Ribosomal translation of fluorinated non-canonical amino acids for de novo biologically active fluorinated macrocyclic peptides.

Fluorination has emerged as a promising strategy in medicinal chemistry to improve the pharmacological profiles of drug candidates. Similarly, incorporating fluorinated non-canonical amino acids into macrocyclic peptides expands chemical diversity and enhances their pharmacological properties, from improved metabolic stability to enhanced cell permeability and target interactions. However, only a limited number of fluorinated non-canonical amino acids, which are canonical amino acid analogs, have been incorporated into macrocyclic peptides by ribosomes for de novo construction and target-based screening of fluorinated macrocyclic peptides. In this study, we report the ribosomal translation of a series of distinct fluorinated non-canonical amino acids, including mono-to tri-fluorinated variants, as well as fluorinated l-amino acids, d-amino acids, ß-amino acids, etc. This enabled the de novo discovery of fluorinated macrocyclic peptides with high affinity for EphA2, and particularly the identification of those exhibiting broad-spectrum activity against Gram-negative bacteria by targeting the BAM complex. This study not only expands the scope of ribosomally translatable fluorinated amino acids but also underscores the versatility of fluorinated macrocyclic peptides as potent therapeutic agents.

Ribosomal Incorporation of Lithocholic Acid into Peptides for the De Novo Discovery Of Peptide-Lithocholic Acid Hybrid Macrocyclic Peptides.

Peptide-bile acid hybrids offer promising drug candidates due to enhanced pharmacological properties, such as improved protease resistance and oral bioavailability. However, it remains unknown whether bile acids can be incorporated into peptide chains by the ribosome to produce a peptide-bile acid hybrid macrocyclic peptide library for target-based de novo screening. In this study, we achieved the ribosomal incorporation of lithocholic acid (LCA)-d-tyrosine into peptide chains. This led to the construction of a peptide-LCA hybrid macrocyclic peptide library, which enabled the identification of peptides TP-2C-4L3 (targeting Trop2) and EP-2C-4L5 (targeting EphA2) with strong binding affinities. Notably, LCA was found to directly participate in binding to EphA2 and confer on the peptides improved stability and resistance to proteases. Cell staining experiments confirmed the high specificity of the peptides for targeting Trop2 and EphA2. This study highlights the benefits of LCA in peptides and paves the way for de novo discovery of stable peptide-LCA hybrid drugs.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

A latent profile analysis of resilience and their relation to differences in sleep quality in patients with lung cancer.

PURPOSE: Sleep problems are a significant issue in patients with lung cancer, and resilience is a closely related factor. However, few studies have identified subgroups of resilience and their relationship with sleep quality. This study aimed to investigate whether there are different profiles of resilience in patients with lung cancer, to determine the sociodemographic characteristics of each subgroup, and to determine the relationship between resilience and sleep quality in different subgroups. METHODS: A total of 303 patients with lung cancer from four tertiary hospitals in China completed the General Sociodemographic sheet, the Connor-Davidson Resilience Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis was applied to explore the latent profiles of resilience. Multivariate logistic regression was used to analyze the sociodemographic variables in each profile, and ANOVA was used to explore the relationships between resilience profiles and sleep quality. RESULTS: The following three latent profiles were identified: the "high-resilience group" (30.2%), the "moderate-resilience group" (46.0%), and the "low-resilience group" (23.8%). Gender, place of residence, and average monthly household income significantly influenced the distribution of resilience in patients with lung cancer. CONCLUSION: The resilience patterns of patients with lung cancer varied. It is suggested that health care providers screen out various types of patients with multiple levels of resilience and pay more attention to female, rural, and poor patients. Additionally, individual differences in resilience may provide an actionable means for addressing sleep problems.

Social Support and Life Satisfaction in Women With Cervical Cancer: A Serial Multiple Mediation Model.

BACKGROUND: Although the number of studies focusing on life satisfaction in women with cervical cancer is increasing, there are limited studies on the pathway between social support and life satisfaction in this population. OBJECTIVE: This study explored the pathway between social support and life satisfaction in women with cervical cancer by examining the serial mediating effects of self-care self-efficacy, coping strategies, and depressive symptoms. METHODS: In this cross-sectional study, a total of 292 women with cervical cancer completed a questionnaire for assessing social support, self-efficacy, coping strategies, depressive symptoms, and life satisfaction. Structural equation modeling was used to test the direct and/or indirect effects of the variables on life satisfaction. RESULTS: Structural equation modeling analysis indicated that self-efficacy, coping strategies, and depressive symptoms mediates the effect of social support on life satisfaction. Direct paths from social support to life satisfaction, social support to self-efficacy, self-efficacy to coping strategies, coping strategies to depressive symptoms, and depressive symptoms to life satisfaction were significant ( P < .05). Moreover, indirect paths from social support to life satisfaction, self-efficacy to life satisfaction, and coping strategies to life satisfaction were also significant ( P < .05). CONCLUSIONS: Self-care self-efficacy, coping strategies, and depressive symptoms are potential pathways through which social support may affect life satisfaction in women with cervical cancer. IMPLICATION: Healthcare providers, family, and friends should offer more social support to the patients and make efforts to strengthen their self-care self-efficacy, facilitate active coping, and alleviate depressive symptoms to improve women's life satisfaction.