Computational Biology and Chemistry最新文献_第4页

Computational analysis of natural compounds as potential phosphodiesterase type 5A inhibitors 作为潜在 5A 型磷酸二酯酶抑制剂的天然化合物的计算分析

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-10 DOI: 10.1016/j.compbiolchem.2024.108239

Phosphodiesterase type 5 (PDE5) is a cyclic nucleotide-hydrolyzing enzyme that plays essential roles in the regulation of second messenger cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) produced in response to various stimuli. Pharmacological inhibition of PDE5 has been shown to have several therapeutic uses, including treating cardiovascular diseases and erectile dysfunction. In search of PDE5A inhibitors with safer pharmacokinetic properties, computational analyses of the binding propensity of fifty natural compounds comprising flavonoids, polyphenols, and glycosides were conducted. Molecular dynamics simulation coupled with Molecular mechanics with generalized Born and surface area solvation (MM/GBSA) showed that verbascoside may inhibit the activity of PDE5 with a comparative binding energy (ΔG) of -87.8 ± 9.2 kcal/mol to that of the cocrystal ligand (PDB ID: 3BJC), having ΔG = -77.7±4.5 kcal/mol. However, the other top compounds studied were found to have lower binding propensities than the cocrystal ligand WAN: hesperidin (ΔG = -33.8 ± 3.4 kcal/mol), rutin (ΔG = -23.6 ± 26.3 kcal/mol), caftaric acid (ΔG = -21.2 ±3.6 kcal/mol), and chlorogenic acid (ΔG = 6.0 ± 16.5 kcal/mol). Therefore, verbascoside may serve as a potential PDE5A inhibitor while hesperidin, rutin, and caftaric acid may provide templates for further structural optimization for the designs of safer PDE5 inhibitors.

5 型磷酸二酯酶（PDE5）是一种环核苷酸水解酶，在调节第二信使环磷酸腺苷（cAMP）和环磷酸鸟苷（cGMP）对各种刺激的反应中发挥着重要作用。药理抑制 PDE5 已被证明具有多种治疗用途，包括治疗心血管疾病和勃起功能障碍。为了寻找具有更安全药代动力学特性的 PDE5A 抑制剂，研究人员对包括类黄酮、多酚和苷类在内的 50 种天然化合物的结合倾向进行了计算分析。分子动力学模拟结合广义玻恩和表面积溶解分子力学（MM/GBSA）表明，马鞭草苷可抑制 PDE5 的活性，其结合能（ΔG）为-87.8 ± 9.2 kcal/mol，而共晶体配体（PDB ID：3BJC）的结合能（ΔG = -77.7±4.5 kcal/mol）为-77.8 ± 9.2 kcal/mol。然而，研究发现其他顶级化合物的结合率低于共晶配体 WAN：橙皮甙（ΔG = -33.8 ± 3.4 kcal/mol）、芦丁（ΔG = -23.6 ± 26.3 kcal/mol）、茶醛酸（ΔG = -21.2 ± 3.6 kcal/mol）和绿原酸（ΔG = 6.0 ± 16.5 kcal/mol）。因此，马鞭草苷可作为一种潜在的 PDE5A 抑制剂，而橙皮甙、芦丁和茶黄酸则可为进一步优化结构以设计更安全的 PDE5 抑制剂提供模板。

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引用次数: 0

Drug–target prediction through self supervised learning with dual task ensemble approach 利用双任务集合方法，通过自我监督学习进行药物靶标预测。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-10 DOI: 10.1016/j.compbiolchem.2024.108244

Drug–Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.

药物-靶点相互作用（DTI）预测是制药研究中的一种变革性方法，它为基于计算方法的虚拟筛选、解决未治疗疾病的现有药物以及发现现有药物的副作用寻求新的治疗应用。所提出的模型结合了药物、基因和疾病相关知识，通过异构生物网络预测 DTI。为实现嵌入提取的目的，使用了自我监督学习（SSL），通过借口任务训练模型，从而消除了人工注释的需要。借口任务与基于结构的信息或基于相似性的信息有关。为了减轻图神经网络的不稳定性，可以将集合学习纳入图神经网络，利用多个模型来增强稳健性。本文介绍了一种基于图神经网络的架构，该架构由基于任务的模块和用于 DTI 链接预测的集合模块组成。双任务组合的集合模块在冷启动和暖启动情况下都取得了非常好的性能，冷启动平均 AUCROC 得分为 0.960，暖启动平均 AUCROC 得分为 0.970，且偏差较小。

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引用次数: 0

A Novel Ensemble Approach with Deep Transfer Learning for Accurate Identification of Foodborne Bacteria from Hyperspectral Microscopy 利用深度迁移学习的新型集合方法从高光谱显微镜准确识别食源性细菌

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-09 DOI: 10.1016/j.compbiolchem.2024.108238

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100% for Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE), while achieving rates of 96.30% for Salmonella Typhimurium (ST), 87.13% for Staphylococcus aureus (SA), and 94.12% for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.

