Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets.
Stat Appl Genet Mol Biol
; 23(1)2024 Jan 01.
Article
em En
| MEDLINE
| ID: mdl-38563699
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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Anticorpos
/
Antifibrinolíticos
Idioma:
En
Revista:
Stat Appl Genet Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Noruega