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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
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SurfDiff: protein surface profiling for selective or broadly reactive epitope prioritisation in binder and immunogen
Benedikt Jedlitzke1, Oliver Wissett1, Tung H Nguyen1,2,3
1Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CB2 1EW, Cambridge, UK.
Biorxiv : the Preprint Server for Biology
|January 9, 2026
Summary
SurfDiff is a new computational framework that compares protein surfaces to identify unique sites for drug and vaccine development. It accurately predicts binding selectivity without needing binder information, outperforming existing methods.
Area of Science:
- Structural biology
- Computational chemistry
- Bioinformatics
Background:
- Rational design of targeted therapeutics and vaccines relies on identifying specific protein surface features like epitopes or ligandable sites.
- Accurate comparison of protein surfaces is crucial for predicting binding affinity and selectivity.
- Existing bioinformatic metrics often fall short in predicting experimental outcomes for diverse binders and targets.
Purpose of the Study:
- To introduce SurfDiff, a novel framework for structure- and sequence-informed protein surface comparison.
- To enable accurate prediction of residue-level uniqueness and surface-level similarity, selectivity, and discriminability scores.
- To provide a tool for designing selective or cross-reactive binders and optimizing immunogen selection.
Main Methods:
- SurfDiff combines local structural alignments with neighborhood analysis considering physicochemical and spatial properties.
- It performs one-to-one and one-to-many protein surface comparisons.
- Residue-level uniqueness and similarity scores are aggregated into surface-level metrics, including a discriminability score.
Main Results:
- SurfDiff scores reliably predict experimental selectivity for various binders (small molecules, peptides, antibodies) across diverse targets (viral antigens, GPCRs, etc.).
- Scores correlate strongly with binding and neutralization data across pathogen variants.
- SurfDiff significantly outperforms traditional bioinformatic metrics like sequence substitution matrices and structural similarity measures.
Conclusions:
- SurfDiff offers a generalizable and interpretable approach to protein surface profiling.
- The framework facilitates the design of selective or cross-reactive binders and aids in immunogen selection.
- SurfDiff is available as open-source software and a webserver for broader accessibility.
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