The group of Theoretical Structural Biology at the Technical University of Berlin is led by Dr. Ariane Nunes Alves. One of our main interests is the development and application of computational methods to predict kinetic rates for protein-ligand binding and enzyme-substrate binding. Knowledge of binding pathways and fine tuning of kinetic rates can lead to better drugs and improved enzyme catalysis. The main methods we use to study binding kinetics are molecular dynamics simulations and machine learning.
Another main interest is to understand how crowded environments affect protein-ligand binding and enzyme catalysis. While experiments and simulations to characterize proteins are usually performed using low concentration of proteins, the environment inside cells is crowded with different macromolecules. Such environment may affect binding and catalysis through excluded volume effects and quinary interactions.
Another main interest is to understand how crowded environments affect protein-ligand binding and enzyme catalysis. While experiments and simulations to characterize proteins are usually performed using low concentration of proteins, the environment inside cells is crowded with different macromolecules. Such environment may affect binding and catalysis through excluded volume effects and quinary interactions.
Publications
Farzin Sohraby, Jing-Yao Guo, Ariane Nunes-Alves
Journal of Chemical Information and Modeling, vol. 65, 2025, pp. 589-602
Farzin Sohraby, Ariane Nunes-Alves
The Journal of Physical Chemistry Letters, vol. 16, 2025, pp. 7960-7967
Agonists and Antagonists Show Different Unbinding Paths from the TLR8 Receptor
Valerij Talagayev, Gerhard Wolber, Ariane Nunes-Alves
Journal of Chemical Information and Modeling, vol. 65, 2025, pp. 7678-7688
Giulia D'Arrigo, Daria B Kokh, Ariane Nunes-Alves, Rebecca C Wade
Communications Biology, vol. 7, 2024, p. 1159
Ganna Gryn’ova, Tristan Bereau, Carolin Müller, Pascal Friederich, Rebecca C. Wade, Ariane Nunes-Alves, Thereza A. Soares, Kenneth Jr. Merz
Journal of Chemical Information and Modeling, vol. 64, 2024, pp. 5737-5738
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