Computational Biology and Chemistry最新文献

Screening and computational characterization of novel antimicrobial cathelicidins from amphibian transcriptomic data 从两栖动物转录组数据中筛选新型抗菌素并计算其特征。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-13 DOI: 10.1016/j.compbiolchem.2024.108276

H. Varela-Rodríguez, A. Guzman-Pando, J. Camarillo-Cisneros

As cold-blooded organisms living in damp and dark environments, amphibians have evolved robust defense mechanisms to protect themselves from predators and infections. Among the wide repertoire of bioactive compounds they produce are antimicrobial peptides (AMPs), which are required as part of innate immunity. One important class of AMPs is cathelicidins, known for their broad-spectrum activity against pathogens and their immunoregulatory roles. However, despite their promising biomedical potential and the increasing availability of omics data, few cathelicidins have been studied in amphibians, mostly through conventional experimental techniques. Here, we present 210 novel cathelicidin sequences from amphibian transcriptomes, identified through a comprehensive computational pipeline, which employed HMMER and BLAST tools to screen cathelicidin domains. These sequences reveal a typical tripartite domain architecture that was confirmed by SignalP and InterProScan analysis. Phylogenetic inference with IQ-TREE classified the sequences into six categories based on evolutionary relationships. Compared to cathelicidins from other vertebrates, amphibian mature peptides exhibit longer average lengths (around 50 amino acids), fewer aromatic and hydrophobic residues, and reduced thermal stability. Furthermore, these amphibian cathelicidins were characterized for their physicochemical and biological properties, revealing significant antimicrobial potential with lower hemolytic capability, especially in anurans, which suggests a balance between their antimicrobial and hemolytic activities predicted through AMPlify, ampir, AmpGram, and HemoPI. Secondary structure estimations, including three-dimensional modeling using AlphaFold2, indicate that amphibian cathelicidins predominantly feature

α

-helices and coils. Some representative models also display a high

α

-helix composition with amphipathic topology, facilitating interactions with simulated bacterial membranes as assessed by the PPM approach. Thus, these findings highlight the functional role of cathelicidins in amphibian immunity and their promising biomedical applicability, emphasizing the importance of applying computational methods to expand the scope and reveal the diverse landscape of cathelicidins across vertebrates.

作为生活在潮湿和黑暗环境中的冷血生物，两栖动物进化出了强大的防御机制来保护自己免受捕食者和感染的侵害。在两栖动物产生的大量生物活性化合物中，抗菌肽（AMPs）是先天免疫所必需的。其中一类重要的 AMPs 是柔毛鞘氨醇，因其对病原体的广谱活性和免疫调节作用而闻名。然而，尽管它们具有广阔的生物医学潜力，而且全局数据的可用性也在不断提高，但对两栖动物中猫肝素的研究却寥寥无几，大多数研究都是通过传统的实验技术进行的。在这里，我们从两栖动物的转录组中发现了210个新的柔毛素序列，这些序列是通过综合计算管道确定的，计算管道采用了HMMER和BLAST工具来筛选柔毛素结构域。这些序列揭示了典型的三方结构域结构，并通过 SignalP 和 InterProScan 分析得到了证实。利用 IQ-TREE 进行的系统发育推断根据进化关系将这些序列分为六类。与其他脊椎动物的柔毛球蛋白相比，两栖动物的成熟肽平均长度较长（约 50 个氨基酸），芳香族和疏水残基较少，热稳定性较低。此外，对这些两栖动物柔毛鞘氨醇的理化和生物学特性进行了表征，发现它们具有显著的抗菌潜力，但溶血能力较低，尤其是在无尾目动物中，这表明通过 AMPlify、ampir、AmpGram 和 HemoPI 预测的抗菌和溶血活性之间存在平衡。二级结构估计（包括使用 AlphaFold2 进行的三维建模）表明，两栖动物的柔毛素主要以 α-螺旋和线圈为特征。一些具有代表性的模型还显示出具有两性拓扑结构的高α-螺旋组成，这有利于通过PPM方法评估与模拟细菌膜的相互作用。因此，这些发现突显了柔毛球蛋白在两栖动物免疫中的功能性作用及其良好的生物医学应用前景，强调了应用计算方法扩大范围和揭示脊椎动物柔毛球蛋白多样性景观的重要性。

