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Machine learning reveals hidden stability code in protein native fluorescence

Zhang, H; Yang, Y; Zhang, C; Farid, SS; Dalby, PA; (2021) Machine learning reveals hidden stability code in protein native fluorescence. Computational and Structural Biotechnology Journal , 19 pp. 2750-2760. 10.1016/j.csbj.2021.04.047. Green open access

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Abstract

Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.

Type: Article
Title: Machine learning reveals hidden stability code in protein native fluorescence
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.csbj.2021.04.047
Language: English
Additional information: © 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Protein stability, Machine learning, Biopharmaceuticals
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10127756
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