ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, T. Fehér, Christoph M. Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost
This paper trains and evaluates language models on protein sequences to predict structural and localization features, achieving state-of-the-art results without evolutionary information.
IEEE Transactions on Pattern Analysis and Machine Intelligence · Jul 7, 2021Peer-reviewedBioinformatics