Evaluating Alkaline Phosphatase-Instructed Self-Assembly of d‑Peptides for Selectively Inhibiting Ovarian Cancer Cells

Cancer is a major public health concern requiring novel treatment approaches. Enzyme-instructed self-assembly (EISA) provides a unique approach for selectively inhibiting cancer cells. However, the structure and activity correlation of EISA remains to be explored. This study investigates new EISA substrates of alkaline phosphatase (ALP) to hinder ovarian cancer cells. Analogues 2–8 were synthesized by modifying the amino acid residues of a potent EISA substrate 1 that effectively inhibits the growth of OVSAHO, a high-grade serous ovarian cancer (HGSOC) cell line. The efficacy of 2–8 against OVSAHO was assessed, along with the combination of substrate 1 with clinically used drugs. The results reveal that substrate 1 displays the highest cytotoxicity against OVSAHO cells, with an IC50 of around 8 μM. However, there was limited synergism observed between substrate 1 and the tested clinically used drugs. These findings indicate that EISA likely operates through a distinct mechanism that necessitates further elucidation

Evaluating Alkaline Phosphatase-Instructed Self-Assembly of d‑Peptides for Selectively Inhibiting Ovarian Cancer Cells

Cancer is a major public health concern requiring novel treatment approaches. Enzyme-instructed self-assembly (EISA) provides a unique approach for selectively inhibiting cancer cells. However, the structure and activity correlation of EISA remains to be explored. This study investigates new EISA substrates of alkaline phosphatase (ALP) to hinder ovarian cancer cells. Analogues 2–8 were synthesized by modifying the amino acid residues of a potent EISA substrate 1 that effectively inhibits the growth of OVSAHO, a high-grade serous ovarian cancer (HGSOC) cell line. The efficacy of 2–8 against OVSAHO was assessed, along with the combination of substrate 1 with clinically used drugs. The results reveal that substrate 1 displays the highest cytotoxicity against OVSAHO cells, with an IC50 of around 8 μM. However, there was limited synergism observed between substrate 1 and the tested clinically used drugs. These findings indicate that EISA likely operates through a distinct mechanism that necessitates further elucidation

Algorithm for Designing Nanoscale Supramolecular Therapeutics with Increased Anticancer Efficacy

In the chemical world, evolution is mirrored in the origin of nanoscale supramolecular structures from molecular subunits. The complexity of function acquired in a supramolecular system over a molecular subunit can be harnessed in the treatment of cancer. However, the design of supramolecular nanostructures is hindered by a limited atomistic level understanding of interactions between building blocks. Here, we report the development of a computational algorithm, which we term Volvox after the first multicellular organism, that sequentially integrates quantum mechanical energy-state- and force-field-based models with large-scale all-atomistic explicit water molecular dynamics simulations to design stable nanoscale lipidic supramolecular structures. In one example, we demonstrate that Volvox enables the design of a nanoscale taxane supramolecular therapeutic. In another example, we demonstrate that Volvox can be extended to optimizing the ratio of excipients to form a stable nanoscale supramolecular therapeutic. The nanoscale taxane supramolecular therapeutic exerts greater antitumor efficacy than a clinically used taxane <i>in vivo</i>. Volvox can emerge as a powerful tool in the design of nanoscale supramolecular therapeutics for effective treatment of cancer

Algorithm for Designing Nanoscale Supramolecular Therapeutics with Increased Anticancer Efficacy

In the chemical world, evolution is mirrored in the origin of nanoscale supramolecular structures from molecular subunits. The complexity of function acquired in a supramolecular system over a molecular subunit can be harnessed in the treatment of cancer. However, the design of supramolecular nanostructures is hindered by a limited atomistic level understanding of interactions between building blocks. Here, we report the development of a computational algorithm, which we term Volvox after the first multicellular organism, that sequentially integrates quantum mechanical energy-state- and force-field-based models with large-scale all-atomistic explicit water molecular dynamics simulations to design stable nanoscale lipidic supramolecular structures. In one example, we demonstrate that Volvox enables the design of a nanoscale taxane supramolecular therapeutic. In another example, we demonstrate that Volvox can be extended to optimizing the ratio of excipients to form a stable nanoscale supramolecular therapeutic. The nanoscale taxane supramolecular therapeutic exerts greater antitumor efficacy than a clinically used taxane <i>in vivo</i>. Volvox can emerge as a powerful tool in the design of nanoscale supramolecular therapeutics for effective treatment of cancer

Characterization of distinct peptides identified from mouse plasma reference sets

The histogram of peptide length of unique sequences in mouse PeptideAtlas (blue) is overlaid on an tryptic digest of the IPI mouse database (black).<p><b>Copyright information:</b></p><p>Taken from "A mouse plasma peptide atlas as a resource for disease proteomics"</p><p>http://genomebiology.com/2008/9/6/R93</p><p>Genome Biology 2008;9(6):R93-R93.</p><p>Published online 3 Jun 2008</p><p>PMCID:PMC2481425.</p><p></p

Genomic mapping of mouse peptide LLEAQIATGGIIDPK using UCSC Blast program

Mouse splice variant M13C2563_1_s386_e8960_1_rf2_c1_n0 was identified with 13 distinct peptides. Twelve peptides matched a predicted mouse protein similar to desmoplakin using a homolog search against the NCBI NR database. However, all 13 peptides were homologous to the human desmoplakin sequence. Of note, mouse peptide LLEAQIATGGIIDPK aligns to the coding sequence of human desmoplakin, but not to the annotated mouse desmoplakin gene.<p><b>Copyright information:</b></p><p>Taken from "A mouse plasma peptide atlas as a resource for disease proteomics"</p><p>http://genomebiology.com/2008/9/6/R93</p><p>Genome Biology 2008;9(6):R93-R93.</p><p>Published online 3 Jun 2008</p><p>PMCID:PMC2481425.</p><p></p