Computational biology and chemistry最新文献

Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD patterns requires further investigation. This study identified a novel molecular classification named PCD subtype (C1, C2, and C3) based on the genes associated with 19 PCD patterns, distinguished by clinical, biological functional pathways, mutations, immune characteristics, and drug sensitivity. Validated in 4 independent datasets, diverse cell death pathways were enriched in the C3 subtype, including apoptosis, pyroptosis, and autophagy, it also exhibited a highly infiltrative immunosuppressive microenvironment and demonstrated higher sensitivity to compounds such as Paclitaxel, Bortezomib, and YK-4-279, while C1 subtype was significantly enriched in cuproptosis and metabolism-related pathways, suggesting that it may be more suitable for cuproptosis-inducing agent therapy. Subsequently, utilizing the machine learning algorithms, we constructed a cell death-related index (CDRI) with 22 gene features and constructed prognostic nomograms with high predictive performance by combining CDRI with clinical features. Notably, we found that CDRI effectively predicted the response of HCC patients to therapeutic strategies, where patients with high CDRI were more suitable for sorafenib drug therapy and patients with low CDRI were more ideal for transarterial chemoembolization (TACE). In conclusion, the PCD subtype and CDRI demonstrate significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. These findings offer valuable insights for the development of precise, individualized treatment strategies.

Menthol is a naturally occurring cyclic terpene alcohol and is the major component of peppermint and corn mint essential oils extracted from Mentha piperita L. and Mentha arvensis L.. Menthol and its derivatives are widely used in pharmaceutical, cosmetic and food industries. Among its eight isomers, (-)-menthol is the most effective one in terms of refreshing effect. While the invigorating property of (-)-menthol is generally known, this claim is based on a substantial amount of literature and experience. (-)-Menthol has consistently been reported to possess better cooling and refreshing qualities in comparison to its isomers, making it the preferred choice in a broad range of applications such as personal care products, pharmaceuticals and food additives. Additionally, the (-)-menthol molecular structure allows it to have a tighter fitting with the thermoreceptors in the skin and mucous membranes, and thus to provide a more intense cooling feeling. Thus, although others have similar properties to a degree, (-)-menthol is the best compared to all in its refreshing capacity. This study focuses on menthol and some of its esters, viz. menthyl acetate, propionate, butyrate, valerate and hexanoate, with the purpose of establish a connection between structural, electrostatic and electronic characteristics and biological effects. The mostly favoured interactions of the esters with biotargets were investigated at a molecular level, offering a plausible foundation for their bioactivity elucidation. This study is conducted at a quantum mechanical and molecular docking level. The results may be of possible usefulness in areas of applications, such as pharmacological research and drug.

Plastics play an essential role in modern fisheries and their degradation releases micro- and nano-sized plastic particles which further causes ecological and human health hazards through various environmental contamination pathways and toxicity mechanisms, which can cause respiratory problems, cancer, reproductive toxicity, endocrine disruption and neurological effects in humans. This study utilized various bioinformatics tools through multi-step computational analyses to investigate the interactions between prevalent fisheries microplastics and the key protein receptor acetylcholinesterase (AChE), which is associated with neurotoxicity, as it can interfere with nerve impulses and muscle control. Our results indicate that the binding of seven polymers within AChE's active site, with dodecane and polypropylene exhibited highest affinity with hydrogen bonding were observed through Molecular docking of different program (PyRx) and servers (CB-Dock, eDock) then the stability of AChE-dodecane and AChE-polypropylene complexes were observed through MD simulations for 100 ns. Further analysis of dodecane was done by using pharmacophore modelling and virtual screening. The pharmacophore model of dodecane is based on six hydrophobic rings. Using this model, we screened among thousands of substrates form (CMNPD, COCONUT, NPASS, NANPDB, and ZINC) database and identified fifty highly similar candidates that align with dodecane's structure and interaction with acetylcholinesterase (AChE). The compound triacontafluoropentadec-1-ene exhibited highest binding affinity (score: -9.6) which was further confirmed through molecular dynamics for 100 ns. The key finding for this study is triacontafluoropentadec-1-ene as a promising alternative to dodecane, and the study highlights that the integrated in silico framework presents a valuable computational model for guiding future guidelines on environmental safety through prioritizing constituents and accelerated discovery of alternatives. These findings will help us identify the most hazardous plastics through ranking and characterizing the substance for sustainably "greening" fisheries worldwide. The study forecasts the groundwork of these compounds, which may be able to reduce the environmental toxicity of microplastics in future.

