ROCK-I Derived Bi-functional Peptides with Antibacterial Infection Activity

Efficacy of Antimicrobial Peptide DP7, Designed by Machine-Learning Method, Against Methicillin-Resistant Staphylococcus aureus

Antimicrobial peptides (AMPs) provide a promising strategy against infections involving multidrug-resistant pathogens. In previous studies, we designed a short 12 amino acid AMP DP7, using a machine-learning method based on an amino acid activity contribution matrix. DP7 shows broad-spectrum antimicrobial activities both in vitro and in vivo. Here, we aim to investigate the efficacy of DP7 against multidrug resistant Staphylococcus aureus (S. aureus) and reveal the potential mechanisms. First, by measuring the killing kinetics of DP7 against S. aureus and comparing these results with antibiotics with different antimicrobial mechanisms, we hypothesize that DP7, in addition to its known ability to induce cell wall cation damage, can also exert a full killing effect. With FITC-conjugated or biotin-labeled DP7, we tracked DP7's attachment, membrane permeation and subsequent intracellular distribution in S. aureus. These results indicated that the possible targets of DP7 were within the bacterial cells. Transcriptome sequencing of S. aureus exposed to DP7 identified 333 differentially expressed genes (DEGs) influenced by DP7, involving nucleic acid metabolism, amino acid biosynthesis, cell wall destruction and pathogenesis, respectively, indicating the comprehensive killing efficacy of DP7. In addition, the genome sequencing results of the induced DP7 resistant strain S. aureus DP7-R revealed two-point mutations in the mprF and guaA gene. Moreover, in a murine model for MRSA blood stream infection, intravenously treating mice with DP7 showed a good protective effect on mice. In conclusion, DP7 is an effective bactericide for S. aureus, which deserves further study for clinical application and drug development.

Derivation of Self-inhibitory Helical Peptides to Target Rho-kinase Dimerization in Cerebrovascular Malformation: Structural Bioinformatics Analysis and Peptide Binding Assay

Rho-kinase dimerization is essential for its kinase activity and biological function; disruption of the dimerization has recently been established as a new and promising therapeutics strategy for cerebrovascular malformation (CM). Based on Rho-kinase dimer crystal structure we herein combined in silico analysis and in vitro assay to rationally derive self-inhibitory peptides from the dimerization interface. Three peptides namely Hlp1, Hlp2 and Hlp3 were successfully designed that have potential capability to rebind at the dimerization domain of Rho-kinase. Molecular dynamics (MD) simulations revealed that these peptides are helically structured when bound to Rho-kinase, but exhibit partially intrinsic disorder in unbound state. Binding free energy (BFE) analysis suggested that the peptides have a satisfactory energetic profile to interact with Rho-kinase. The computational findings were then substantiated by fluorescence anisotropy assays, conforming that the helical peptides can bind tightly to Rho-kinase with affinity KD at micromolar level. These designed peptides are considered as lead molecular entities that can be further modified and optimized to obtain more potent peptidomimetics as self-competitors to disrupt Rho-kinase dimerization in CM.