食源性细菌的检测对于确保消费者安全和食品安全至关重要。如果不能正确识别这些病原体，就会导致危险的交叉污染。对细菌进行分类的最常用方法之一是通过高光谱显微镜成像（HMI）进行检查。显微镜细胞计数是一种广泛使用的微生物生长测量技术。高光谱显微成像是一个费力且昂贵的过程，会产生大量数据，并且需要专业设备，而这些设备可能并不普及。目前，机器学习（ML）方法经常被用来自动解释高光谱显微镜的数据。我们的研究目标是设计一种技术，利用深度迁移学习来应对数据有限的挑战，并利用四个基础分类器--InceptionResNetV2、MobileNet、ResNet101V2 和 Xception--来创建一个基于集合的分类模型，以区分六种致病菌株的活细菌细胞和死细菌细胞。为了确定基础分类器的最佳权重，我们采用了鲍威尔优化法和加权平均集合（WAVE）技术。我们进行了广泛的实验研究，以验证我们提出的集合模型在六种不同食源性细菌的活细胞和死细胞图像上的效率。为了更好地了解这些区域，我们进行了 Grad-CAM 分析，以解释我们的模型所做的预测。通过一系列实验，我们提出的框架证明了其有效、精确检测多种细菌病原体的能力。具体来说，它对大肠杆菌（EC）、无毒李斯特菌（LI）和肠炎沙门氏菌（SE）的完美识别率达到了 100%，而对鼠伤寒沙门氏菌（ST）、金黄色葡萄球菌（SA）和海德堡沙门氏菌（SH）的识别率分别为 96.30%、87.13% 和 94.12%。因此，该方法因其高效率而被视为鉴定食源性病原体的有效工具。

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引用次数: 0

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection 基于核磁共振成像和语音信号的多模态抑郁检测的联合学习和深度学习框架

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-09 DOI: 10.1016/j.compbiolchem.2024.108232

Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this affects the daily life of the person. This leads to educational and social impairments and this acts as a major risk for suicide. As a result, the identification and treatment for this disorder are essential. By applying Deep learning (DL) algorithms to medical data, the mental condition of a person can be predicted. However, the traditional deep learning models face the challenge in processing the huge sized data. Hence, FL has emerged as an efficient solution for addressing the data size issue of DL. Here, Depression detection in adolescents is carried out by considering the FL framework, which comprises two modules, namely the local module and the Global module. The detection process is done in the local module using the proposed Exponential African Pelican Optimization based Deep Convolutional Neural Network (ExpAPO-DCNN), whereas the Global module produces the aggregated output of the local module. In this research, FL utilizes the DL model in producing the output, where the DL model considered two modalities of inputs, such as speech signal and Magnetic Resonance Imaging (MRI) image. The processing steps used for this research are pre-processing, feature extraction and detection. For MRI and speech signals, all the above processes are carried out individually. Finally, both the outputs are fused utilizing the overlap coefficient. The ExpAPO-DCNN obtained accuracy, Loss, Root mean Squared error (RMSE), Mean Squared error (MSE), True Negative rate (TNR), and True Positive rate (TPR) of 98.00 %, 0.023, 0.058, 0.240, 97.90 %, and 96.30 %, respectively.

青春期是发展技能和知识、学习如何处理人际关系和情绪、培养成熟特质的重要时期。最近，抑郁症成为青少年常见的心理健康问题，影响了他们的日常生活。这将导致教育和社交障碍，并成为自杀的主要风险。因此，识别和治疗这种疾病至关重要。通过将深度学习（DL）算法应用于医疗数据，可以预测一个人的精神状况。然而，传统的深度学习模型在处理海量数据时面临挑战。因此，FL 成为了解决 DL 数据大小问题的有效解决方案。在这里，青少年的抑郁检测是通过考虑 FL 框架来进行的，该框架由两个模块组成，即本地模块和全局模块。本地模块使用基于 Exponential African Pelican Optimization 的深度卷积神经网络（ExpAPO-DCNN）进行检测，而全局模块则生成本地模块的汇总输出。在这项研究中，FL 利用 DL 模型生成输出，其中 DL 模型考虑了两种输入模式，如语音信号和磁共振成像（MRI）图像。本研究采用的处理步骤包括预处理、特征提取和检测。对于核磁共振成像和语音信号，上述所有过程都是单独进行的。最后，利用重叠系数对两个输出进行融合。ExpAPO-DCNN 获得的准确率、损失、均方根误差 (RMSE)、均方根误差 (MSE)、真阴性率 (TNR) 和真阳性率 (TPR) 分别为 98.00 %、0.023、0.058、0.240、97.90 % 和 96.30 %。

