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Protein Sci ; 33(4): e4936, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38501461

RESUMO

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on ß-sheet-ß-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.


Assuntos
Anticorpos , Dobramento de Proteína , Modelos Moleculares , Conformação Proteica em Folha beta , Domínios de Imunoglobulina
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