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


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


Molecular characterization of AIFM2/FSP1 inhibition by iFSP1-like molecules


T. N. Xavier da Silva, Clemens Schulte, Ariane Nunes-Alves, H. Maric, José Pedro Friedmann Angeli

Cell Death and Disease, 2023


Diffusion of small molecule drugs is affected by surface interactions and crowder proteins


Debabrata Dey, Ariane Nunes-Alves, Rebecca C. Wade, Gideon Schreiber

iScience, vol. 25, 2022, p. 105088


View all

Courses


Anwendung von Computern in der Chemie

winter semester 2022/2023

- application of computers in chemistry; master degree


Computational Methods in Drug Design

summer semester 2023

- master degree


Applied Machine Learning in Chemistry

winter semester 2023/2024

- master degree

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