The pathophysiological distinctions between osteoarthritis (OA) and diabetic osteoarthritis (DOA) are critical yet not well delineated. In this study, we employed single-cell RNA sequencing to clarify the unique cellular and molecular mechanisms underpinning the progression of both conditions. We identified a novel subpopulation of chondrocytes in DOA, termed 'Heat Shock' chondrocytes, marked by the expression of distinct molecular markers including HSPA1A, HSPA1B, HSPB1, and HSPA8. Our comprehensive gene expression analysis revealed a pronounced upregulation of inflammatory pathways associated with oxidative stress-namely the MAPK, NF-κB, and PI3K signaling pathways-in the effector and proliferating chondrocyte subpopulations, with a predominance in DOA. Further, our investigation into cell-cell communication demonstrated a significant diminution of intercellular signaling in DOA compared to OA. These insights not only elucidate distinct cellular heterogeneities and potential pathogenic mechanisms differentiating OA from DOA but also enhance our understanding of their molecular pathophysiology, offering novel avenues for targeted therapeutic strategies.

Cancer metastasis is the dissemination of tumor cells from the primary tumor site to other parts of the body via the lymph system or bloodstream. Metastasis is the leading cause of cancer associated death. Despite the significant advances in cancer research and treatment over the past decades, metastasis is not fully understood and difficult to predict in advance. In particular, distant metastasis is more difficult to predict than lymph node metastasis, which is the spread of cancer cells to nearby lymph nodes. Distant metastatic sites is even more difficult to predict than the occurrence of distant metastasis because the problem of predicting distant metastatic sites is a multi-class and multi-label classification problem; there are more than two classes for distant metastatic sites (bone, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. This paper presents a new method for predicting distant metastatic sites based on correlation changes of miRNAs with competing endogenous RNAs (ceRNAs) in individual cancer patients. Testing the method on independent datasets of several cancer types demonstrated a high prediction performance. In comparison of our method with other state of the art methods, our method showed a much better and more stable performance than the others. Our method can be used as useful aids in determining treatment options by predicting if and where metastasis will occur in cancer patients at early stages.

Nipah virus (NiV) is a re-emerging zoonotic pathogen with a high mortality rate and no effective treatments, prompting the search for new antiviral strategies. While conventional antiviral drugs are often limited by issues such as poor specificity, off-target effects, and resistance development, nanobodies offer distinct advantages. These small, single-domain antibodies exhibit high specificity and stability, making them ideal candidates for antiviral therapy. The NiV fusion protein (NiVF) is a crucial target for nanobodies due to its vital role in infection. Thus, we aimed to design a high affinity nanobody targeting NiVF using computational methods. Molecular docking identified the lead NB with the highest binding energy to NiVF. The complementarity determining regions (CDRs) of the lead NB underwent two rounds of in silico site-directed mutagenesis generating a high-affinity engineered NB. Subsequent re-docking, molecular dynamics (MD) simulations, and various in silico evaluations, of the selected engineered NB-NiVF complex were performed. After mutations, results showed that the lead (native) NB, initially with a binding energy of -85.2 kcal.mol^-1, was optimized to an engineered NB with a higher binding energy of -99.65 kcal.mol^-1. Additionally, the engineered NB has more favorable physicochemical properties, exhibited a more stable (in a 200-ns MD simulation) and stronger molecular interactions than the native NB, suggesting a favorable mutation and enhancement of the potential neutralization activity of the engineered NB. This study highlights the use of computational methods to design an optimized high-affinity NB and the potential of NB-based antivirals against NiV, necessitating further experimental validation.

RNA methylation, particularly through m6A modification, represents a crucial epigenetic mechanism that governs gene expression and influences a range of biological functions. Accurate identification of methylation sites is crucial for understanding their biological functions. Traditional experimental methods, however, are often costly and can be influenced by experimental conditions, making machine learning, especially deep learning techniques, a vital tool for m6A site identification. Despite their utility, current machine learning models struggle with unbalanced datasets, a common issue in bioinformatics. This study addresses the RNA methylation site data imbalance problem from three key perspectives: feature encoding representation, deep learning models, and data resampling strategies. Using the K-mer one-hot encoding strategy, we effectively extracted RNA sequence features and developed classification prediction models utilizing long short-term memory networks (LSTM) and its variant, Multiplicative LSTM (mLSTM). We further enhanced model performance by ensemble and weighted strategy models. Additionally, we utilized the sequence generative adversarial network (SeqGAN) and the synthetic minority resampling technique (SMOTE) to construct balanced datasets for RNA methylation sites. The prediction results were rigorously analyzed using the Wilcoxon test and multivariate linear regression to explore the effects of different K-mer values, model architectures, and sampling methods on classification outcomes. The analysis underscored the significant impact of feature selection, model architecture, and sampling techniques in addressing data imbalance. Notably, the optimal prediction performance was achieved with a K value of 5 using the mLSTM-ensemble model. These findings not only offer new insights and methodologies for RNA methylation site identification but also provide valuable guidance for addressing similar challenges in bioinformatics.

