A Divalent Metal Cation–Metabolite Interaction Model Reveals Cation Buffering and Speciation
Abstract
I present the perspective that the divalent metalome and the metabolome can be modeled as a network of chelating interactions instead of separate entities. I review progress in understanding the complex cellular environment, in particular recent contributions to modeling metabolite-Mg 2+ interactions. I then demonstrate a simple extension of these strategies based approximately on intracellular Escherichia coli concentrations. This model is composed of four divalent metal cations with a range of cellular concentrations and physical properties (Mg 2+ , Ca 2+ , Mn 2+ , and Zn 2+ ), eight representative metabolites, and interaction constants. I applied this model to predict the speciation of divalent metal cations between free and metabolite-chelated species. This approach reveals potentially beneficial properties, including maintenance of free divalent metal cations at biologically relevant concentrations, buffering of free divalent metal cations, and enrichment of functional metabolite-chelated species. While currently limited by available interaction coefficients, this modeling strategy can be generalized to more complex systems. In summary, biochemists should consider the potential of cellular metabolites to form chelating interactions with divalent metal cations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The bulk ions (K+, Mg2+) have a low affinity for ligands and a large labile pool.
“The bulk ions (K+, Mg2+) have a low affinity for ligands and a large labile pool,5,6”
From Metals in Motion: Understanding Labile Metal Pools in Bacteria (2025) · cites this paper as [6]
At equilibrium, free metal pools are significant for K+ and Mg2+ and are composed of both free and weakly complexed ions.
“At equilibrium, free metal pools are significant for K+ and Mg2+ and are composed of both free and weakly complexed ions.5−7”
From Metals in Motion: Understanding Labile Metal Pools in Bacteria (2025) · cites this paper as [6]
Cites
Metabolite concentrations, fluxes and free energies imply efficient enzyme usage
Park, J. O., Rubin, S. A., Xu, Y.-F., Amador-Noguez, D., Fan, J., Shlomi, T., & Rabinowitz, J. D. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2077 · Cited by 453 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Metals in Motion: Understanding Labile Metal Pools in Bacteria
Helmann, J. D. (2025) · Biochemistry · doi:10.1021/acs.biochem.4c00726 · Cited by 49 · see citing works
- Authors
- Sieg, J. P.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Sieg, Jacob P.
- ORCID
- Sieg, Jacob P.
- Venue
- Biochemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-07-08
- Volume
- 63
- Issue
- 14
- Pages
- 1709-1717
- ISSN
- 0006-2960 (ISSN-L)
- 1520-4995
- OpenAlex
- W4400419518
- Cited by
- 2
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biochemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Heavy Metal Exposure and Toxicity
- Health Sciences › Nursing › Nutrition and Dietetics › Trace Elements in Health
- Health Sciences › Medicine › Hematology › Iron Metabolism and Disorders
- Keywords
- divalent metal cations, chelation, metal speciation, metabolome, Escherichia coli, magnesium, calcium, manganese, zinc, interaction constants
- MeSH terms
- Buffers, Cations, Divalent, Chelating Agents, Escherichia coli, Magnesium, Models, Biological, Zinc, Metabolome
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 ›
Are you an author, or do you see a problem with this page? Request a review