ArticleAmino acids2022
Structural insights into the amino acid usage variations in the profilin gene family.
Article in Amino acids, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.Journal of proteome research · 2026Article
- Perspectives of Proteomics in Respiratory Allergic Diseases.International journal of molecular sciences · 2023Review
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Profilin protein is present ubiquitously in all forms of life and is allied with allergic responses among atopic individuals. In addition to this, profilins from various food sources are also associated with IgE cross-reactivity and are thus classified as pan-allergens. The present study unravels the physicochemical basis of differential amino acid usage patterns observed in the profilin gene family. Correspondence analysis based on amino acid usage of allergen and non-allergen profilins revealed discrete clusters among them, signifying differential patterns of amino acid usage. The amino acids, namely methionine, proline, histidine, glutamine, glutamic acid, tryptophan and glycine were found to be more frequently utilised by the allergen profilins compared to the non-allergens. Correlation analysis revealed that physicochemical features like protein disorder, trypsin digestion and solubility differed significantly among the allergen and non-allergen profilins, thus supporting the observations from correspondence analysis. In addition, comprehensive sequence analysis revealed that the allergen profilins possess conserved motifs which may correlate with their distinct physicochemical features. An in-depth structural analysis revealed that the over-represented amino acids in allergen profilins have a propensity of being exposed on the surface, which may be attributed to their distinct allergenic characteristics. The distinguished physicochemical features observed among allergens and non-allergens can be employed as descriptors to develop machine learning-based allergenicity prediction models.
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