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bioRxiv · 10.1101/2023.10.09.561482

HSQC2STRUC: A Machine Learning Model for Protein Secondary Structure Prediction using Unassigned NMR Spectra

Dietrich, J.·Bellstedt, P.
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Abstract

Dynamic changes in the secondary structure content of proteins can provide valuable insights into protein function or dysfunction. Predicting these dynamic changes is still a significant challenge but is of paramount importance for basic research as well as drug development. Here, we present a machine learning-based model that predicts the secondary structure content of proteins based on their un assigned1H,15N-HSQC NMR spectra with an RMSE of 0.11 for -helix, 0.08 for {beta}-sheet and 0.12 for random coil content. Our model has been implemented into an easy-to-use and publicly available web service that estimates secondary structure content based on a provided peak list. Furthermore, a Python version is provided, ready to be integrated into Brukers TopSpin software or own scripts.

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Dietrich, J., Bellstedt, P.. 2023-10-10. HSQC2STRUC: A Machine Learning Model for Protein Secondary Structure Prediction using Unassigned NMR Spectra. https://doi.org/10.1101/2023.10.09.561482

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