Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.
In: Proceedings of the National Academy of Sciences of the United States of America, Jg. 119 (2022-05-03), Heft 18, S. e2202713119
Online
academicJournal
Zugriff:
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.
| Titel: |
Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.
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| Autor/in / Beteiligte Person: | Ren, H ; Zhang, Q ; Wang, Z ; Zhang, G ; Liu, H ; Guo, W ; Mukamel, S ; Jiang, J |
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| Zeitschrift: | Proceedings of the National Academy of Sciences of the United States of America, Jg. 119 (2022-05-03), Heft 18, S. e2202713119 |
| Veröffentlichung: | Washington, DC : National Academy of Sciences, 2022 |
| Medientyp: | academicJournal |
| ISSN: | 1091-6490 (electronic) |
| DOI: | 10.1073/pnas.2202713119 |
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