ArticlePLoS computational biology2020
Discriminating between negative cooperativity and ligand binding to independent sites using pre-equilibrium properties of binding curves.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Molecular Tweezers Block the Functional Pore of a Protein Machine.Journal of the American Chemical Society · 2025Article
- Quantification and potential functional relevance of binding cooperativity of adjacent transcription factors on DNA.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Probing Solid-Binding Peptide Self-Assembly Kinetics Using a Frequency Response Cooperativity Model.Biomimetics (Basel, Switzerland) · 2025Article
- Unexpected Single-Ligand Occupancy and Negative Cooperativity in the SARS-CoV-2 Main Protease.Journal of chemical information and modeling · 2024Article
- Enhanced hexamerization of insulin via assembly pathway rerouting revealed by single particle studies.Communications biology · 2023Article
- The archaeal glutamate transporter homologue GltPh shows heterogeneous substrate binding.The Journal of general physiology · 2022Article
- The Use of the Statistical Entropy in Some New Approaches for the Description of Biosystems.Entropy (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Negative cooperativity is a phenomenon in which the binding of a first ligand or substrate molecule decreases the rate of subsequent binding. This definition is not exclusive to ligand-receptor binding, it holds whenever two or more molecules undergo two successive binding events. Negative cooperativity turns the binding curve more graded and cannot be distinguished from two independent and different binding events based on equilibrium measurements only. The need of kinetic data for this purpose was already reported. Here, we study the binding response as a function of the amount of ligand, at different times, from very early times since ligand is added and until equilibrium is reached. Over those binding curves measured at different times, we compute the dynamic range: the fold change required in input to elicit a change from 10 to 90% of maximum output, finding that it evolves in time differently and controlled by different parameters in the two situations that are identical in equilibrium. Deciphering which is the microscopic model that leads to a given binding curve adds understanding on the molecular mechanisms at play, and thus, is a valuable tool. The methods developed in this article were tested both with simulated and experimental data, showing to be robust to noise and experimental constraints.
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