Computational Biology

Overview

The main research focus of the Computational Biology Group is on the interaction of proteins and peptides with phospholipid membranes as models for biomembranes, studied in silico using atomistic and coarse-grained molecular dynamics simulations, pursuing a multiscale approach. The research aims to elucidate (i) on the role of lipids in membrane protein structure, dynamics, function, and protein localization, (ii) on the effect of membrane-embedded or absorbed proteins on the membrane shape, and (iii) to characterize the composition, structure and dynamics of nano- and micrometer-sized biomembrane domains and its coupling to biomembrane function.

A methodological focus is on the force field development for protein-lipid systems, structure-based free energy predictions for the stability of proteins and protein binding affinities with a focus on membrane-associated proteins.

People

Prof. Rainer Böckmann

Email: rainer.boeckmann@fau.de

Methods and Software

Molecular dynamics simulations are performed using Gromacs with both atomistic and coarse-grained force fields. Additionally, the Computational Biology Group developed several tools for setup and analysis of simulations and they actively optimize lipid force fields.

• • GroPBS: Fast Solver for Implicit Electrostatics of Biomolecules https://doi.org/10.3389/fbioe.2015.00186
  • Computational Lipidomics with insane: A Versatile Tool for Generating Custom Membranes for Molecular Simulations https://doi.org/10.1021/acs.jctc.5b00209
  • High-Throughput Simulations of Dimer and Trimer Assembly of Membrane Proteins. The DAFT Approach https://doi.org/10.1021/ct5010092
  • Going Backward: A Flexible Geometric Approach to Reverse Transformation from Coarse Grained to Atomistic Models https://doi.org/10.1021/ct400617g
  • Extension of the LOPLS-AA Force Field for Alcohols, Esters, and Monoolein Bilayers https://doi.org/10.1021/acs.jpcb.5b08569
  • Optimization of the OPLS-AA Force Field for Long Hydrocarbons https://doi.org/10.1021/ct200908r

Research Projects

Main research projects within the group focus on

• the role of the lipid environment on receptor dimerization, oligomerization, and signaling
• the characterization of the structure and dynamics of biomembrane domain formation, membrane structural integrity, and environmental effects on the formation of biomembrane nano- and microdomains
• the localization and sorting of immune receptors to biomembrane domains, its dependency on receptor activation and coupling to function

Selected Publications

• A. Kirsch and R. A. Böckmann. Coupling of membrane nanodomain formation and enhanced electroporation near phase transition. Biophys. J. (2019) 116(11): 2131-2148
• Sun and R.A. Böckmann, Membrane Phase Transition during Heating and Cooling: Molecular Insight into Reversible Melting, Europ. Biophys. J., 2018, 47, 151-164. https://doi.org/10.1007/s00249-017-1237-3
• Gahbauer, K. Pluhackova, R.A. Böckmann, Closely related, yet unique: Distinct homo- and heterodimerization patterns of G protein coupled chemokine receptors and their fine-tuning by cholesterol, PLoS Comp. Biol., 2018, 14, e1006062. https:/doi.org/10.1371/journal.pcbi.1006062
• Pluhackova, S. Gahbauer, F. Kranz, T.A. Wassenaar, and R.A. Böckmann, Dynamic Cholesterol-conditioned Dimerization of the G Protein Coupled Chemokine Receptor Type 4, PLoS Comp. Biol., 2016, 12, e1005169. https://doi.org/10.1371/journal.pcbi.1005169
• Sonja A. Kirsch and Rainer A. Böckmann, Membrane Pore Formation in Atomistic and Coarse-Grained Simulations, BBA-Biomembranes, 2016, 1858, 2266-2277. https://doi.org/10.1016/j.bbamem.2015.12.031
• Rainer A. Böckmann, Bert L. de Groot, Sergej Kakorin, Eberhard Neumann, Helmut Grubmüller, Kinetics, Statistics, and Energetics of Lipid Membrane Electroporation Studied by Molecular Dynamics Simulations, Biophys. J., 2008, 95, 1837-1850. https://doi.org/10.1529/biophysj.108.129437
• Friess, K. Pluhackova, R.A. Böckmann, Structural Model of the mIgM B-cell Receptor Transmembrane Domain from Self-Association Molecular Dynamics Simulations, Front. Immunol., 2018, 9, 2947. https://doi.org/10.3389/fimmu.2018.02947
• Benedix, C. M. Becker, B. L. de Groot, A. Caflisch, R. A. Böckmann. Predicting Free Energy Changes Using Structural Ensembles. Nature Methods 6:3-4 (2009)