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引用次数: 0

Leukotriene B4 receptor 1 (BLT1) activation by leukotriene B4 (LTB4) and E resolvins (RvE1 and RvE2) 白三烯 B4 (LTB4) 和 E resolvins (RvE1 和 RvE2) 激活白三烯 B4 受体 1 (BLT1)

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-06 DOI: 10.1016/j.compbiolchem.2024.108236

Leukotriene B4 (LTB₄) is a lipid inﬂammatory mediator derived from arachidonic acid (AA). Leukotriene B4 receptor 1 (BLT1), a G protein-coupled receptor (GPCR), is a receptor of LTB₄. Nonetheless, the resolution of inflammation is driven by specialized pro-resolving lipid mediators (SPMs) such as resolvins E1 (RvE1) and E2 (RvE2). Both resolvins are derived from omega-3 fatty acid eicosapentaenoic acid (EPA). Here, long-term molecular dynamics simulations (MD) were performed to investigate the activation of the BLT1 receptor using two pro-resolution agonists (RvE1 and RvE2) and an inflammatory agonist (LTB₄). We have analyzed the receptor's activation state, electrostatic interactions, and the binding affinity the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach. The results showed that LTB4 and RvE1 have kept the receptor in an active state by higher simulation time. MD showed that the ligand-receptor interactions occurred mainly through residues H94, R156, and R267. The MMPBSA calculations showed residues R156 and R267 were the two mainly hotspots. Our MMPBSA results were compatible with experimental results from other studies. Overall, the results from this study provide new insights into the activation mechanisms of the BLT1 receptor, reinforcing the role of critical residues and interactions in the binding of pro-resolution and pro-inflammatory agonists.

白三烯 B4（LTB4）是一种由花生四烯酸（AA）衍生的脂质炎症介质。白三烯 B4 受体 1（BLT1）是一种 G 蛋白偶联受体（GPCR），是 LTB4 的受体。然而，炎症的消退是由专门的促进消退脂质介质（SPMs）驱动的，如 resolvins E1（RvE1）和 E2（RvE2）。这两种溶解素都来源于欧米伽-3 脂肪酸二十碳五烯酸（EPA）。在此，我们进行了长期分子动力学模拟（MD），研究了使用两种促溶解激动剂（RvE1 和 RvE2）和一种炎症激动剂（LTB4）激活 BLT1 受体的过程。我们采用分子力学泊松-玻尔兹曼表面积（MMPBSA）方法分析了受体的活化状态、静电相互作用和结合亲和力。结果表明，LTB4 和 RvE1 在较长的模拟时间内使受体处于激活状态。MD 显示，配体与受体之间的相互作用主要通过 H94、R156 和 R267 等残基发生。MMPBSA 计算显示 R156 和 R267 残基是两个主要热点。我们的 MMPBSA 计算结果与其他研究的实验结果一致。总之，本研究的结果为 BLT1 受体的活化机制提供了新的见解，加强了关键残基和相互作用在促溶解和促炎症激动剂结合中的作用。

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引用次数: 0

Statistical analysis of the unique characteristics of secondary structures in proteins 对蛋白质二级结构独特特征的统计分析。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-05 DOI: 10.1016/j.compbiolchem.2024.108237

Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as

α

-helices,

β

-sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that

α

-helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to

α

-helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at https://proseqanalyser.iitgn.ac.in/

蛋白质折叠是一个受氨基酸主序列影响的复杂过程。早期的研究侧重于了解氨基酸的特异性或特性保持对于折叠成二级结构（如α螺旋、β片、转折和线圈）是否至关重要。然而，随着人工智能（AI）和机器学习（ML）的出现，重点已转向特定氨基酸的精确性质和出现。在我们的研究中，我们分析了来自不同生物体的大量蛋白质，以确定二级结构的独特特征，特别是极性、非极性和带电氨基酸残基的分布。我们发现，与其他二级结构相比，α-螺旋中带电和非极性基团的比例往往较高，螺旋中存在带相反电荷的氨基酸残基可使螺旋稳定，有利于形成较长的螺旋。这些特征与α螺旋截然不同。这项研究为蛋白质设计领域的研究人员提供了宝贵的见解，使他们能够重新创造短螺旋肽，用于一系列应用。我们还开发了一个网络服务器，用于广泛分析不同数据库中的蛋白质。网络服务器设在 https://proseqanalyser.iitgn.ac.in/。

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引用次数: 0

A novel prognostic risk score model based on RNA editing level in lower-grade glioma 基于低级别胶质瘤 RNA 编辑水平的新型预后风险评分模型。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-05 DOI: 10.1016/j.compbiolchem.2024.108229

Background

Lower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently.

