Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets

Minotto Thomas (), Robert Philippe A. (), Hobæk Haff Ingrid () and Sandve Geir K. ()
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Minotto Thomas: Department of Mathematics, 6305 University of Oslo , Oslo, Norway
Robert Philippe A.: Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
Hobæk Haff Ingrid: Department of Mathematics, 6305 University of Oslo , Oslo, Norway
Sandve Geir K.: Department of Informatics, 6305 University of Oslo , Oslo, Norway

Statistical Applications in Genetics and Molecular Biology, 2024, vol. 23, issue 1, 14

Abstract: Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity.

Keywords: antibody-antigen binding; synthetic data; emergent properties; logistic regression; main effects; statistical interactions (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/sagmb-2023-0027

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