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引用次数: 0

Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy 以药理为指导，在硅内发现用于癌症靶向治疗的 SIRT1 抑制剂。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-09 DOI: 10.1016/j.compbiolchem.2024.108275

Deepak Sharma, Rajiniraja Muniyan

Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1‐7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be −44.68 kcal/mol much better than the reference complex i.e., −29.38 kcal/mol.

表观遗传修饰因子 Sirtuin（SIRTs）是一个由七种同工酶（SIRT1-7）和依赖于烟酰胺腺嘌呤二核苷酸（NAD+）的第三类组蛋白去乙酰化酶（HDACs）蛋白组成的家族。SIRT1 与 p53 蛋白结合，可通过去乙酰化过程调控葡萄糖代谢、脂质代谢、线粒体生物生成、DNA 修复、氧化应激、细胞凋亡和炎症等关键细胞过程。当 SIRT1 对 p53 进行去乙酰化作用时，它就会失去抑制肿瘤的特性。通过抑制 SIRT1 蛋白，促进细胞凋亡，减少细胞增殖，最终提高 p53 的乙酰化水平，恢复其抑肿瘤功能。虽然我们已经有了很多 SIRT1 蛋白抑制剂，但它们在临床试验阶段表现出脱靶效应和低效性。本研究旨在找出更多潜在有效且可靠的 SIRT1 抑制剂，使其性能优于现有选择。为此，研究人员对化合物库进行了基于药效学的筛选，然后进行了虚拟筛选、药代动力学、药物相似性和毒性研究，最终得到了 42 个化合物供进一步评估。随后，通过详尽的分子对接和分子动力学模拟，预测出 4 个潜在化合物比参考化合物更能抑制 SIRT1 蛋白。通过主成分分析、自由能分布和结合自由能估算等进一步研究，Hit4（PubChem ID：55753455）被认为是一种新型且有效的 SIRT1 小分子抑制剂。研究发现，Hit4 的总结合自由能为 -44.68 kcal/mol，远高于参照复合物的 -29.38 kcal/mol。

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引用次数: 0

A multi-layer neural network approach for the stability analysis of the Hepatitis B model 用于乙型肝炎模型稳定性分析的多层神经网络方法。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-08 DOI: 10.1016/j.compbiolchem.2024.108256

Muhammad Farhan , Zhi Ling , Zahir Shah , Saeed Islam , Mansoor H. Alshehri , Elisabeta Antonescu

In the present study, we explore the dynamics of Hepatitis B virus infection, a significant global health issue, through a newly developed dynamics system. This model is distinguished by its inclusion of asymptomatic carriers and the impact of vaccination and treatment strategies. Compared to Hepatitis A, Hepatitis B poses a more serious health risk, with some cases progressing from acute to chronic. To diagnose and predict disease recurrence, the basic reproduction number (

R_{0}

) is calculated. We investigate the stability of the disease’s dynamics under different conditions, using the Lyapunov function to confirm our model’s global stability. Our findings highlight the relevance of vaccination and early treatment in reducing Hepatitis B virus spread, making them a useful tool for public health efforts aiming at eradicating Hepatitis B virus. In our research, we investigate the dynamics of a specific model that is characterized by a system of differential equations. This work uses deep neural networks (DNNs) technique to improve model accuracy, proving the use of DNNs in epidemiological modeling. Additionally, we want to find the curves that suit the target solutions with the minimum residual errors. The simulations we conducted demonstrate our methodology’s capability to accurately predict the behavior of systems across various conditions. We rigorously test the solutions obtained via the DNNs by comparing them to benchmark solutions and undergoing stages of testing, validation, and training. To determine the accuracy and reliability of our approach, we perform a series of analyses, including convergence studies, error distribution evaluations, regression analyses, and detailed curve fitting for each equation.

