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Protein embeddings and deep learning predict binding residues for various ligand classes
View ORCID ProfileMaria Littmann, View ORCID ProfileMichael Heinzinger, View ORCID ProfileChristian Dallago, View ORCID ProfileKonstantin Weissenow, View ORCID ProfileBurkhard Rost
doi: https://doi.org/10.1101/2021.09.03.458869
Maria Littmann
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
Michael Heinzinger
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
Christian Dallago
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
Konstantin Weissenow
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
Burkhard Rost
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
3Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
4Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY 10032, USA
Posted September 05, 2021.
Protein embeddings and deep learning predict binding residues for various ligand classes
Maria Littmann, Michael Heinzinger, Christian Dallago, Konstantin Weissenow, Burkhard Rost
bioRxiv 2021.09.03.458869; doi: https://doi.org/10.1101/2021.09.03.458869
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