to our understanding how cells work and how diseases can be cured.
and interactions in cells and make them universally accessible.
and interactions in cells and make them universally accessible.
and interactions in cells and make them universally accessible.
and interactions in cells and make them universally accessible.
LATEST PUBLICATIONS
Reliable identification of protein-protein interactions by crosslinking mass spectrometry
Retention Time Prediction Using Neural Networks Increases Identifications in Crosslinking Mass Spectrometry
LAB NEWS
We are proud to share our review “Leveraging crosslinking mass spectrometry in structural and cell biology” https://www.cell.com/structure/fulltext/S0969-2126(21)00419-6 for an upcoming @structure_CP special issue. (1/2)
Happy to share our new preprint! We provide a methodological understanding of the key parameters that govern crosslink identification and hope to pave the way for simplified workflows that a wider number of labs can use. https://www.biorxiv.org/content/10.1101/2021.11.23.469675v1
Protein-protein interactions (PPIs) are on the basis of life yet their discovery is complicated - CLMS offers an exciting opportunity for their study but error control remained controversial (1/10)
I am delighted and honoured by being elected into the ranks of EMBO. Thanks to many friends, colleagues and collaborators, especially in my lab and at @WTcell https://twitter.com/EMBO/status/1402234053332508677
EMBO is pleased to announce the 64 new #EMBO Members and Associate Members.
Congratulations and welcome!
Find out more at: https://bit.ly/3ioNs4Y
Our latest research on the use of deep learning for retention time prediction in crosslinking mass spectrometry has just been published with @SpringerNature in @NatureComms. Find out more here: https://www.nature.com/articles/s41467-021-23441-0
@RappsilberLab @SvenHGiese @LrSinn
