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dc.contributor.authorZhang, H-
dc.contributor.authorYang, Y-
dc.contributor.authorZhang, C-
dc.contributor.authorFarid, SS-
dc.contributor.authorDalby, PA-
dc.identifier.citationZhang, H. et al. (2021) ‘Machine learning reveals hidden stability code in protein native fluorescence’, Computational and Structural Biotechnology Journal. Elsevier BV. 19 pp. 2750 – 2760. doi:10.1016/j.csbj.2021.04.047.en_US
dc.description.abstractConformational 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.en_US
dc.description.sponsorshipFunding from the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub hosted at University College London with UK university partners is gratefully acknowledged (Grant Reference: EP/P006485/1). Financial and in-kind support from the consortium of industrial users and sector organizations is also acknowledged.en_US
dc.format.extent2750 - 2760-
dc.publisherElsevier BVen_US
dc.rights© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.-
dc.subjectProtein stabilityen_US
dc.subjectMachine learningen_US
dc.titleMachine learning reveals hidden stability code in protein native fluorescenceen_US
dc.relation.isPartOfComputational and Structural Biotechnology Journal-
dc.rights.licenseThis is an open access article under the CC BY license (
Appears in Collections:Chemistry
Dept of Mechanical and Aerospace Engineering Research Papers

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