Pattern enrichment analysis for phage selection of stapled peptide ligands† † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2sc04058a

Phage display is the most widely used technique to discover de novo peptides that bind to target proteins. However, it is associated with some challenges such as compositional bias. In this study, to overcome these difficulties, we devised a ‘pattern enrichment analysis.' In this method, two samples (one obtained by affinity selection, the other simply amplified without selection) are prepared, and the two sequence datasets read on next-generation sequencer are compared to find the three-residue pattern most enriched in the selected sample. This allows us to compare two sequence datasets with high coverage and facilitates the identification of peptide sequences and the key residues for binding. We also demonstrated that this approach in the combination with structured peptide libraries allowed spatial mapping of the enriched sequence patterns. Here, we prepared a phage library displaying chemically stapled helical peptides with the X1C2X3X4X5X6X7X8C9X10 sequence, where X is any amino acid. To validate our method, we performed screening against the HDM2 protein. The results showed that the hydrophobic residues (Phe, Tyr, Trp and Leu) that are key to interactions with HDM2 were clearly identified by the pattern enrichment analysis. We also performed selection targeting the SARS-CoV-2 spike RBD in the same manner. The results showed that similar patterns were enriched among the hit peptides that inhibited the protein–protein interaction. To effectively identify helical peptide binders from a phage library, we developed "pattern enrichment analysis,” which finds the enriched three-residue patterns by comparison with a control sample.

Pattern enrichment analysis for phage selection of stapled peptide ligands.

Phage display is the most widely used technique to discover de novo peptides that bind to target proteins. However, it is associated with some challenges such as compositional bias. In this study, to overcome these difficulties, we devised a 'pattern enrichment analysis.' In this method, two samples (one obtained by affinity selection, the other simply amplified without selection) are prepared, and the two sequence datasets read on next-generation sequencer are compared to find the three-residue pattern most enriched in the selected sample. This allows us to compare two sequence datasets with high coverage and facilitates the identification of peptide sequences and the key residues for binding. We also demonstrated that this approach in the combination with structured peptide libraries allowed spatial mapping of the enriched sequence patterns. Here, we prepared a phage library displaying chemically stapled helical peptides with the X1C2X3X4X5X6X7X8C9X10 sequence, where X is any amino acid. To validate our method, we performed screening against the HDM2 protein. The results showed that the hydrophobic residues (Phe, Tyr, Trp and Leu) that are key to interactions with HDM2 were clearly identified by the pattern enrichment analysis. We also performed selection targeting the SARS-CoV-2 spike RBD in the same manner. The results showed that similar patterns were enriched among the hit peptides that inhibited the protein-protein interaction.

Impact of the COVID-19 Pandemic on Glycemic Control and Blood Pressure Control in Patients with Diabetes in Japan.

Objective In this study, we investigated whether and how the COVID-19 pandemic affected glycemic control and blood pressure (BP) control in patients with diabetes mellitus (DM). Methods DM patients whose HbA1c level was measured regularly before and after the declaration of a state of emergency were included in this study. Some patients were given questionnaires about changes in their lifestyle to determine the factors affecting glycemic control and BP control. Results The median HbA1c level of the 804 patients increased significantly from 6.8% before the state of emergency to 7.1% and 7.0% during and after the state of emergency, respectively. This was in contrast to the decrease one year earlier due to seasonal variations. In the 176 patients who responded to the questionnaire, the HbA1c level also increased significantly during and after the state of emergency. The worsening of glycemic control was more pronounced in the group that had achieved HbA1c of <7% before the state of emergency than in those with higher values. Unlike the rise in HbA1c, the BP did not rise during the state of emergency but did rise significantly afterwards. There was no marked decrease in HbA1c or BP after the state of emergency, even in patients who responded that they were much more careful with their diet, ate less, or exercised more. Conclusions The COVID-19 pandemic worsened glycemic control and BP control, even in patients who perceived no marked change in their diet or exercise, suggesting that more active lifestyle guidance is necessary for good treatment of DM patients.