Integrative Computational Prediction Strategy for Antibody-Antigen Binding

A computational dataset supporting a strategy to predict antibody-antigen binding, using 14 antibody-bound complexes with 9 diverse antigens. The data was generated by Mehmet Emin Aygen and last updated on 2026-05-27. The study includes results from the ESSA and EPIGUIDE methods for predicting binding residues and poses, with comparisons to AlphaFold3.

Use Cases

• Benchmarking epitope prediction algorithms based on the 14 antibody-antigen complexes.
• Guiding molecular docking simulations using the ESSA method's essential residue rankings.
• Developing automated antibody-antigen binding workflows based on the described EPIGUIDE strategy.
• Comparing coarse-grained modeling efficiency against all-atom simulations for binding site screening.

Strengths

• Includes a rigorous test case on Interleukin-1β (IL-1β) with four distinct epitope regions.
• Dataset size is 187.3 MB, suggesting substantial computational output.
• The computational strategy was validated on 11 diverse antibody-bound complexes with successful results.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Generated via computational strategy involving epitope/paratope prediction, molecular docking (ClusPro), and molecular dynamics simulations.

Freshness

Last updated 2026-05-27 15:06:48; freshness should be verified.