Deciphering the Structural Dynamics of Chicken Albumin through Molecular Dynamics Simulations and Deep Learning

Area of Science:

• Biochemistry
• Immunology
• Computational Biology

Background:

• Serum albumin (SA) is a major blood protein and allergen.
• Chicken serum albumin (CHSA) cross-reactivity with human serum albumin (HSA) is linked to chicken meat allergy.

Purpose of the Study:

• To compare structural and dynamic characteristics of CHSA and HSA.
• To elucidate the structural basis of immunological cross-recognition between CHSA and HSA.
• To identify molecular differences influencing allergenicity and binding properties.

Main Methods:

• Molecular Dynamics (MD) simulations.
• Data-driven artificial intelligence (AI) analyses, including deep learning and explainable AI (Testing with Concept Activation Vectors).

Main Results:

• CHSA and HSA exhibit comparable protein compactness and backbone flexibility, with CHSA being slightly more compact.
• Conserved scissor-like motion in Domains I and III of both proteins suggests structural mimicry.
• CHSA has larger drug-binding sites (Sudlow sites I and II) than HSA.
• AI models effectively distinguished CHSA from HSA based on subtle spatial and dynamic differences.

Conclusions:

• Structural and dynamic similarities between CHSA and HSA facilitate antibody cross-recognition and allergy.
• Nuanced differences in flexibility, solvent exposure, and stabilization identified by AI may impact epitope presentation.
• Findings provide molecular insights into CHSA-HSA interactions, relevant for biomedical applications and allergy research.