Methods

Survival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Results

A risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing.

Conclusion

In conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

背景：低级别胶质瘤（LGG）是指WHO 2级和3级胶质瘤。手术结合放疗和化疗可显著改善 LGG 患者的预后，但肿瘤进展仍不可避免。据报道，RNA编辑（RE）作为转录后调控的一种形式，参与了肿瘤的发生和发展，最近对此进行了深入研究：方法：对生存数据和 RE 数据进行单变量和多变量 Cox 回归分析以及 lasso 回归分析，以建立 RE 风险评分模型。结合风险评分和临床病理特征，建立了预测患者1年、3年和5年生存概率的提名图。通过反转录-定量聚合酶链反应（RT-qPCR）验证了ADAR1、SOD2和SOAT1之间的关系结果：建立了与RE相关的风险模型，并根据风险评分将患者分为不同的风险组。该模型显示出很强的预后能力，预测 1 年、3 年和 5 年生存率的 ROC 曲线下面积（AUC）值分别为 0.882、0.938 和 0.947。通过接收者操作特征曲线（ROC）和校准曲线，验证了所构建的提名图在预测患者生存概率方面的性能优于年龄、分级和风险评分。除功能分析外，风险差异表达基因（RDEGs）与RE之间的相关性分析结果也有助于我们了解LGG中RE的内在机制。ADAR可能通过基因编辑调控SOD2和SOAT1的表达：总之，本研究建立了一个新颖、准确的17-RE模型和提名图，用于预测LGG患者的生存概率。ADAR可能通过影响基因表达来影响胶质瘤患者的预后。

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引用次数: 0

In silico profiling, docking analysis, and protein interactions of secondary metabolites in Musa spp. Against the SGE1 protein of Fusarium oxysporum f. sp. cubense 针对 Fusarium oxysporum f. sp. cubense 的 SGE1 蛋白的硅学剖析、对接分析以及麝香树次生代谢物与蛋白质的相互作用

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-05 DOI: 10.1016/j.compbiolchem.2024.108230

Banana Fusarium Wilt (BFW), caused by Fusarium oxysporum f. sp. cubense (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in Musa accuminata subsp. aalaccensis. Through in silico analyses, we explored interactions between Musa spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in Musa spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected Musa metabolites, such as 3-Phenylphenol, Catechin, and Epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7 kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in Musa spp. identified key domains like Chalcone synthase, Phenylalanine ammonia-lyase, Aminotran 1–2, and CoA-ligase, which are integral to phenylpropanoid production—a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for Musa spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.

由 Fusarium oxysporum f. sp. cubense（Foc）引起的香蕉镰刀菌枯萎病（Banana Fusarium Wilt，BFW）威胁着全球的香蕉作物，病原体的毒力部分受 Sge1 转录因子的调控，而 Sge1 转录因子会增强病害的严重性。某些蕈蚊物种对 Foc 具有抗性，这表明其固有的遗传特性赋予了对 Sge1Foc 的免疫力。本研究利用生物信息学工具研究了茄子亚种（Musa accuminata subsp.通过硅学分析，我们探索了麝香草属植物与 Foc 之间的相互作用，重点是 Sge1 蛋白。Anti-SMASH、AutoDockVina 4.0、STRING 和 Phoenix 等工具有助于分析穆萨属植物的次生代谢物，并确定参与防御的生物合成基因簇。我们的研究结果表明，次生代谢物（包括糖类、萜烯类和多酮类化合物）对植物的免疫反应至关重要。对 3-苯基苯酚、儿茶素和表儿茶素等选定的麝香草代谢物进行的分子对接研究显示，3-苯基苯酚与 Sge1Foc 蛋白的结合亲和力最高（-6.7 kcal/mol）。进一步分析与麝香草次生代谢物生物合成相关的基因簇，发现了查耳酮合成酶、苯丙氨酸氨解酶、氨基曲霉 1-2 和 CoA 连接酶等关键域，它们是苯丙类化合物生产不可或缺的部分--这是次生代谢物的关键途径。该研究强调，苯丙酮途径和次生代谢物的生物合成对麝香草属植物抵抗 Foc 至关重要。类黄酮和木质素可能会抑制 Sge1 蛋白的形成，从而可能破坏 Foc 的细胞过程。这些发现强调了苯丙类途径和次生代谢物在抗BFW中的作用，并表明针对这些途径可提供创新战略，增强香蕉作物的抗性并控制BFW。这项研究为开发保护香蕉种植和确保粮食安全的可持续方法奠定了基础。

