# Antibodies and cryptographic hash functions: quantifying the specificity paradox

**Authors:** Robert J. Petrella

PMC · DOI: 10.3389/fimmu.2025.1585421 · 2025-11-05

## TL;DR

This paper explains how antibodies can be both highly specific and multispecific, similar to cryptographic hash functions, using mathematical models.

## Contribution

The paper introduces a novel analogy between antibody behavior and cryptographic hash functions to explain immune specificity and degeneracy.

## Key findings

- Antibodies achieve high specificity and degeneracy by decoupling these properties, similar to secure hash algorithms.
- Mathematical models show how new antibodies avoid cross-reactivity with self-antigens despite multispecificity.
- Polyclonal binding likely enhances the overall specificity of the immune response.

## Abstract

The specificity of the immune response is critical to its biological function, yet the generality of immune recognition implies that antibody binding is multispecific or degenerate. The current work explores and quantifies this paradox through a systems analysis approach that incorporates set theoretic ideas and an application of structural and statistical modeling to prior experimental immunological and biochemical data. Order-of-magnitude estimates are computed for the average degeneracies and specificities of antibodies and epitopes using a chemico-spatial model for epitope diversity and a binary model for antibody-antigen binding. The results illustrate and quantify how the humoral immune system achieves both high specificity and high degeneracy simultaneously by effectively decoupling the two properties, similarly to programs in cryptography called secure hash algorithms (SHAs), which display the same paradoxical features. In addition, an antibody-epitope interaction probability model is used to help show how newly formed antibodies may avoid cross-reactivity with self-antigens despite their high degree of multispecificity and how the requirement of polyclonal binding likely improves the overall specificity of the immune response. Because they describe the relationships between various statistical parameters in humoral immunity, the models developed here may also have predictive utility.

## Full-text entities

- **Diseases:** viral infection (MESH:D014777), AEIP (MESH:C563663), autoimmune diseases (MESH:D001327), OpS (MESH:D010149)
- **Chemicals:** lipids (MESH:D008055), digoxin (MESH:D004077), H (MESH:D006859), OpS (-), S (MESH:D013455), amino acid (MESH:D000596), Pc (MESH:C053518), carbohydrates (MESH:D002241), water (MESH:D014867), Sc (MESH:D012538), acids (MESH:D000143), sugars (MESH:D000073893), P (MESH:D010758), O (MESH:D010100), N (MESH:D009584), C (MESH:D002244)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12627013/full.md

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Source: https://tomesphere.com/paper/PMC12627013