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abYpap: Improvements to the Prediction of Antibody V H/V L Packing Using Gradient Boosted Regression

Boron, Veronica A; Martin, Andrew CR; (2023) abYpap: Improvements to the Prediction of Antibody V H/V L Packing Using Gradient Boosted Regression. Protein Engineering, Design and Selection , Article gzad021. 10.1093/protein/gzad021. (In press). Green open access

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Abstract

The Fv region of the antibody (comprising VH and VL domains) is the area responsible for target binding and thus the antibody’s specificity. The orientation, or packing, of these two domains relative to each other influences the topography of the Fv region, and therefore can influence the antibody’s binding affinity. We present abYpap, an improved method for predicting the packing angle between the VH and VL domains. With the large data set now available, we were able to expand greatly the number of features that could be used compared with our previous work. The machine-learning model was tuned for improved performance using 37 selected residues (previously 13) and also by including the lengths of the most variable ‘complementarity determining regions’ (CDR-L1, CDR-L2, and CDR-H3). Our method shows large improvements from the previous version, and also against other modelling approaches, when predicting the packing angle.

Type: Article
Title: abYpap: Improvements to the Prediction of Antibody V H/V L Packing Using Gradient Boosted Regression
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/protein/gzad021
Publisher version: https://doi.org/10.1093/protein/gzad021
Language: English
Additional information: © The Author(s) 2023. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: VH/VL packing, antibodies, machine learning, modelling, prediction
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/10182803
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