This study develops an Artificial Neural Network (ANN)-based framework to model the transmission dynamics and long-term disability outcomes of Ebola Virus Disease (EVD). Building on existing deterministic SEIR models, we extend the framework by introducing a disability compartment, capturing the progression of Ebola survivors to chronic health complications, such as post-Ebola syndrome. The proposed model stratifies the population into various epidemiological states, incorporating delays to better reflect the natural progression and intervention strategies associated with EVD. Fundamental properties of the model, such as positivity, boundedness, and stability, have been thoroughly examined. By leveraging the Levenberg-Marquardt backpropagation (LMB) algorithm, the ANN is trained on data generated through the Runge-Kutta method to solve a system of delay differential equations (DDEs) representing disease progression. This approach offers an alternative to conventional numerical solvers, addressing limitations such as computational overhead and approximation errors. The ANN model divides the dataset into 85% training, 10% validation, and 5% testing, ensuring reliable predictions with minimal absolute error. Comparative analysis against traditional methods highlights the advantages of the ANN-based solver in handling complex, delay-integrated systems. Our results underscore the utility of integrating ANN approaches in epidemic modeling, providing insights into both short- and long-term dynamics of Ebola outbreaks. By capturing disability outcomes, this work offers a robust framework for planning healthcare interventions and optimizing resource allocation for survivor rehabilitation. The findings contribute to the development of more comprehensive models for understanding and managing infectious diseases with long-term impacts.

Multiple myeloma (MM) is the second most frequently diagnosed hematological malignancy, presenting limited treatment options with no curative potential and significant drug resistance. Recent studies involving genetic knockdown established the crucial role of GRK6 in upholding the viability of MM cells, emphasizing the need to identify potential inhibitors. Computational exploration of GRK6 inhibitors has not been attempted previously. Herein, the present study reports a multilayered lead prioritization and optimization framework using chemometrics and molecular simulations. 2D QSAR studies revealed that hydrogen bonding and polar interactions enhanced GRK6 inhibitory activity, while increased electron accessibility posed a risk of off-target effects. The pharmacophore hypothesis (DDHRRR_1) featured two hydrogen bond donors, one hydrophobic region, and three aromatic rings, laying the foundation for the 3D QSAR models. Hydrophobic groups, such as pyridine and pyrazole, were shown to enhance inhibition, while smaller groups, like ethyl and hydroxyl, reduced activity. 12,557 DrugBank compounds were screened using the developed chemometric models and molecular docking in tandem, which led to the identification of 7 potential parent leads for subsequent QSAR-guided structural optimizations. 350 lead analogs were generated and the top 4 were further analyzed using molecular docking, ADMET, molecular dynamics, and metadynamics analysis based on Principal Component Analysis (PCA), Probability Density Function (PDF), and Free Energy Landscapes (FEL). Upon cumulative retrospection, we propose a novel analog of DB07168 (DB07168-A13) (docking score: -11.2 kcal/mol, MM-GBSA binding energy: -55.2 kcal/mol) as the most promising GRK6 inhibitor, warranting further in vitro validation, for addressing prospective therapeutic intervention in MM.

This study aimed to profile metabolites from five Trichoderma strains and assess their cytotoxic and pharmacological activities, particularly targeting oral squamous cell carcinoma (OSCC). UHPLC-TOF-MS analysis revealed the presence of 25 compounds, including heptelidic acid, viridiol isomers, and sorbicillinol from the different Trichoderma extracts. Pharmacokinetic analysis showed moderate permeability and low interaction with P-glycoprotein, suggesting good drug absorption with minimal interference in cellular uptake. ADME-Tox analysis indicated limited inhibition of cytochrome P450 enzymes, low renal clearance, which are favorable for maintaining therapeutic levels. Toxicity predictions revealed some compounds with potential mutagenicity, but low hepatotoxicity and skin sensitization risks. Network pharmacology identified MAPK1 as a key target for oral cancer, and molecular docking and induced fit docking studies demonstrated strong binding affinities of Trichoderma metabolites, including stachyose and harzianol, to MAPK1. In addition, molecular dynamics (MD) simulations confirmed stable interactions. In vitro studies on NIH3T3 and YD-10B cells showed significant cytotoxicity, particularly with extracts CNU-05-001 (IC₅₀:10.15 µg/mL) and CNU-02-009 (10.00 µg/mL) against YD-10B cells. These findings underscore the potential of Trichoderma metabolites in drug discovery, particularly for cancer therapies.