Leveraging Zernike polynomials for peptide conformer identification

Diego Amaya-Ramirez (1) , Bernard Maigret (1) , Etienne Corman (2) , Jean-Luc Taupin (3) , Marie-Dominique Devignes (1)

1 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 PIXEL - Structurer des formes géométriques
3 AP-HP - Hopital Saint-Louis [AP-HP]

Peptides play a key role in multiple biological processes, e.g. peptide hormones or peptide-mediated immune response in signaling pathways . For this reason, peptides have gained relevance in recent years as pharmacological agents. However, peptides are highly flexible leading to diverse three-dimensional (3D) conformers that can modify their specificity and efficacy in binding to their targets. Identifying and understanding such 3D-conformers is therefore fundamental. This can be achieved by analysis of molecular dynamics (MD) runs. However, classical methods such as RMSD maps are difficult to process automatically and manual interpretation can be subjective. In this vein, the main goal of the present work is to implement an automatic strategy for identifying 3D conformers of the [Pyr1]apelin-13 peptide in a MD run using Zernike polynomials.

Mots clés: conformer identification; Molecular dynamic simulations; Zernike polynomials

Domaines: Bio-informatique [q-bio.QM]

Diego Amaya-Ramirez, Bernard Maigret, Etienne Corman, Jean-Luc Taupin, Marie-Dominique Devignes. Leveraging Zernike polynomials for peptide conformer identification. GGMM SFCi, Sep 2021, Villeneuve d'Ascq, France. . ⟨hal-04578212⟩