End-to-end learning for protein docking
Title: End-to-end learning for protein docking
SNIC Project: Berzelius-2021-64
Project Type: LiU Berzelius
Principal Investigator: Arne Elofsson <arne@bioinfo.se>
Affiliation: Stockholms universitet
Duration: 2021-12-01 – 2022-06-01
Classification: 10203
Homepage: https://bioinfo.se/
Keywords:

Abstract

We are currently involved in a large scale project to predict the structure of all human protein-protein interactions. This unfortunately demands quite some (at least temporary) disk space (in addition to the already allocated GPU time we have received). Therefore we apply for in total 100 Tb diskspace We already have submitted two manuscripts using this resource, both have been well received by the community (preprints retweeted 350 and 650 times). Now we are (in collaboration with EBI) working on Two manuscript submitted thanks to Berzelius: https://www.biorxiv.org/content/10.1101/2021.09.15.460468v1 Submitted to nature communication https://www.biorxiv.org/content/10.1101/2021.09.26.461876v1 Submitted to Cell