Test of a Statistical Model for Molecular Recognition in Biological Repertoires
Abstract
A chance encounter between members of a random repertoire and a molecular target is characteristic of different biological systems, including the immune and olfactory pathways as well as combinatorial libraries. In such systems, the affinity between the target and members of the repertoire is distributed with a probability function describing the propensity of obtaining a particular affinity value. We have previously proposed a phenomenological receptor affinity distribution (RAD) formalism, which describes this probability function based on simple statistical considerations. In the present analysis, we use published data from diverse experimental systems, including phage display libraries, immunoglobulins and enzymes, to test the RAD model and to compare it to other affinity distribution formalisms. The RAD model is found to provide the best description for binding data for over eight orders of magnitude on the affinity scale, and to account for a relationship between repertoire size and the maximal obtainable affinity within different repertoires. This approach points to a potential universality of the rules that govern affinity distributions in biology.
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The inferred receptor affinity distribution was verified by meta-analysis of published data from diverse experimental systems, including phage display libraries, hapten-immunoglobulin interactions and enzyme-substrate recognition.
“For enhanced rigour, the inferred distribution was verified by meta-analysis of published data from diverse experimental systems, including phage display libraries, hapten–immunoglobulin interactions and enzyme–substrate recognition [81].”
From Systems protobiology: origin of life in lipid catalytic networks (2018) · cites this paper as [81]
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- Authors
- ROSENWALD, S., KAFRI, R., & LANCET, D.
- Affiliations
- Crown Human Genome Center (IL)
- Corresponding author
- LANCET, DORON
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2002-06
- Volume
- 216
- Issue
- 3
- Pages
- 327-336
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W2061222114
- Cited by
- 37
- References
- 77
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 4 · 2019: 1 · 2018: 1 · 2013: 1 · 2012: 4
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- molecular recognition, phage display library, immunoglobulins, enzyme affinity, combinatorial libraries, immune repertoire, olfactory receptors
- MeSH terms
- Animals, Antibody Affinity, Ligands, Models, Genetic, Probability, Protein Binding, Receptors, Cell Surface, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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