Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain. Taft, Weber et al.

Published: 23 August 2022| Version 3 | DOI: 10.17632/pkg3jk26y6.3
Contributors:
,
Cédric Weber,
Beichen Gao,
Roy Alexander Ehling,

Description

Yeast displayed RBD-library NGS sequencing data from FACS sorted binding/escape populations to ACE2 and 13 different SARS-CoV-2 binding antibodies, used to train and evaluate predictive models in "Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain" (Taft J, Weber C, et al, 2022) Datasets are annotated by RBD sequence ('sequence_aa'), Binding Label (0 - escape, 1 - binding), distance from wild type RBD sequence ('Distance'), mAb tested ('Antibody'), and the RBM region and library each sequence is derived from ('Library') All sequences/data combined is available as "Taft_Weber_Cell2022_FullData.csv". Alternatively, datasets for individual mAbs, and individual libraries are available in the "Full_sets" folder, separated first by library and then by mAb.

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Institutions

Eidgenossische Technische Hochschule Zurich Department of Biosystems Science and Engineering

Categories

Machine Learning, Protein Engineering, Monoclonal Antibody, COVID-19

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