An NRP1/MDM2‐Targeted D‐Peptide Supramolecular Nanomedicine for High‐Efficacy and Low‐Toxic Liver Cancer Therapy

Abstract

Supramolecular nanomedicines based on self‐assembly of D‐peptides have been of great interest as potential candidates for cancer therapy. Neuropilin‐1 (NRP1) and mouse double minute 2 (MDM2) have been considered as the anticancer targets because of their overexpression in cancers. However, NRP1/MDM2‐targeted D‐peptide supramolecular nanomedicines remain unreported. Here, a potent anticancer D‐peptide supramolecular nanomedicine targeting NRP1 and MDM2, termed as NMTP‐5, is identified by using structure‐based virtual screening techniques. NMTP‐5 exhibits good biostability and strong cellular uptake performance. Moreover, NMTP‐5 displays strong anticancer activity to SK‐Hep‐1 cells in vitro and in vivo, with no apparent host toxicity. This work demonstrates that NMTP‐5 can be used as a potential chemotherapeutic agent for the treatment of liver cancer.

Discovery of Novel and Highly Potent Inhibitors of SARS CoV-2 Papain-Like Protease Through Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, Molecular Dynamics Simulations, and Biological Evaluation

“…The structure-based pharmacophore model has become an important tool in drug discovery ( Yang et al, 2020 ; Zhou et al, 2021 ). Previous studies have shown that MOE can be applied to obtain the pharmacophore features on the ligand binding to the target protein by analyzing the protein-ligand interaction of its crystal structure ( Zhou et al, 2021 ).…”

“…Structure-based virtual screening that combines structure-based pharmacophore modeling and molecular docking is one of the methods used in CADD and it enables screening of many compounds in a relatively short time compared to the high throughput screening via laboratory experiments ( McInnes, 2007 ; Yang et al, 2020 ). The previous studies have successfully identified kinds of novel and effective inhibitors by using such a virtual screening strategy ( Yang et al, 2020 ; Zhou et al, 2021 ).…”

A GSH/CB Dual-Controlled Self-Assembled Nanomedicine for High-Efficacy Doxorubicin-Resistant Breast Cancer Therapy

“…To satisfy the design criteria illustrated in Figure 1, we designed the supramolecular gelator precursor cyclic-1a, the C 10 H 7 CH 2 C(O)CFFYKCGGRRGKGGHHHRRGDS sequence (disulfide bond cyclization). The cyclic-1a contains the following four distinct segments: 1) the naphthyl group (C 10 H 7 CH 2 -) for providing the hydrophobic force to improve the ability of self-assembly in aqueous environment (Zhou et al, 2021); 2) the tetrapeptide (FFYK) segment (being made of D-amino acids) being the major building block to act as both a donor and an acceptor of hydrogen bonds (Zhou et al, 2021); 3) the disulfided cysteine (Cys) motif as a trigger of GSH reduction, which was responsible for the first order nanofiber self-assembly; and 4) the peptide sequence (GGRRGKGGHHHRRGDS), whose cleavage by CB confers on the molecule the ability of the second order self-assembly.…”

“…Linear peptides have been designed to undergo self-assembly in response to enzymatic catalysis in biological microenvironments (Rudra et al, 2010;Rudra et al, 2012). As a useful strategy for generating supramolecular nanofibers/hydrogels, linear peptide-based hydrogelations show promising applications in cell fate control, drug delivery, immune modulation, biosensing, and regenerative medicine (Habibi et al, 2016;Acar et al, 2017;Zhou et al, 2021). Despite many advantages of linear peptides as the precursors for enzyme-triggered hydrogelation, linear peptides are easily degraded by extracellular serum proteases before entering the cytoplasm of cells (Lian et al, 2014;Yang et al, 2020), which greatly restricts their clinical application.…”

finDr: A web server for in silico D-peptide ligand identification

“…An alternative way to identify L- and D-peptide ligands, which does not suffer from the abovementioned restrictions, is computational modelling of PPIs [ 22 ]. Recent publications have shown the feasibility of identifying biologically active D-peptide ligands by modelling the structure of short helical D-peptide segments with molecular dynamics (MD) simulations and by screening peptide libraries generated this way for ligand-target interactions via molecular docking [ 23 , 24 ]. These programs enable the high-throughput in silico screening of peptide libraries; however, with increasing peptide length, the size of peptide libraries and, consequently, the search space and computational cost for docking-based screenings increase exponentially to a point where they are simply too large to be systematically explored.…”