Which three-dimensional characteristics make efficient inhibitors of protein–protein interactions?
Abstract
The specific properties of protein-protein interactions (PPI) (flat, large and hydrophobic) make them harder to tackle with low-molecular-weight compounds. Learning from the properties of successful examples of PPI interface inhibitors (iPPI) at earlier stages of developments, has been pinpointed as a powerful strategy to circumvent this trend. To this end, we have computationally analyzed the bioactive conformations of iPPI and those of inhibitors of conventional targets (e.g enzymes) to highlight putative iPPI 3D characteristics. Most noticeably, the essential property revealed by this study illustrates how efficiently iPPI manages to bind to the hydrophobic patch often present at the core of protein interfaces. The newly identified properties were further confirmed as characteristics of iPPI using much larger data sets (e.g iPPI-DB, www.ippidb.cdithem.fr ). Interestingly, the absence of correlation of such properties with the hydrophobicity and the size of the compounds opens new ways to design potent iPPI with better pharmacokinetic features.
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HADDOCKPPI is a linear scoring function specifically developed to predict binding affinities of inhibitors of protein-protein interactions (iPPIs).
“The HADDOCKPPI is a linear scoring function specifically developed to predict binding affinities of inhibitors of protein–protein interactions (iPPIs)”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Kuenemann et al., 2014
Inhibitors of protein-protein interactions interact in uncommon binding cavities characterized by higher hydrophobicity, aromaticity, and molecular weight compared to enzyme inhibitors, and usually within flatter, larger, and more hydrophobic binding sites than enzyme catalytic sites.
“which interact in uncommon binding cavities characterized by higher hydrophobicity, aromaticity, and molecular weight compared to enzyme inhibitors, as usually interacting within flatter, larger, and more hydrophobic binding sites than the enzyme catalytic sites (Morelli et al., 2011; Kuenemann et al., 2014).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Kuenemann et al., 2014
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- Authors
- Kuenemann, M. A., Bourbon, L. M. L., Labbé, C. M., Villoutreix, B. O., & Sperandio, O.
- Affiliations
- Inserm (FR)
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-10-17
- Volume
- 54
- Issue
- 11
- Pages
- 3067-3079
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2018745394
- Cited by
- 42
- References
- 42
- Citations by year
- 2026: 2 · 2022: 4 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 6 · 2017: 5 · 2016: 7 · 2015: 8
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-09-PIRI-0017)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein-protein interaction inhibitors, protein-protein interfaces, hydrophobic patch, low molecular weight inhibitors, bioactive conformation, pharmacokinetic properties, inhibitor design
- MeSH terms
- Models, Molecular, Protein Binding, Protein Conformation, Proteins, Drug Discovery, Hydrophobic and Hydrophilic Interactions
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