APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides.

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Surface modification potentials of cell membrane-based materials for targeted therapies: a chemotherapy-focused review.

Nanotechnology has significant potential for cancer management at all stages, including prevention, diagnosis and treatment. In therapeutic applications, nanoparticles (NPs) have biological stability, targeting and body-clearance issues. To overcome these difficulties, biomimetic or cell membrane-coating methods using immune cell membranes are advised. Macrophage or neutrophil cell membrane-coated NPs may impede cancer progression in malignant tissue. Immune cell surface proteins and their capacity to maintain activity after membrane extraction and NP coating determine NP functioning. Immune cell surface proteins may offer NPs higher cellular interactions, blood circulation, antigen recognition for targeting, progressive drug release and reduced in vivo toxicity. This article examines nano-based systems with immune cell membranes, their surface modification potential, and their application in cancer treatment.

Nanoparticles (NPs) are small particles that range between 1 and 100 nanometres in size that are used to deliver substances that aid in the prevention, diagnosis and treatment of cancer. NPs are promising for therapeutic use but face challenges like stability, cancer targeting and clearance in the body. This article suggests that these challenges can be overcome using biomimetic methods. This involves coating NPs in cell membranes from immune cells. This has been demonstrated using two types of white blood cells, called macrophages and neutrophils. NPs coated in membranes derived from these cells have been shown to hinder cancer progression. How effective these coated NP cells are depends on what proteins from the surface of the immune cells are included and whether they remain active. These immune cell surface proteins allow coated NPs to have improved interactions with cells, circulate in the blood for longer, target proteins overexpressed on cancer cells and release drugs gradually. Biomimentic cell membrane coating also decreases cell membrane toxicity. The article examines NP-based systems using immune cell membranes, their potential for surface modification and their application in cancer treatment.

The impact of microbiota-derived short-chain fatty acids on macrophage activities in disease: Mechanisms and therapeutic potentials.

Short-chain fatty acids (SCFAs) derived from the fermentation of carbohydrates by gut microbiota play a crucial role in regulating host physiology. Among them, acetate, propionate, and butyrate are key players in various biological processes. Recent research has revealed their significant functions in immune and inflammatory responses. For instance, butyrate reduces the development of interferon-gamma (IFN-γ) generating cells while promoting the development of regulatory T (Treg) cells. Propionate inhibits the initiation of a Th2 immune response by dendritic cells (DCs). Notably, SCFAs have an inhibitory impact on the polarization of M2 macrophages, emphasizing their immunomodulatory properties and potential for therapeutics. In animal models of asthma, both butyrate and propionate suppress the M2 polarization pathway, thus reducing allergic airway inflammation. Moreover, dysbiosis of gut microbiota leading to altered SCFA production has been implicated in prostate cancer progression. SCFAs trigger autophagy in cancer cells and promote M2 polarization in macrophages, accelerating tumor advancement. Manipulating microbiota- producing SCFAs holds promise for cancer treatment. Additionally, SCFAs enhance the expression of hypoxia-inducible factor 1 (HIF-1) by blocking histone deacetylase, resulting in increased production of antibacterial effectors and improved macrophage-mediated elimination of microorganisms. This highlights the antimicrobial potential of SCFAs and their role in host defense mechanisms. This comprehensive review provides an in-depth analysis of the latest research on the functional aspects and underlying mechanisms of SCFAs in relation to macrophage activities in a wide range of diseases, including infectious diseases and cancers. By elucidating the intricate interplay between SCFAs and macrophage functions, this review aims to contribute to the understanding of their therapeutic potential and pave the way for future interventions targeting SCFAs in disease management.

PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction.

The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be fully explored. In this study, an integrated AI framework called PepScaf was developed to extract the critical scaffold relative to bioactivity based on a vast dataset from an initial in vitro selection campaign against a model protein target, interleukin-17C (IL-17C). Taking the generated scaffold, a focused macrocyclic peptide library was rationally constructed to target IL-17C, yielding over 20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction. Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents a viable methodology for more efficient macrocyclic peptide discovery, offering potential time and cost savings. Additionally, this is also the first report regarding the discovery of macrocyclic peptides against IL-17C/IL-17RE interaction.

Construction of predictive model for prognosis of patients after radical resection of colon cancer based on nomogram.

