ArticleMolecules (Basel, Switzerland)2024
Modeling Peptide-Protein Interactions by a Logo-Based Method: Application in Peptide-HLA Binding Predictions.
Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Recent advances in structural and functional diversities of cancer lncRNA-encoded peptides: current opportunities and challenges for enhancing cancer diagnosis and treatment.Cancer cell international · 2026Review
- Molecular Insights into Tumor Immunogenicity.Current issues in molecular biology · 2025Article
- In Silico Identification of Peanut Peptides Suitable for Allergy Immunotherapy in HLA-DRB1*03:01-Restricted Patients.Pharmaceuticals (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
Peptide-protein interactions form a cornerstone in molecular biology, governing cellular signaling, structure, and enzymatic activities in living organisms. Improving computational models and experimental techniques to describe and predict these interactions remains an ongoing area of research. Here, we present a computational method for peptide-protein interactions' description and prediction based on leveraged amino acid frequencies within specific binding cores. Utilizing normalized frequencies, we construct quantitative matrices (QMs), termed 'logo models' derived from sequence logos. The method was developed to predict peptide binding to HLA-DQ2.5 and HLA-DQ8.1 proteins associated with susceptibility to celiac disease. The models were validated by more than 17,000 peptides demonstrating their efficacy in discriminating between binding and non-binding peptides. The logo method could be applied to diverse peptide-protein interactions, offering a versatile tool for predictive analysis in molecular binding studies.
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