PhD Scientific Days 2019

Budapest, April 25–26, 2019

Using cryo-EM maps to determine protein transmembrane regions

Csizmadia, Georgina

Georgina Csizmadia1,#, Bianka Farkas1,2,#, Eszter Katona1,3, Gábor E. Tusnády4, Tamás Hegedűs1

1 Department of Biophysics and Radiation Biology, Semmelweis University, and MTA-SE Molecular Biophysics Research Group, HAS, Budapest, HU,
2 Faculty of Information Technology and Bionics, PPKE, Budapest, HU,
3 University College London, London, UK,
4 "Momentum" Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, HAS,
# equally contributed

Language of the presentation

English

Text of the abstract

Introduction: Transmembrane (TM) proteins play an important role in many cellular processes and are highly significant drug targets. In order to understand TM protein function and develop novel therapies targeting diseases associated with TM proteins, it is crucial to define the localization of transmembrane regions.

Aims: Experimental data on the boundaries of membrane embedded regions is sparse. As this information is present in cryo-electron microscopy (cryo-EM) density maps, our aim was to utilize it for determining membrane regions.

Method: Therefore, we developed a computational pipeline, which requires a cryo-EM map, the corresponding atomistic structure, and the potential bilayer orientation determined by TMDET algorithm as input. The resulted output includes the residues assigned to the bulk water phase, lipid interface, and the lipid hydrophobic core.

Results: Based on this method, we built a database involving published cryo-EM maps with a resolution better than 4 Å and also a web application to allow the analysis of unpublished densities. Unlike previous approaches, our method presents the membrane region as a volume with boundaries that follows the shape of the lipid environment, not as a slab with parallel edges.

Conclusion: Our pipeline provides the first experimental data set on TM regions at atomic resolution. Our method also allows the comparison of existing in silico predictions with experiments. The database and server is available at http://memblob.hegelab.org.

Support: NKFIH-111678, NKFI-127961, CFF HEGEDU18I0, KIFÜ HPC, MTA Wigner GPU Laboratory, NVIDIA Corporation, and Semmelweis Science and Innovation Fund.

Data of the presenter

Doctoral School: Basic Medicine
Program: Cellular and molecular biophysics
Supervisor: Tamás Hegedűs
E-mail address: g.csizmadia@hegelab.org