Universität Salzburg
Salzburg | Website
Kurzbeschreibung
Die Server-Infrastruktur umfasst 16 Hochleistungsrechner mit Multikernprozessoren (bis 3 GHz):
8 x HP ProLiant
1 x supermicro 4 HE 2x4-core
1 x supermicro 1 HE 2x10-core
1 x supermicro 2 HE 2x10-core
1 x supermicro 1 HE 2x24-core
1 x supermicro AMD EPYC 7452 4 x 32-Core
2 x HE Intel Dual CPU
1 x Lynx Calleo / Intel Modular Server
Einsatz: Speicherung von Proteinstrukturdaten, Proteinstrukturvergleich/-suche, Berechnung von Proteinklassifikationen, Ableitung von Energiefunktionen zur Proteinstrukturanalyse, Immunoinformatik, Computational Systems
Biology, Prozessierung von Sequencing-Rohdaten, Integration von multi-omics Daten, Analyse von single-cell Daten, Modellierung biologischer Netzwerke, Maschinelles Lernen, etc.
Vielfach werden die gewonnenen Ergebnisse auch als Web Service der scientific community zur Verfügung gestellt.
Die PC-Infrastruktur umfasst ca. 20 Desktoprechner (Linux, Windows, MacOS X)
Einsatz: Software-Entwicklung, Datenanalyse, etc.
Ansprechperson
Assoc. Prof. Dr. Markus Wiederstein
Research Services
Die an der CF entwickelten Methoden (Proteinstrukturanalyse, Proteinstrukturvergleich, effiziente Proteinstruktursuche, Immunoinformatik, etc.) werden der scientific community in Form von Software und Web Services zur Verfügung gestellt.
Methoden & Expertise zur Forschungsinfrastruktur
Speicherung von Proteinstrukturdaten
Proteinstrukturvergleich/-suche
Berechnung von Proteinklassifikationen
Ableitung von Energiefunktionen zur Proteinstrukturanalyse
Immunoinformatik
Computational Systems Biology
Prozessierung von Sequencing-Rohdaten
Integration von multi-omics Daten
Analyse von single-cell Daten
Modellierung biologischer Netzwerke
Maschinelles Lernen
Software-Entwicklung (stand-alone Tools und 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