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引用次数: 0

Drug repositioning identifies potential autophagy inhibitors for the LIR motif p62/SQSTM1 protein 药物重新定位为 LIR motif p62/SQSTM1 蛋白确定了潜在的自噬抑制剂。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-03 DOI: 10.1016/j.compbiolchem.2024.108235

Autophagy is a critical cellular process for degrading damaged organelles and proteins under stressful conditions and has casually been shown to contribute to tumor survival and drug resistance. Sequestosome-1 (SQSTM1/p62) is an autophagy receptor that interacts with its binding partners via the LC3-interacting region (LIR). The p62 protein has been a highly researched target for its critical role in selective autophagy. In this study, we aimed to identify FDA-approved drugs that bind to the LIR motif of p62 and inhibit its LIR function, which could be useful targets for modulating autophagy. To this, the homology model of the p62 protein was predicted using biological data, and docking analysis was performed using Molegro Virtual Docker and PyRx softwares. We further assessed the toxicity profile of the drugs using the ProTox-II server and performed dynamics simulations on the effective candidate drugs identified. The results revealed that the kanamycin, velpatasvir, verteporfin, and temoporfin significantly decreased the binding of LIR to the p62 protein. Finally, we experimentally confirmed that Kanamycin can inhibit autophagy-associated acidic vesicular formation in breast cancer MCF-7 and MDA-MB 231 cells. These repositioned drugs may represent novel autophagy modulators in clinical management, warranting further investigation.

自噬是在应激条件下降解受损细胞器和蛋白质的关键细胞过程，已被证明有助于肿瘤的存活和耐药性。Sequestosome-1（SQSTM1/p62）是一种自噬受体，它通过 LC3 结合区（LIR）与其结合伙伴相互作用。p62 蛋白因其在选择性自噬中的关键作用而成为备受研究的靶标。在本研究中，我们旨在找出与 p62 的 LIR 矩阵结合并抑制其 LIR 功能的 FDA 批准药物，这些药物可能是调节自噬的有用靶点。为此，我们利用生物数据预测了 p62 蛋白的同源模型，并使用 Molegro Virtual Docker 和 PyRx 软件进行了对接分析。我们使用 ProTox-II 服务器进一步评估了药物的毒性特征，并对确定的有效候选药物进行了动力学模拟。结果显示，卡那霉素、velpatasvir、verteporfin 和 temoporfin 能显著减少 LIR 与 p62 蛋白的结合。最后，我们通过实验证实，卡那霉素能抑制乳腺癌 MCF-7 和 MDA-MB 231 细胞中与自噬相关的酸性囊泡的形成。这些重新定位的药物可能是临床治疗中新型的自噬调节剂，值得进一步研究。

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引用次数: 0

Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks 分析 COVID-19 传播风险的智能计算框架：迈耶小波人工神经网络

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-02 DOI: 10.1016/j.compbiolchem.2024.108234

The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.

COVID-19 模型的流行病学最佳控制方法是通过一种新型的先进智能计算基础设施来实现的，该基础设施将人工神经网络与基于无监督学习的优化器（即遗传算法（GA）和顺序二次编程（SQP））结合在一起。提供的无监督学习策略取决于小波基对随机数据的顺序解构。神经网络的权重或选择值是利用迈耶小波人工神经网络（MWANNs）的累积算法与全局搜索遗传算法（GA）和顺序二次编程（SQP）进行优化的，称为 MWANNs-GA-SQP。本文的研究结果表明，为了以最低的成本和复杂性、安全性、重点医疗护理和外部消毒方法的适用性最大限度地减少疾病传播总量。所提供的数据通过各种图形模拟进行了验证，这无疑证明了所建议的求解器的有效性和鲁棒性。建议的求解器 MWANNs-GA-SQP 在各种情况下进行测试，以检验其可靠性、安全性和容错性。利用提出的 MWANNs hubristic 智能方法，在前馈神经网络中创建了一个目标优化函数，以最小化均方误差。通过对混合 GA-SQP 的研究，确认了 MWANNs 模型结果的准确性和可靠性。构建了平均绝对图，以评估所建议方法的完整性和效率。所建议方法的准确性和可靠性体现在对大量不同试验计算的分析评估标准变量不断达到最大值。

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引用次数: 0