Prohormone cleavage prediction uncovers a non-incretin anti-obesity peptide
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Role of pancreatic lipase inhibition in obesity treatment: mechanisms and challenges towards current insights and future directions
The worldwide health emergency of obesity is closely connected to how dietary fats are metabolized, whereas the process is significantly influenced by pancreatic lipase (PL), an enzyme critical for lipid hydrolysis into fatty acids. This narrative review employs a methodological approach utilizing literature searches of PubMed data up to March 2024. The search term criteria encompasses keywords related to the role, mechanism, challenges, and current and future treatments of pancreatic lipase in obesity with an overall references is 106. This paper offers a comprehensive explanation of the role of PL, underlining its significance in the digestive process and lipid imbalances that contribute to obesity and by extension, its impact on obesity development and progression. Additionally, it delves into the dual functionality of the pancreas, emphasizing its impact on metabolism and energy utilization which, when dysregulated, promotes obesity. A focal point of this review is the investigation into the efficacy, challenges, and adverse effects of current pancreatic lipase inhibitors, with orlistat being highlighted as a primary current drug delivery. By discussing advanced obesity treatments, including the exploration of novel anti-obesity medications that target specific biological pathways, this review underscores the complexity of obesity treatment and the necessity for a multifaceted approach. In conclusion, this paper emphasizing the importance of understanding the role of enzymes like pancreatic lipase mechanistic and adopting a multidisciplinary approach to treatment and side effects of current obesity drugs and explore new emerging therapeutic strategies for more effective obesity management.
Binary peptide coacervates as an active model for biomolecular condensates
Biomolecular condensates formed by proteins and nucleic acids are critical for cellular processes. Macromolecule-based coacervate droplets formed by liquid-liquid phase separation serve as synthetic analogues, but are limited by complex compositions and high molecular weights. Recently, short peptides have emerged as an alternative component of coacervates, but tend to form metastable microdroplets that evolve into rigid nanostructures. Here we present programmable coacervates using binary mixtures of diphenylalanine-based short peptides. We show that the presence of different short peptides stabilizes the coacervate phase and prevents the formation of rigid structures, allowing peptide coacervates to be used as stable adaptive compartments. This approach allows fine control of droplet formation and dynamic morphological changes in response to physiological triggers. As compartments, short peptide coacervates sequester hydrophobic molecules and enhance bio-orthogonal catalysis. In addition, the incorporation of coacervates into model synthetic cells enables the design of Boolean logic gates. Our findings highlight the potential of short peptide coacervates for creating adaptive biomimetic systems and provide insight into the principles of phase separation in biomolecular condensates.
Pediatric obesity and the risk of multiple sclerosis: a nationwide prospective cohort study
Emerging evidence implies a link between high pediatric body mass index (BMI) and an increased risk of multiple sclerosis (MS). However, previous research suggests this association is only present for adolescent obesity and not childhood obesity. The present study aimed to assess the association between pediatric obesity and risk of developing MS, and to investigate if degree of obesity and age at obesity treatment initiation affects the risk. In a subgroup, response to obesity treatment on MS risk was assessed.
Cellular and molecular mechanisms underlying obesity in degenerative spine and joint diseases
Degenerative spine and joint diseases, including intervertebral disc degeneration (IDD), ossification of the spinal ligaments (OSL), and osteoarthritis (OA), are common musculoskeletal diseases that cause pain or disability to the patients. However, the pathogenesis of these musculoskeletal disorders is complex and has not been elucidated clearly to date. As a matter of fact, the spine and joints are not independent of other organs and tissues. Recently, accumulating evidence demonstrates the association between obesity and degenerative musculoskeletal diseases. Obesity is a common metabolic disease characterized by excessive adipose tissue or abnormal adipose distribution in the body. Excessive mechanical stress is regarded as a critical risk factor for obesity-related pathology. Additionally, obesity-related factors, mainly including lipid metabolism disorder, dysregulated pro-inflammatory adipokines and cytokines, are reported as plausible links between obesity and various human diseases. Importantly, these obesity-related factors are deeply involved in the regulation of cell phenotypes and cell fates, extracellular matrix (ECM) metabolism, and inflammation in the pathophysiological processes of degenerative spine and joint diseases. In this study, we systematically discuss the potential cellular and molecular mechanisms underlying obesity in these degenerative musculoskeletal diseases, and hope to provide novel insights for developing targeted therapeutic strategies.
Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens
Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein–Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.
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