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


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


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


pH-dependence of the Plasmodium falciparum chloroquine resistance transporter is linked to the transport cycle


Fiona Berger, Guillermo M. Gomez, Cecilia P. Sanchez, Britta Posch, Gabrielle Planelles, Farzin Sohraby, Ariane Nunes-Alves, Michael Lanzer

Nature Communications, 2023


Advances in computational methods for ligand binding kinetics


Farzin Sohraby, Ariane Nunes-Alves

Trends in Biochemical Sciences, 2023


AlphaFold2 in Molecular Discovery


Ariane Nunes-Alves, Kenneth Merz

Journal of Chemical Information and Modeling, vol. 63, 2023, pp. 5947-5949


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Courses


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


Computational methods in drug design

summer semester 2023

- master degree


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