Relating Molecular Dynamics Simulations to Functional Activity for Gly-Rich Membranolytic Helical Kiadin Peptides

Abstract

Kiadins are in silico designed peptides with a strong similarity to diPGLa-H, a tandem sequence of PGLa-H (KIAKVALKAL) and with single, double or quadruple glycine substitutions. They were found to show high variability in their activity and selectivity against Gram-negative and Gram-positive bacteria, as well as cytotoxicity against host cells, which are influenced by the number and placing of glycine residues along the sequence. The conformational flexibility introduced by these substitutions contributes differently peptide structuring and to their interactions with the model membranes, as observed by molecular dynamics simulations. We relate these results to experimentally determined data on the structure of kiadins and their interactions with liposomes having a phospholipid membrane composition similar to simulation membrane models, as well as to their antibacterial and cytotoxic activities, and also discuss the challenges in interpreting these multiscale experiments and understanding why the presence of glycine residues in the sequence affected the antibacterial potency and toxicity towards host cells in a different manner.

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

“…To verify the reliability of the model, we downloaded the latest discovered anticancer peptide sequences from the Frontiers in Genetics frontiersin.org DBAASP database (Pirtskhalava et al, 2021), Through biological experiments, it has been demonstrated that this sequence possesses functions such as anti-Gram+, anti-Gram-, and anticancer (Rončević et al, 2023). Moreover, this sequence is not included in our training dataset.…”