ArticleComputational and structural biotechnology journal2021
Machine learning reveals hidden stability code in protein native fluorescence.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.Molecular pharmaceutics · 2024Article
- Crystal structures and molecular dynamics simulations of a humanised antibody fragment at acidic to basic pH.Scientific reports · 2023Article
- NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF.Molecular pharmaceutics · 2022Article
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5 authors.
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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.
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