University of Salzburg
Salzburg | Website
Short Description
The server infrastructure comprises 16 high-performance computers with multi-core processors (up to 3 GHz):
8 x HP ProLiant
1 x supermicro 4U 2x4-core
1 x supermicro 1U 2x10-core
1 x supermicro 2U 2x10-core
1 x supermicro 1U 2x24-core
1 x supermicro AMD EPYC 7452 4 x 32-core
2 xU Intel Dual CPU
1 x Lynx Calleo / Intel Modular Server
Application: Storage of protein structure data, protein structure comparison/search, calculation of protein classifications, derivation of energy functions for protein structure analysis, immunoinformatics, computational systems biology, processing of sequencing raw data, integration of multi-omics data, analysis of single-cell data, modelling of biological networks, machine learning, etc.
In many cases, the results obtained are also made available to the scientific community as a web service.
The PC infrastructure comprises approx. 20 desktop computers (Linux, Windows, MacOS X).
Use: software development, data analysis, etc.
Contact Person
Assoc. Prof. Dr. Markus Wiederstein
Research Services
The methods developed at the CF (protein structure analysis, protein structure comparison, efficient protein structure search, immunoinformatics, etc.) are made available to the scientific community by means of software and web services.
Methods & Expertise for Research Infrastructure
Storage of protein structure data
Protein structure comparison/search
Calculation of protein classifications
Development of energy functions for protein structure analysis
Immunoinformatics
Computational Systems Biology
Processing of sequencing raw data
Integration of multi-omics data
Analysis of single-cell data
Modelling of biological networks
Machine Learning
Software development (stand-alone tools and web services)
Cancer Research Center (DKFZ), Heidelberg, Germany
Paracelsus Medical University, Salzburg
Medical University of Vienna, Vienna
Children's Cancer Research Institute, Vienna, Austria
University of Cambridge, UK
University of Navarra, Pamplona, Spain
2019-2022,
Nikolaus Fortelny,
Vienna Science and Technology Fund (WWTF),
https://www.wwtf.at/funding/programmes/ls/LS18-111/
2022
Lara-Astiaso, D., Goñi-Salaverri, A., Mendieta-Esteban, J., Narayan, N., Valle, C. D., Gross, T., Giotopoulos, G., Navarro-Alonso, M., Zazpe, J., Marchese, F., Torrea, N., Calvo, I. A., Lopez, C., Alignani, D., Lopez, A., Saez, B., Taylor-King, J. P., Prosper, F., Fortelny, N. & Huntly, B. J. P.
BioRxiv
https://doi.org/10.1101/2022.08.11.503571
Backbone distortions in lactam-bridged helical peptides
2022
Moazzam, A., Stanojlovic, V., Hinterholzer, A., Holzner, C., Roschger, C., Zierer, A. et al.
Journal of Peptide Science
https://doi.org/10.1002/psc.3400
Bet v 1 from birch pollen is a hypoallergen with vitamin D3 in the pocket
2021
Hufnagl K, Kromp L, Bianchini R, Afify SM, Wiederstein M, Redegeld FA et al.
Allergy
https://doi.org/10.1111/all.15052
Disease-related blood-based differential methylation in cystic fibrosis and its representation in lung cancer revealed a regulatory locus in PKP3 in lung epithelial cells
2021
Schamschula E., Lahnsteiner A., Assenov Y., Hagmann W., Zaborsky N., Wiederstein M. et al.
Epigenetics
https://doi.org/10.1080/15592294.2021.1959976
MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides
2021
Laimer, J., & Lackner, P.
International Journal of Molecular Sciences
https://doi.org/10.3390/ijms22010012
In silico Design of Phl p 6 Variants With Altered Fold-Stability Significantly Impacts Antigen Processing, Immunogenicity and Immune Polarization
2020
Winter P., Stubenvoll S., Scheiblhofer S., Joubert I.A., Strasser L., Briganser C et al.
Frontiers in Immunology
https://doi.org/10.3389/fimmu.2020.01824
TopMatch-web: pairwise matching of large assemblies of protein and nucleic acid chains in 3D
2020
Wiederstein M. & Sippl M.J.
Nucleic Acids Research
https://doi.org/10.1093/nar/gkaa366
Synergistic cross-talk of hedgehog and interleukin-6 signaling drives growth of basal cell carcinoma
2018
Sternberg, C., Gruber W., et al.
International Journal of Cancer
https://doi.org/10.1002/ijc.31724
MAESTROweb: a web server for structure based protein stability prediction
2016
Laimer J., Hiebl-Flach J., Lengauer D., Lackner P.
Bioinformatics
http://bioinformatics.oxfordjournals.org/content/early/2016/01/27/bioinformatics.btv769.abstract
Structure-Based Characterization of Multiprotein Complexes
2014
Wiederstein M., Gruber M., Frank K., Melo F., Sippl M.J.
Structure
http://dx.doi.org/10.1016/j.str.2014.05.005
D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements
2013
Laimer J., Zuzan C.J., Ehrenberger T., Freudenberger M., Gschwandtner S., Lebherz C., Lackner P.
BMC Bioinformatics
http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-140
Detection of spatial correlations in protein structures and molecular complexes
2012
Sippl M.J., Wiederstein M.
Structure
http://dx.doi.org/10.1016/j.str.2012.01.024
Real space refinement of crystal structures with canonical distributions of electrons
2011
Ginzinger S., Gruber M., Brandstetter H., Sippl M.J.
Structure
http://dx.doi.org/10.1016/j.str.2011.10.011
High-performance signal peptide prediction based on sequence alignment techniques
2008
Frank K., Sippl M.J.
Bioinformatics
http://dx.doi.org/10.1093/bioinformatics/btn422
ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins
2007
Wiederstein M., Sippl M.J.
Nucleic Acids Research
http://dx.doi.org/10.1093/nar/gkm290
NQ-Flipper: recognition and correction of erroneous asparagine and glutamine side-chain rotamers in protein structures
2007
Weichenberger C.X., Sippl M.J.
Nucleic Acids Research
http://dx.doi.org/10.1093/nar/gkm263