Topology in Biological Matter: New Ways to Use Entanglement in Structural Biology

We have been aware of the existence of knotted and slipknotted proteins for over 30 years; however, the potential of entanglement in proteins has not been fully explored. From the perspective of chemistry and soft condensed matter, it is known that knots can perform various functions, such as increasing thermal or mechanical strength. During my lecture, I will show that the vast majority of knotted proteins have the simplest type of knot, and that the presence of knots is not preferred in any of the three domains—Bacteria, Eukaryota, or Archaea. Moreover, there is no organism without at least one knotted protein [1,2].

Recent advances in machine learning methods in structural biology have opened up new perspectives for protein analysis and prediction. I will show new types of knot discovered in proteins [3, suggest possible folding pathway for 6_3, 8_3 knot types. I will demonstrate how machine learning techniques can be used to incorporate knotting into structural biology and biopolymers [4], enabling the design of fit-for-purpose new proteins that enhance or manipulate protein activity [5].

[1] Everything AlphaFold tells us about protein knots, A Perlinska, M Sikora, JI Sulkowska, Journal of Molecular Biology (2024)
[2] Conservation of knotted and slipknotted topology in transmembrane transporters, V Zayats, M Sikora, AP Perlinska, A Stasiulewicz, B Gren, JI Sulkowska, Biophysical Journal (2023), 122(23)
[3] AlphaKnot 2.0 – a web server for the visualization of proteins’ knotting and a database of knotted AlphaFold-predicted models, P Rubach, M Sikora, AI Jarmolinska, AP Perlinska, JI Sulkowska
Nucleic Acids Research (2024), 52(W1)
[4] Knots and θ-Curves Identification in Polymeric Chains and Native Proteins Using Neural Networks, Bruno da Silva, F., Gabrovšek, B., Korpacz, M., Luczkiewicz, K., Niewieczerzal, S., Sikora, M., & Sulkowska, J. I. Macromolecules (2024), 57(9)
[5] Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model, M Sikora, E Klimentova, D Uchal, D Sramkova, AP Perlinska, ML Nguyen, M Korpacz, R Malinowska, S Nowakowski, P Rubach, P Simecek, JI Sulkowska, Protein Science (2024), 33 (7)

Bio
https://jsulkowska.cent.uw.edu.pl/