Computational biology and chemistry最新文献

With climate change, habitat destruction, and increased population ages, the incidence of both communicable and non-communicable diseases is rising, and managing these has become a growing concern. In recent years, generative artificial intelligence (AI) and large language models (LLMs) have ushered in a transformative era for smart healthcare applications. These models, built on advanced ML architectures like Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT), have demonstrated significant capabilities in various medical tasks. This review aims to provide an overview of the potential benefits of generative AI and LLMs in smart healthcare applications, as well as challenges and ethical considerations. A systematic literature review was conducted to identify relevant research papers published in peer-reviewed journals. Databases such as PubMed, PMC, Cochrane Library, Google Scholar, and Web of Science were searched using keywords related to generative AI, LLMs, and healthcare applications. The relevant papers were analyzed to extract key findings and contributions. Generative AI and LLMs are powerful tools that can process and analyze massive amounts of data. Researchers are actively exploring their potential to transform healthcare-powering intelligent virtual health assistants, crafting personalized patient care plans, and facilitating early detection and intervention for medical conditions. With ongoing research and development, the future of generative AI and LLMs in healthcare is promising; however, issues such as bias in AI models, lack of explainability, ethical concerns, and integration difficulties must be addressed.

Cytological examination of serous effusion is critical for diagnosing malignancies, yet it heavily relies on subjective interpretation by pathologists, leading to inconsistent accuracy and misdiagnosis, especially in regions with limited medical resources. To address this challenge, we propose a two-step deep learning framework to standardize and enhance the diagnostic process. First, we improved the YOLOv8 model by integrating the Online Convolutional Reparameterization (OREPA) module, achieving a 93.09 % sensitivity for detecting abnormal cells. Second, we employed the Dual Attention Vision Transformer (DaViT) to classify normal cells (lymphocytes, mesothelial cells, histiocytes, neutrophils) with 98.74 % accuracy. By jointly deploying these models, our approach reduces missed diagnoses and provides granular insights into cell composition, offering a robust tool for rapid and objective cytopathological diagnosis. This work bridges the gap between AI-driven automation and clinical needs, particularly in resource-constrained settings.

Antimicrobial resistance (AMR) poses a growing global threat, with antibiotic-resistant infections becoming a leading cause of death worldwide. The present study explores natural cyanobacterial compounds as possible inhibitors of Escherichia coli DNA gyrase B (GyrB) which is a verified antibacterial target that is not present in higher eukaryotes. Because of the urgent need for novel antibacterial drugs, we identified nine drug-like candidates using lipinski's rule of five and ADMET profiling. Molecular docking revealed that Biselyngbyaside B and Smenamide A exhibited greater binding affinities in comparison to the co-crystallized inhibitor EOF, with a binding energy of -9.03 kcal/mol. Further molecular dynamics simulations revealed that the Biselyngbyaside B-DNA gyrase B complex surpassed both EOF and Smenamide A in terms of structural stability, compactness, and strong hydrogen bonding. Umbrella sampling was employed to estimate the binding free energy from thirty sampling simulations, and Biselyngbyaside B exhibited a significantly favourable ΔG bind of -91.66 kJ/mol, outperforming EOF (-68.93 kJ/mol) and Smenamide A (-36.4 kJ/mol). These findings clearly indicate a stronger and more stable interaction between Biselyngbyaside B and GyrB. Biselyngbyaside B continuously showed better pharmacokinetic characteristics, non-hepatotoxicity, and a greater binding affinity than previously documented DNA gyrase B inhibitors. This study emphasizes the integration of molecular dockings, molecular dynamics simulation, umbrella sampling, and ADMET analysis provided crucial quantitative insights into the identification of potent drug-like candidates for further validation. Overall, the Biselyngbyaside B was found to be the most promising lead compound for novel antibacterial drug development targeting DNA gyrase B.

Acid sphingomyelinases (aSMases) are enzymes involved in the repair of the plasma membrane in eukaryotic cells. However, neutral sphingomyelinases (nSMases) have also been shown to possess other roles in bacteria and eukaryotic microorganisms, especially as virulence factors. These enzymes exhibit structural conservation but are characterized by elusive homology and the lack of sequence signatures or motifs. In a previous study, we reported the structural features of the complete set of sphingomyelinases (SMases) in Entamoeba histolytica and Trichomonas vaginalis, showing structural homology and functional differences in two aSMases from E. histolytica (EhSMase). However, the approach was limited due to the AlphaFold3 source code not being publicly available at the time. In this report, the structural transitions in the aSMases from T. vaginalis (TvSMase) were measured using open-source AlphaFold3 and collective motions of proteins via Normal Mode Analysis in internal coordinates. They compared them with the models from aSMase4 (EHI_100080) and aSMase6 (EHI_125660) from E. histolytica, containing different combinations of ligands. Using full-length sphingomyelin and the Mg²⁺ and Co²⁺ ions, where Co²⁺ was shown to inhibit the enzymes of both organisms, we demonstrate that the enzymes exhibit limited flexibility and deformability, except for the T. vaginalis TVAG_271580 enzyme, which displays high structural deformability. This contrasts with the inhibitory mechanism elicited by Co²⁺ as shown previously. TVSMase3 (TVAG_222460) could not be modelled with the sphingomyelin in the active site pocket, suggesting a regulatory role rather than a functional active enzyme. Additional physicochemical parameters calculated for T. vaginalis enzymes suggest unstable structures and high internal mobility (estimated using the Internal Coordinate method), which may be associated with the functional role of these enzymes. The results presented here open an avenue for searching for novel inhibitors of aSMases that target their physical properties, which could potentially complement treatment to control the parasite burden. These inhibitors could be valuable for further studying the role of these enzymes in parasite pathobiology and, potentially, as therapeutic targets.