Ligand binding to proteins: The binding landscape model
Open accessAbstract
Models of ligand binding are often based on four assumptions: (1) steric fit: that binding is determined mainly by shape complementarity; (2) native binding: that ligands mainly bind to native states; (3) locality: that ligands perturb protein structures mainly at the binding site; and (4) continuity: that small changes in ligand or protein structure lead to small changes in binding affinity. Using a generalization of the 2D HP lattice model, we study ligand binding and explore these assumptions. We first validate the model by showing that it reproduces typical binding behaviors. We observe ligand‐induced denaturation, ANS and heme‐like binding, and “lock‐and‐key” and “induced‐fit” specific binding behaviors characterized by Michaelis‐Menten or more cooperative types of binding isotherms. We then explore cases where the model predicts violations of the standard assumptions. For example, very different binding modes can result from two ligands of identical shape. Ligands can sometimes bind highly denatured states more tightly than native states and yet have Michaelis‐Menten isotherms. Even low‐population binding to denatured states can cause changes in global stability, hydrogen‐exchange rates, and thermal B‐factors, contrary to expectations, but in agreement with experiments. We conclude that ligand binding, similar to protein folding, may be better described in terms of energy landscapes than in terms of simpler mass‐action models.
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In solution, protein conformational fluctuations cause even stable proteins to form ensembles of highly populated substates with thermodynamic population distributions.
“Conformational fluctuations of proteins in solution,11 as revealed mostly by nuclear magnetic resonance (NMR) experiments and computations, make even stable proteins exist as conformational ensembles with highly populated substates whose populations follow thermodynamic distributions.12–16”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [13]
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- Authors
- Miller, D. W., & Dill, K. A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Dill, Ken A.
- ORCID
- Dill, Ken A.
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 1997-10
- Volume
- 6
- Issue
- 10
- Pages
- 2166-2179
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC2143563
- OpenAlex
- W2166523933
- Cited by
- 178
- References
- 53
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 7 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 7 · 2019: 7 · 2018: 5 · 2017: 8 · 2016: 8 · 2015: 8 · 2014: 1 · 2013: 8 · 2012: 7
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Protein Science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (9255688)
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- ligand binding, HP lattice model, protein denaturation, induced fit, cooperative binding, hydrogen exchange, energy landscape, binding affinity, protein folding
- MeSH terms
- Hemeproteins, Hydrogen, Ligands, Mathematics, Models, Biological, Probability, Protein Binding, Protein Denaturation, Thermodynamics, Protein Folding
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