Group

Elsa Sánchez-García

Prof. Dr. Elsa Sánchez-García


Publications Curriculum Vitae Google Scholar Profile

Prof. Sanchez-Garcia's field of research is computational chemistry and computational chemical biology, with emphasis on complex biological systems, solvent effects and reactive intermediates. Before she established the Group of Computational Biochemistry within the Faculty of Biology of the University of Duisburg-Essen, she was independent group leader at the Theory Department directed by Prof. Walter Thiel in the Max Planck Institute for Coal Research (Mülheim an der Ruhr). At the UDE, the Computational Biochemistry group works on investigating the properties, reactive behavior and applications of diverse systems, from reactive species to protein complexes. Among our main interests are the in silico design of ligands with therapeutic potential and the regulation of enzymatic activity and protein-protein interactions by means of computational mutagenesis, ligands and solvents. Implementation and application of novel computational tools are central to the research.

Jörg Tatchen

Dr. Jörg Tatchen


Publications

Carbenes are organic compounds which carry a bivalent, unsaturated carbon atom. Transient carbenes are unstable and reactive species which chemistry is highly versatile and depends on the electronic spin state (singlet or triplet).  In cooperation with experimentalists, our group established that the reactivity of aryl compounds can be drastically influenced by solvent effects. By means of quantum-chemical computational tools, I investigate spin-orbit coupling and rates of singlet-triplet interconversion (intersystem crossing, ISC) in arylcarbenes over a wide range of temperatures. Quantum-chemical and quantum-chemical / molecular mechanics (QM/MM) methods are used to model solvation effects. Also, quantum-mechanical tunneling under low-temperature matrix conditions is explored for unimolecular and bimolecular reactions of arylcarbenes.

Joel Mieres Perez

Dr. Joel Mieres Perez


Publications

My work is related to the theoretical study of biomolecules with potential therapeutic activity. The goal is to get new insights on such systems for the in-silico design of ligands with tailor-made properties. In addition, I investigate the spectroscopic behavior and chemical reactivity of organic molecules, to predict reaction pathways which can be then tested by the experimentalists. I also work on systems where quantum tunneling plays a key role and develop theoretical approaches for the prediction of tunneling probabilities.

Yasser B. Ruiz Blanco

Dr. Yasser B. Ruiz Blanco


Google Scholar Profile

My research focuses on machine-learning methods and biomolecular simulations. I am very interested on protein modeling and on the simulation of protein – ligand interactions, multi-scale approaches, methodological implementations for proteome-wide prediction of postranslational modifications and proteome-wide prediction of protein-protein interactions as well as on the design of antimicrobial peptides using machine learning and atomistic simulation approaches. With my work, I aim to achieve a synergistic background of computational methods from chemistry and computer sciences to both support biological evidences and make novel designs using rational-computer-aided approaches.

Sunil Kumar Tripathi

Dr. Sunil Kumar Tripathi


Google Scholar Profile

As part of my research I use computational techniques to investigate biomolecular systems with emphasis on conformational changes, protein-protein and protein-ligand interactions. I employ MD simulations, advanced sampling techniques, free energy calculations and hybrid approaches for the identification and design of small molecules as inhibitors of therapeutically important drug targets.

Pradeep Pant

Dr. Pradeep Pant


Publications

My research work focuses on elucidating the role of solvent as a function of pH to gather insights into the conformational and functional adaptations of biomacromolecular systems using advanced MD simulations, hybrid QM/MM techniques and free energy perturbation calculations. This will help in delineating the mechanism as how the rigidity/flexibility associated with polypeptide chains influence the resultant structure of a macromolecule, which in turn dictates the function of a macromolecule upon varying the pH of a solution.

Sandra Romero Molina

Sandra Romero Molina


Publications

My research is focused in Bioinformatics. My main interest is to develop integrated methodologies combining novel machine-learning-based predictors and computational chemistry methods. Particularly, I am involved in the development of protein-protein interaction models, with application for the de novo design of bioactive peptides and as scoring function for docking algorithms.

Julio C. Vieyto Nuñez

Julio C. Vieyto Nuñez


A big area of interest for me is biomolecular modelling using state-of-the-art approaches in molecular dynamics simulations and hybrid techniques. My research is mainly focused on the computational study of supramolecular ligands on proteins; which includes ligand binding sites prediction and binding free energy calculations as well as molecular docking oriented to in silico drug design. I am also interested in the study of the influence of different solvent mixtures on enzymatic reactions and protein properties.

Cornelia Yano

Cornelia Yano


Mailing Address

Universität Duisburg-Essen
Universitätsstr. 2
45141 Essen

Julia Wille, University of Ulm (November 2019)

Dr. Matthias Heyden, Arizona State University (June-July 2018)

Dr. Kenny Bravo-Rodriguez (2011 – 2019)

Dr. Angela Rodriguez Serrano (2017 - 2019)

Dr. Pandian Sokkar (2013 - 2018)

Dr. Sumit Mittal (2013 - 2018)

Amandeep Singh (internship 2018)

Figures generated using the VMD software. VMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign.

Humphrey, W., Dalke, A. and Schulten, K., `VMD -Visual Molecular Dynamics, J. Molecular Graphics, 1996, vol. 14, pp. 33-38.

http://www.ks.uiuc.edu/Research/vmd/