In Silico Approaches for Identifying the Structure of a Glucagon Family Protein

Introduction: Interest in functional proteins derived from food sources has increased due to their potential therapeutic effects across a wide range of diseases. Studying the biological activity of food proteins with antihypertensive properties through conventional methods is costly and labor-intensive. Therefore, computational approaches capable of predicting the formation of bioactive peptides from animal-derived sources, as well as analyzing the relationship between protein structure and function, have gained new significance in the scientific domain. The use of in silico proteomic analysis methods may help address the global challenge of providing sufficient high-quality protein by enabling the development of functional food products.

Purpose: To identify the structure and functional domains of a low-molecular-weight protein from porcine stomach using in silico methods (UniProt and STRING) in order to evaluate its antihypertensive potential.

Materials and Methods: A protein with a molecular weight of approximately 10.7 kDa, isolated from porcine stomach, was characterized using in silico approaches based on UniProtKB (for functional annotation) and STRING (for protein–protein interaction network analysis), followed by structural modeling.

Results: Protein P01284, belonging to the glucagon family and consisting of 75 amino acids (with a molecular weight of 8.5 kDa), was identified. Based on domain analysis and protein–protein interaction profiling, the protein is presumed to exhibit vasoactive properties.

Conclusion: The findings support the utility of in silico analysis for predicting the bioactivity of food-derived proteins and justify further in vivo investigation of P01284 as a nutraceutical antihypertensive agent.

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