Theoretical Structural Biology group


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. 

Publications


PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases


Farzin Sohraby, Jing-Yao Guo, Ariane Nunes-Alves

Journal of Chemical Information and Modeling, vol. 65, 2025, pp. 589-602


Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD


Giulia D'Arrigo, Daria B Kokh, Ariane Nunes-Alves, Rebecca C Wade

Communications Biology, vol. 7, 2024, p. 1159


EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning


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


Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells


Apoorva Mathur, Rikhia Ghosh, Ariane Nunes-Alves

Journal of Chemical Information and Modeling, vol. 64, 2024, pp. 9063-9081


Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase


Farzin Sohraby, Ariane Nunes-Alves

Journal of Chemical Information and Modeling, vol. 64, 2024, pp. 4193-4203


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Courses


Applied machine learning in chemistry - block course

winter semester 2024/2025

- master degree


Applied machine learning in chemistry

winter semester 2024/2025

- master degree


Computational methods in drug design

summer semester 2024

- master degree


Applied machine learning in chemistry

winter semester 2023/2024

- master degree


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