OBJECTIVE: To construct a predictive model for 3-year survival of patients after curative resection of colon cancer by nomogram. METHOD: A retrospective analysis was conducted to analyze the clinicopathologic data of 102 patients who underwent radical resection of colon cancer in Baoji Central Hospital from April 2015 to April 2017. The optimal cutoff values of preoperative CEA, CA125, and NLR for predicting overall survival were analyzed by receiver operating characteristic (ROC) curves. To observe the relationship between NLR, CEA and CA125 and clinicopathologic data, we performed multivariate Cox regression to analyze the independent factors affecting the prognosis of patients, and Kaplan-Meier test to identify the relationship between NLR, CEA and CA125 and patient survival. A nomogram prediction model was drawn for patients' 1-, 2-, and 3-year survival after radical resection of colon cancer, and the efficacy of the prediction model was evaluated. RESULTS: The area under the curve (AUC) of NLR, CEA and CA125 in predicting patient death was 0.784, 0.790 and 0.771, respectively. NLR was correlated with clinical stage, tumor diameter and differentiation (all P < 0.05); CEA was associated with clinical stage, tumor diameter, differentiation and lymph node metastasis (all P < 0.05); CA125 was only associated with tumor diameter in patients (P < 0.05). Differentiation, NLR, CEA and CA125 were independent risk factors affecting the prognosis of patients (all P < 0.05). The nomogram predicted a model C-index of 0.918 (95% CI 0.885-0.952), and the risk model score was found to have a high clinical value in the 3-year survival of preexisting patients. CONCLUSION: Preoperative NLR, CEA, CA125 and clinical stage are correlated with the prognosis of patients with colon cancer. The nomogram model constructed based on NLR, CEA, CA125 and clinical stage has good accuracy.

Epigenetic signature associated with thyroid cancer progression and metastasis.

Thyroid cancer is not among the top cancers in terms of diagnosis or mortality but it still ranks fifth among the cancers diagnosed in women. Infact, women are more likely to be diagnosed with thyroid cancer than the males. The burden of thyroid cancer has dramatically increased in last two decades in China and, in the United States, it is the most diagnosed cancer in young adults under the age of twenty-nine. All these factors make it worthwhile to fully understand the pathogenesis of thyroid cancer. Towards this end, microRNAs (miRNAs) have constantly emerged as the non-coding RNAs of interest in various thyroid cancer subtypes on which there have been numerous investigations over the last decade and half. This comprehensive review takes a look at the current knowledge on the topic with cataloging of miRNAs known so far, particularly related to their utility as epigenetic signatures of thyroid cancer progression and metastasis. Such information could be of immense use for the eventual development of miRNAs as therapeutic targets or even therapeutic agents for thyroid cancer therapy.

Identification of 5-nitrofuran-2-amide derivatives that induce apoptosis in triple negative breast cancer cells by activating C/EBP-homologous protein expression.

The transcription factor C/EBP-homologous protein (CHOP) is a key component of the terminal unfolded protein response (UPR) that mediates unresolvable endoplasmic reticulum stress-induced apoptosis. CHOP induction is known to cause cancer cell death. Chemicals that induce CHOP expression would thus be valuable as potential cancer therapeutics and as research tools. Here, we identified 5-nitrofuran-2-amide derivatives as small molecule activators of CHOP expression that induced apoptosis in triple negative breast cancer (TNBC) cells. Our preliminary structure-activity relationship studies indicated that compounds with an N-phenyl-5-nitrofuran-2-carboxamide skeleton were particularly potent inducers of TNBC cell apoptosis. The compounds activate CHOP expression via the PERK-eIF2α-ATF4 branch of the UPR. These results indicate that small molecule activators of CHOP expression may have therapeutic potential for TNBC.

Discovery of Intestinal Targeted TGR5 Agonists for the Treatment of Type 2 Diabetes.

Activation of TGR5 stimulates intestinal glucagon-like peptide-1 (GLP-1) release, but activation of the receptors in gallbladder and heart has been shown to cause severe on-target side effects. A series of low-absorbed TGR5 agonists was prepared by modifying compound 2 with polar functional groups to limit systemic exposure and specifically activate TGR5 in the intestine. Compound 15c, with a molecular weight of 1401, a PSA value of 223 Å(2), and low permeability on Caco-2 cells, exhibited satisfactory potency both in vitro and in vivo. Low levels of 15c were detected in blood, bile, and gallbladder tissue, and gallbladder-related side effects were substantially decreased compared to the absorbed small-molecule TGR5 agonist 2.

Identification of small molecules that protect pancreatic ß cells against endoplasmic reticulum stress-induced cell death.

Endoplasmic reticulum (ER) stress plays an important role in the decline in pancreatic ß cell function and mass observed in type 2 diabetes. Here, we developed a novel ß cell-based high-throughput screening assay to identify small molecules that protect ß cells against ER stress-induced cell death. Mouse ßTC6 cells were treated with the ER stressor tunicamycin to induce ER stress, and cell death was measured as a reduction in cellular ATP. A collection of 17600 compounds was screened for molecules that promote ß cell survival. Of the approximately 80 positive hits, two selected compounds were able to increase the survival of human primary ß cells and rodent ß cell lines subjected to ER stressors including palmitate, a free fatty acid of pathological relevance to diabetes. These compounds also restored ER stress-impaired glucose-stimulated insulin secretion responses. We show that the compounds promote ß cell survival by reducing the expression of key genes of the unfolded protein response and apoptosis, thus alleviating ER stress. Identification of small molecules that prevent ER stress-induced ß cell dysfunction and death may provide a new modality for the treatment of diabetes.