Non-additivity of functional group contributions in protein-ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry.
Abstract
Additivity of functional group contributions to protein-ligand binding is a very popular concept in medicinal chemistry as the basis of rational design and optimized lead structures. Most of the currently applied scoring functions for docking build on such additivity models. Even though the limitation of this concept is well known, case studies examining in detail why additivity fails at the molecular level are still very scarce. The present study shows, by use of crystal structure analysis and isothermal titration calorimetry for a congeneric series of thrombin inhibitors, that extensive cooperative effects between hydrophobic contacts and hydrogen bond formation are intimately coupled via dynamic properties of the formed complexes. The formation of optimal lipophilic contacts with the surface of the thrombin S3 pocket and the full desolvation of this pocket can conflict with the formation of an optimal hydrogen bond between ligand and protein. The mutual contributions of the competing interactions depend on the size of the ligand hydrophobic substituent and influence the residual mobility of ligand portions at the binding site. Analysis of the individual crystal structures and factorizing the free energy into enthalpy and entropy demonstrates that binding affinity of the ligands results from a mixture of enthalpic contributions from hydrogen bonding and hydrophobic contacts, and entropic considerations involving an increasing loss of residual mobility of the bound ligands. This complex picture of mutually competing and partially compensating enthalpic and entropic effects determines the non-additivity of free energy contributions to ligand binding at the molecular level.
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The advantage of nonlinear scoring functions partially circumvents the problematic modeling assumptions of linear models.
“This advantage of nonlinear scoring functions partially circumvents the problematic modeling assumptions of linear models (Dill, 1997; Baum et al., 2010; Sotriffer, 2012).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Baum et al., 2010
Machine learning-based scoring functions using advanced algorithms such as random forest and deep convolutional neural networks can characterize non-additive contributions of functional groups in protein-ligand binding interactions.
“By using advanced machine learning algorithms, such as random forest (RF) and deep convolutional neural network, the machine learning-based SFs can characterize the non-additive contributions of functional groups in protein–ligand binding interactions [18].”
From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [18]
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- Authors
- Baum, B., Muley, L., Smolinski, M., Heine, A., Hangauer, D., & Klebe, G.
- Affiliations
- Philipps University of Marburg (DE)
- University at Buffalo, State University of New York (US)
- Corresponding author
- Muley, Laveena
- Smolinski, Michael
- Hangauer, David
- Klebe, Gerhard
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 397
- Issue
- 4
- Pages
- 1042-1054
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2025557696
- Cited by
- 157
- References
- 34
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 7 · 2022: 11 · 2021: 8 · 2020: 9 · 2019: 8 · 2018: 14 · 2017: 8 · 2016: 6 · 2015: 15 · 2014: 11 · 2013: 10 · 2012: 12
- FWCI
- 9.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- HZB Repository (Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (HZB)) (submittedVersion)
- Funders
- CSL Behring
- Bundesministerium für Bildung und Forschung
- Deutsches Elektronen-Synchrotron
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-ligand binding, non-additive effects, functional group contributions, thrombin inhibitors, hydrophobic interactions, hydrogen bonding, cooperativity, enthalpy-entropy compensation, binding free energy, ligand flexibility, congeneric series, docking scoring functions
- MeSH terms
- Anticoagulants, Calorimetry, Models, Chemical, Models, Molecular, Protein Binding, Thrombin, Crystallography, X-Ray, Entropy
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