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Sułkowska, Joanna Ida, 2024, "Biologiczny kod zawęźlenia - identyfikacja wzoru zawęźlenia w biomolekułach na postawie metod AI", https://doi.org/10.58132/H0ZTAM, Dane Badawcze UW, V1
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2021/43/I/NZ1/03341 pt.: Biologiczny kod zawęźlenia - identyfikacja wzoru zawęźlenia w biomolekułach na postawie metod AI - Yearly Report
The dataset contains publications and related Data funded with the NCN grant 2021/43/I/NZ1/03341 for Joanna I. Sulkowska.
The main topic of those publications is related to biological, statistical, and physical analysis with the use of Artificial Intelligence methods for the better understanding of the proteins forming knots along their backbone.
The list contains 4 publications with additional files sorted as follows:
1: AlphaFold predicts novel human proteins with knots.
2: Proteins containing 6-crossing knot types and their folding pathways.
3: Conservation of knotted and slipknotted topology in transmembrane transporters.
4: Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.
Deposited data uses PDF formats for documents; PNG and JPEG formats for images and tables.
knotted proteins, big data, deep learning
Sikora, M., Flapan, E., Wong, H., Rubach, P., Garstka, W., Niewieczerzal, S., Rawdon, E.J. and Sulkowska, J.I., 2023. Proteins containing 6-crossing knot types and their folding pathways. bioRxiv, pp.2023-06. https://doi.org/10.1101/2023.06.16.545156 https://repozytorium.uw.edu.pl//handle/item/160568 doi: 10.1101/2023.06.16.545156
CC BY - Creative Commons Attribution 4.0
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