Academic Journal
Linear antibody epitope prediction using AlphaFold2.
| Τίτλος: | Linear antibody epitope prediction using AlphaFold2. |
|---|---|
| Συγγραφείς: | DeRoo J; School of Biomedical Engineering, Colorado State University, Fort Collins, United States., Terry JS; Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins, United States., Zhao N; Department of Biochemistry and Molecular Genetics, University of Colorado-Anschutz Medical Campus, Aurora, United States., Stasevich TJ; Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, United States., Snow C; School of Biomedical Engineering, Colorado State University, Fort Collins, United States.; Department of Chemical & Biological Engineering, Colorado State University, Fort Collins, United States., Geiss BJ; School of Biomedical Engineering, Colorado State University, Fort Collins, United States.; Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins, United States. |
| Πηγή: | ELife [Elife] 2026 Sep 18; Vol. 13. Date of Electronic Publication: 2026 Sep 18. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: eLife Sciences Publications, Ltd Country of Publication: England NLM ID: 101579614 Publication Model: Electronic Cited Medium: Internet ISSN: 2050-084X (Electronic) Linking ISSN: 2050084X NLM ISO Abbreviation: Elife Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cambridge, UK : eLife Sciences Publications, Ltd., 2012- |
| Ιατρικοί όροι (MeSH): | Epitopes*/immunology , Epitopes*/chemistry , Epitope Mapping*/methods , Computational Biology*/methods, Antibodies, Monoclonal/immunology ; Antibodies, Monoclonal/chemistry ; Prediction Algorithms ; Algorithms ; Humans |
| Περίληψη: | Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive, and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA/Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions were grafted onto several different antibody framework regions in the single-chain antibody fragment format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid-targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment databases. PAbFold is available at https://github.com/jbderoo/PAbFold. (© 2024, DeRoo et al.) |
| Competing Interests: | JD, JT, NZ, CS, BG No competing interests declared, TS Reviewing editor, eLife |
| Σχόλια: | Update of: bioRxiv. 2024 Dec 20:2024.04.19.590298. doi: 10.1101/2024.04.19.590298.. (PMID: 38659833) |
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| Grant Information: | R01AI132668 United States NH NIH HHS; R01AI168459 United States NH NIH HHS; R56 AI155897 United States AI NIAID NIH HHS; R01 AI132668 United States AI NIAID NIH HHS; R56AI155897 United States NH NIH HHS; R01 AI168459 United States AI NIAID NIH HHS |
| Contributed Indexing: | Keywords: AlphaFold2; antibody; computational biology; epitope; immunology; inflammation; systems biology; viruses |
| Substance Nomenclature: | 0 (Epitopes) 0 (Antibodies, Monoclonal) |
| Entry Date(s): | Date Created: 20260918 Date Completed: 20260918 Latest Revision: 20260920 |
| Update Code: | 20260920 |
| PubMed Central ID: | PMC13588620 |
| DOI: | 10.7554/eLife.98369 |
| PMID: | 42758531 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2050-084X |
|---|---|
| DOI: | 10.7554/eLife.98369 |