Seminarium Zakładu Biofizyki

Recent advances in Machine Learning methods in structural biology opened up new perspectives for protein analysis. Utilizing these methods allows us to go beyond the limitations of empirical research, and take advantage of the vast amount of generated data. We use a complete set of potentially knotted protein models identified in all high-quality predictions from the AlphaFold Database to search for any common trends that describe them [1,2]. We show that the vast majority of knotted proteins have the simplest type of knot and that the presence of knots is preferred in neither Bacteria, Eukaryota, or Archaea domains. On the contrary, the percentage of knotted proteins in any given proteome is around 0.4%, regardless of the taxonomical group. Moreover, we show through structural biology methods (X-ray, Cryo-EM), that AF can predict new types of knot in proteins [3] and neural network type models (NN) can be used to detect topology based on geometry [4] and LSTM is a good approach to. design protein with non-trivial topology [5]. In addition, I briefly discuss the use of quantum methods in combination with classical molecular dynamics and AF approach to determine the methylation reactions pathway based on knotted tRNA methyltransferases [6,7,8].1. Everything AlphaFold tells us about protein knots, A Perlinska, M Sikora, JI Sulkowska Journal of Molecular Biology (2024)2. AlphaKnot: server to analyze entanglement in structures predicted by AlphaFold methods, W Niemyska, P Rubach, BA Gren, ML Nguyen, W Garstka, F Bruno da Silva, EJ Rawdon, JI Sulkowska, Nucleic Acids Research (2022) 3. First crystal structure of double knotted protein TrmD-Tm1570 – inside from degradation perspective, B da Silva, I Lewandowska, A Kluza, S Niewieczerzal, R Augustyniak, JI Sulkowska, 2024 JACS (under review)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 SulkowskaProtein Science (2024), 33 (7)6. Nucleolar Essential Protein 1 (Nep1): Elucidation of enzymatic catalysis mechanism by molecular dynamics simulation and quantum mechanics study, M Jedrzejewski, B Belza, I Lewandowska, M Sadlej, AP Perlinska, R Augustyniak, T Christian, Y Hou, M Kalek, JI Sulkowska, Computational and Structural Biotechnology Journal (2023)7. Mg2+-Dependent Methyl Transfer by a Knotted Protein: A Molecular Dynamics Simulation and Quantum Mechanics Study, AP Perlinska, M Kalek, Y-M Hou, JI Sulkowska, ACS Catalysis (2020) 10(15):8058-80688. Methyl Transfer by Substrate Signaling from a Knotted Protein FoldT Christian*, R Sakaguchi*, AP Perlinska*, G Lahoud, T Ito, EA Taylor, S Yokoyama, JI Sulkowska, Y-M Hou, Nature Structural & Molecular Biology (2016) 23: 941-948

Bio: Dr hab. Joanna Sułkowska, prof. UW is a head of the "Interdisciplinary laboratory for modeling biological systems" at the Centre of New Technologies at the University of Warsaw. In 2007 she defended with distinction her doctoral dissertation in the field of biophysics, devoted to the characteristics of mechanical properties of proteins. In 2016 she obtained her habilitation at the Faculty of Chemistry of the University of Warsaw and professor position in 2018. For several years, as part of a postdoctoral internship, she worked at the University of California, San Diego. She spent several months as a visiting professor at MIT and the California Institute of Technology. She is an author of over 80 scientific publications, including Nature Structure & MB, JACS, PNAS, PRL, NAR, where she combines theoretical approach with experimental data. For her greatest scientific achievement so far, she considers a discovery and characterization of non-trivial topology in proteins such as knots, slipknots, lassos and theta curves, the determination of mechanisms of their formation and relationships with biological function. She has also worked successfully on antagonists for GPCR-type proteins (CB1 and CB2).Joanna Sulkowska has been awarded many times for her scientific achievements. She received e.g. Installation and Young Investigator award from the European Molecular Biology Organization (EMBO), grants from the National Science Centre, the Foundation for Polish Science, and the Ministry of Science and Higher Education in Poland (Idea Plus based on ERC Starting grant application). She is the winners of the 2018 National Science Centre Award in Poland in the field of Life Sciences (for people under 40 years), and award from MNiSW, 2020. She received the international prize Unesco-L'Oreal ''Rising talent''. She was chosen as a person of the year “MocArty – 2017” by Polish RMF Classic. She was also ranked among the group of 50 brave people and in the initiative Jutronauci by Gazeta Wyborcza (PL) in 2017. She gave as well many public lectures.