Evaluation of Predicted Protein–Protein Complexes by Binding Free Energy Simulations
Abstract
The accurate prediction of protein-protein complex geometries is of major importance to ultimately model the complete interactome of interacting proteins in a cell. A major bottleneck is the realistic free energy evaluation of predicted docked structures. Typically, simple scoring functions applied to single-complex structures are employed that neglect conformational entropy and often solvent effects completely. The binding free energy of a predicted protein-protein complex can, however, be calculated using umbrella sampling (US) along a predefined dissociation/association coordinate of a complex. We employed atomistic US-molecular dynamics simulations including appropriate conformational and axial restraints and an implicit generalized Born solvent model to calculate binding free energies of a large set of docked decoys for 20 different complexes. Free energies associated with the restraints were calculated separately. In principle, the approach includes all energetic and entropic contributions to the binding process. The evaluation of docked complexes based on binding free energy calculation was in better agreement with experiment compared to a simple scoring based on energy minimization or MD refinement using exactly the same force field description. Even calculated absolute binding free energies of structures close to the native binding geometry showed a reasonable correlation to experiment. However, still for a number of complexes docked decoys of lower free energy than near-native geometries were found indicating inaccuracies in the force field or the implicit solvent model. Although time consuming the approach may open up a new route for realistic ranking of predicted geometries based on calculated free energy of binding.
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When applied to protein assemblies, MM-PBSA and MM-GBSA frequently produce binding free estimates with large statistical errors and make approximations, such as neglecting or partially accounting for the effects of changes in internal energy and entropy on protein association free energies, resulting in limited accuracy.
“However, when applied to protein assemblies, these methods frequently produce binding free estimates with large statistical errors53 and make a number of approximations, such as neglecting or partially accounting for the effects of changes in internal energy and entropy on protein association free energies, resulting in limited accuracy.54”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [53]
While the path-based PMF approach appears more tractable, it is still difficult to obtain converged free energy estimates for large biomolecules with complex geometries.
“While the path-based PMF approach appears more tractable, it is still difficult to obtain converged free energy estimates for large biomolecules with complex geometries.53,62−68”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [53]
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HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
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Standard Binding Free Energies from Computer Simulations: What Is the Best Strategy?
Gumbart, J. C., Roux, B., & Chipot, C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct3008099 · Cited by 343 · see citing works
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Computational prediction of protein–protein binding affinities
Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184 · see citing works
“143 The calculated binding free energies for the near-native complex geometries were in reasonable agreement with experiment.”
BackgroundResultSelf-citationVakser, I. A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.07.001 · Cited by 62 · see citing works
Hazardous Shortcuts in Standard Binding Free Energy Calculations
Blazhynska, M., Goulard Coderc de Lacam, E., Chen, H., Roux, B., & Chipot, C. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01490 · Cited by 27 · see citing works
A Rigorous Framework for Calculating Protein–Protein Binding Affinities in Membranes
Blazhynska, M., Gumbart, J. C., Chen, H., Tajkhorshid, E., Roux, B., & Chipot, C. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00941 · Cited by 22 · see citing works
Notari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4 · see citing works
- Authors
- Siebenmorgen, T., & Zacharias, M.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Zacharias, Martin
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-01-30
- Volume
- 15
- Issue
- 3
- Pages
- 2071-2086
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2913931455
- Cited by
- 59
- References
- 3
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 8 · 2023: 11 · 2022: 8 · 2021: 11 · 2020: 7 · 2019: 6
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 863/A10)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein complex, binding free energy, umbrella sampling, molecular dynamics, conformational entropy, solvent effects, force field accuracy, protein-protein interaction, docking scoring functions
- MeSH terms
- Animals, Humans, Protein Binding, Protein Conformation, Proteins, Thermodynamics, Protein Interaction Mapping, Databases, Protein, Molecular Dynamics Simulation, Protein Interaction Maps, Molecular Docking Simulation
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