Evidence map›Paper›PMID 36208205›Full record

ArticleMolecular biology and evolution2022

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model.

John M McBride, Jean-Pierre Eckmann, Tsvi Tlusty

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

John M McBrideCenter for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea.ORCID 0000-0002-1496-3807
Jean-Pierre EckmannDépartement de Physique Théorique and Section de Mathématiques, University of Geneva, Geneva, Switzerland.
Tsvi TlustyCenter for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea.ORCID 0000-0002-9662-3725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Indexed as

Models, ChemicalProteinsBinding SitesLigandsProtein BindingLigandsProteinsbinding specificityconformational changesmolecular recognitionprotein evolutiontRNA synthetase

Identifiers

PMID36208205
PMCPMC9641994

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.