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Published on: September 7, 2013
CLIPSing Melanotan-II to Discover Multiple Functionally Selective hMCR Agonists.
Stefano Tomassi1, Marilisa Pia Dimmito2, Minying Cai3
1Dipartimento di Farmacia, Università degli Studi di Napoli "Federico II", Via D. Montesano 49, Naples 80131, Italy.
Medicinal chemists developed novel synthetic peptides by modifying melanotan II. Compound 5 showed selective binding to the human melanocortin 1 receptor (hMC1R), offering new therapeutic possibilities.
Area of Science:
- Medicinal Chemistry
- Molecular Pharmacology
- Computational Chemistry
Background:
- Melanocortin receptors (MCRs) are crucial in physiological and pathological processes.
- Developing selective MCR agonists/antagonists is a key goal in medicinal chemistry.
- Melanotan II (MT-II) is a potent but unselective MCR agonist.
Purpose of the Study:
- To design and synthesize novel peptide analogs of MT-II with improved selectivity.
- To investigate the structure-activity relationships of modified peptides with MCRs.
- To explore the conformational effects of different cyclization strategies on peptide affinity.
Main Methods:
- Chemical Linkage of Peptide onto Scaffolds strategy was employed.
- Lactam cyclization in MT-II was replaced with xylene-derived thioethers.
- Binding affinities to human MCRs (hMCRs) were measured.
- Enhanced sampling molecular dynamics simulations were performed.
- A peptide-receptor complex model was built using cryo-EM data.
Main Results:
- Newly designed peptides exhibited binding affinities in the nanomolar to sub-micromolar range.
- A correlation between linker structure and hMCR affinity was observed.
- Compound 5 demonstrated significant functional selectivity for hMC1R over other hMCRs.
- Molecular dynamics simulations elucidated the impact of cyclization on peptide conformation and hMC1R affinity.
Conclusions:
- The novel cyclization strategy successfully generated peptides with modulated affinity and selectivity for hMCRs.
- Compound 5 represents a promising selective hMC1R ligand.
- Computational modeling provides insights into the molecular basis of selective receptor recognition.
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