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PPI Helix Turn 3D-Mimetics Library

PPI Helix Turn 3D-Mimetics Library

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Description
PPIs are the most screened target class in high-throughput screening (HTS) now.
However, success rate of finding hit compounds in many HTS campaigns using small molecule compounds remains generally very low . This suggests that most of the available chemical libraries are not suitable for screening PPI targets, and special design for PPI-focused libraries is required to achieve suitable chemical space. Indeed, discovered to date PPI inhibitors are remarkably different if compared with the majority of known drugs: usually they are larger, more hydrophobic, contain more (hetero)aromatic rings, and therefore belong to “beyond Ro5” chemical space. Not coincidentally, compounds with diverse and well-developed 3D-shapes have gained most attractiveness ones on the market of screening compounds for HTS over last several years. Furthermore, Fsp3 parameter has become one of the most important criterion of HTS libraries value since it was introduced in 2009 by Frank Lovering et.al as a measure of three-dimensionality and therefore complexity for libraries members. According to their findings and some our further observations, scaffold/molecule saturation may benefit:
  • Better diversity;
  • Complexity;
  • Access to greater chemical space;
  • Improved phys-chem parameters (logP; PSA; water solubility etc.);
  • Better opportunity to reduce scaffold MW;
  • Better opportunity for further scaffold modification;
  • Natural product-likeness;
  • Better affinity to target proteins
  • Greater selectivity;
  • Easy access to IP-clean field.

This inspired us to create new subset of our PPI-focused library, namely Helix/Turn 3D-mimetics Library. The library has been designed on selected sp3-enriched scaffolds from our DOS chemistry and populated with members that are able to mimic α-helices and β-turns as key recognition elements of protein secondary structure.
The following requirements were used for preferable scaffolds selection:
  • sp3 – enriched (Fsp3 ≥ 0.4) to ensure their complexity and therefore 3D-diversity;
  • Contain at least 2, preferably more points of diversification with an opportunity to introduce structural elements of recognition such as side chains of proteinogenic amino acids, preferably with high helix propensity (e.g. iso-propyl-, iso-butyl-, benzyl-, carboxyl-, aminoalkyl-, hydroxyalkyl- carboxamide groups, imidazole, indole rings etc.) on each of them;
  • Contain moieties of “privileged structures” such as piperazines, piperidines (including 3- or 4-amines, carboxamides etc.), pyrrolidines (including 3- amines, carboxamides etc.), (benz)-1.4-diazepines, prolines (including unusual) and others;
  • Contain moieties of naturally occurring compounds;
  • Lipophilitciy / hydrophilicity balanced;
  • Conformationally constrained (e.g. spiro- and bridged heterocyclic systems).
Publications
1. Hamon V et al. 2014 2P2IHUNTER: a tool for filtering orthosteric protein–protein interaction modulators via a
dedicated support vector machine. J. R. Soc. Interface 11: 20130860. http://dx.doi.org/10.1098/rsif.2013.0860
2. Barker, A., Kettle, J.G., Nowak, T., Pease, J.E. 2013 Expanding medicinal chemistry space. Drug Discov. Today 18, 298–304. (doi:10.1016/j.drudis. 2012.10.008)
3. Fry D et al. 2013 Design of libraries targeting protein–protein interfaces. ChemMedChem 8, 726–732 (doi:10.1002/cmdc.201200540).
4. Morelli, X., Bourgeas, R.; Roche, P. Curr. Opin. Chem. Biol., 2011, 15, 475–481.
5. Bourgeas R, Basse M-J, Morelli X, Roche P. 2010 Atomic analysis of protein–protein interfaces with known inhibitors: the 2P2I database. PLoS ONE 5, e9598. (doi:10.1371/journal.pone.0009598) 24.
6. Hamon, V., Brunel, J. M., Combes, S., Basse, M. J., Roche, P., Morelli X. 2P2Ichem: focused chemical libraries dedicated to orthosteric modulation of protein–protein interactions. Med. Chem. Commun., 2013, 4, 797-809 (DOI: 10.1039/C3MD00018D)
7. Laraia, L.; Spring, D. R. Chemical library screening approaches to aid the design of protein–protein inhibitors, Pages 32-45 (doi: 10.4155/ebo.13.151) In: Understanding and Exploiting Protein–Protein Interactions as Drug Targets 151 pages October, 2013, Future Science Ltd (doi: 10.4155/9781909453463)
8. Zhang, , X, Betzi, S., Morelli, X., Roche, P. Focused chemical libraries – design and enrichment: an example of protein–protein interaction chemical space. Future Med. Chem. (2014) 6(11), 1291–1307. (doi:10.4155/fmc.14.57).
9. Milroy, L.-G. et al. Modulators of Protein−Protein Interactions. Chem. Rev. 2014, 114, 4695−4748 (dx.doi.org/10.1021/cr400698c)
10. Rizzo, S.; Waldmann, H. Development of a Natural-Product-Derived Chemical Toolbox for Modulation of Protein Function. Chem. Rev. 2014, 114, 4621-4639 (DOI: 10.1021/cr400442v).
11. Hajduk, P.J., Galloway, W.R.J.D., Spring, D.R. Drug discovery: A question of library design. Nature 470, 42–43 (2011).
12. Hung, A.W. et al. Route to three-dimensional fragments using diversity-oriented synthesis. Proc. Natl. Acad. Sci. USA. 108, 6799-6804 (2011).
13. Lovering, F., Bikker, J., Humblet, C. Escape from flatland: Increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756 (2009).
14. Bourne, G.T.; Kuster, D.J.; Marshall, G.R. Synthesis of the Phenylpyridal Scaffold as a Helical Peptide Mimetic. Eur. J. Chem, 2010, 16(28), 8439-8445.
15. Witby, L.R. ; Boger, D.L. Comprehensive Peptidomimetic Libraries Targeting Protein_Protein Interactions. Acc. Chem. Res. 2012, 45, 1698 – 1709.
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