ArticleProceedings of the National Academy of Sciences of the United States of America2022
Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. 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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3 citing papers in PubMed.
- Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.Briefings in bioinformatics · 2023Article
- Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis.Sensors (Basel, Switzerland) · 2022Review
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8 authors.
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Abstract
Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.
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