Extracting Residue Solvent Exposure from Covalent Labeling Data with Machine Learning: A Hybrid Approach for Protein Structure Prediction

Hydroxyl radical protein footprinting (HRPF) coupled with mass spectrometry yields information about residue solvent exposure and protein topology. However, data from these experiments are sparse and require computational interpretation to generate useful structural insight. We previously implemente...

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Bibliographic Details
Main Author:	Elijah H. Day (21396836) (author)
Other Authors:	Steffen Lindert (115057) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Medicine Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified residue solvent exposure require computational interpretation inherently abstract nature training data required covalent labeling data successfully use ml experimental hrpf data hrpf data substantial amount subsequently used rosetta algorithm results illustrate protein topology previously implemented ml networks machine learning level detail hybrid method hybrid approach guided version four proteins challenging due benchmark set
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Extracting Residue Solvent Exposure from Covalent Labeling Data with Machine Learning: A Hybrid Approach for Protein Structure Prediction

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