrafast laser-probing spectroscopy for studying molecular structure of protein aggregates

Author affiliations

Abstract

We report the development of a new technique to screen protein aggregation based on laser-probing spectroscopy with sub-picosecond resolution. Protein aggregation is an important topic for materials science, fundamental biology as well as clinical studies in neurodegenerative diseases and translation studies in biomaterials engineering. However, techniques to study protein aggregation and assembly are limited to infrared spectroscopy, fluorescent assays, immunostaining, or functional assays among others. Here, we report a new technique to characterize protein structure–property relationship based on ultrafast laser-probing spectroscopy. First, we show theoretically that the temperature dependence of the refractive index of a protein is correlated to its crystallinity. Then, we performed time-domain thermo-transmission experiments on purified semi-crystalline proteins, both native and recombinant (i.e., silk and squid ring teeth), and also on intact E. coli cells bearing overexpressed recombinant protein. Our results demonstrate, for the first time, relative quantification of crystallinity in real time for protein aggregates. Our approach can potentially be used for screening an ultra-large number of proteins in vivo. Using this technique, we could answer many fundamental questions in structural protein research, such as the underlying sequence-structure relationship for protein assembly and aggregation.

Graphical abstract: Ultrafast laser-probing spectroscopy for studying molecular structure of protein aggregates

Supplementary files

Article information


Submitted
01 Dec 2016
Accepted
22 Feb 2017
First published
27 Feb 2017

Analyst, 2017,142, 1434-1441
Article type
Paper

Ultrafast laser-probing spectroscopy for studying molecular structure of protein aggregates

H. Jung, C. J. Szwejkowski, A. Pena-Francesch, J. A. Tomko, B. Allen, Ş. K. Özdemir, P. Hopkins and M. C. Demirel, Analyst, 2017, 142, 1434 DOI: 10.1039/C6AN02570F

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