在本研究中，我们通过一个新开发的动力学系统，探讨了乙型肝炎病毒感染这一重大全球健康问题的动态变化。该模型的独特之处在于纳入了无症状携带者以及疫苗接种和治疗策略的影响。与甲型肝炎相比，乙型肝炎对健康的危害更为严重，有些病例会从急性发展为慢性。为了诊断和预测疾病复发，需要计算基本繁殖数（R0）。我们利用 Lyapunov 函数研究了不同条件下疾病动力学的稳定性，以确认我们模型的全局稳定性。我们的研究结果凸显了疫苗接种和早期治疗在减少乙肝病毒传播方面的重要性，使其成为旨在根除乙肝病毒的公共卫生工作的有用工具。在我们的研究中，我们研究了一个以微分方程系统为特征的特定模型的动力学。这项工作利用深度神经网络（DNN）技术提高了模型的准确性，证明了 DNN 在流行病学建模中的应用。此外，我们希望以最小的残余误差找到适合目标解决方案的曲线。我们进行的模拟证明了我们的方法能够准确预测各种条件下的系统行为。我们将 DNN 获得的解决方案与基准解决方案进行比较，并经历测试、验证和训练等阶段，从而对其进行严格测试。为了确定我们方法的准确性和可靠性，我们进行了一系列分析，包括收敛性研究、误差分布评估、回归分析以及每个方程的详细曲线拟合。

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引用次数: 0

Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: An insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology 揭示肺癌亚型中 TP53 突变引发的多组学独特变化：洞察瘤内微生物群、肿瘤微环境和病理学之间的相互作用。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-07 DOI: 10.1016/j.compbiolchem.2024.108274

Shanhe Tong , Kenan Huang , Weipeng Xing , Yuwen Chu , Chuanqi Nie , Lei Ji , Wenyan Wang , Geng Tian , Bing Wang , Jialiang Yang

The TP53 mutation is one of the most common gene mutations in non-small cell lung cancer (NSCLC) and plays a significant role in the occurrence, development, and prognosis of both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Recent studies have also suggested the predictive value of TP53 mutations in the response to immunotherapy for NSCLC. It is known that intratumoral microbiota, tumor immune microenvironment (TIME) and histology are associated with the roles of TP53 mutation in NSCLC. However, the intrinsic associations among these three factors and their underlying interaction with TP53 mutation are not well understood. Additionally, the potential of predicting TP53 mutations using deep learning methods has not yet been fully evaluated. In this paper, we comprehensively evaluated the tissue microbiome, host gene expression characteristics, and histopathological slides of 992 NSCLC patients obtained from the cancer genome atlas (TCGA) and validated the findings using multi-omics data of 332 NSCLC patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Compared to LUSC, LUAD exhibited more substantial differences between patients with and without TP53 mutation in all three aspects. In LUAD, our results imply underlying links between the tissue microbiome and immune cell components in the TIME, and show that the abundance of immune cells is reflected in histology slides. Furthermore, we propose a novel multimodal deep learning model that focuses on histopathology images, which achieves an area under the curve (AUC) of 0.84 in LUAD. In summary, TP53 mutation of LUAD resulted more significant changes in intratumoral microbiota, TIME and histology than that of LUSC. And histopathology images can be used to predict TP53 mutation in LUAD with reasonable accuracy.

TP53 突变是非小细胞肺癌（NSCLC）中最常见的基因突变之一，在肺腺癌（LUAD）和肺鳞癌（LUSC）的发生、发展和预后中起着重要作用。最近的研究还表明，TP53 基因突变对 NSCLC 免疫疗法的反应具有预测价值。众所周知，瘤内微生物群、肿瘤免疫微环境（TIME）和组织学与 TP53 突变在 NSCLC 中的作用有关。然而，这三个因素之间的内在联系及其与 TP53 基因突变之间的潜在相互作用还不十分清楚。此外，利用深度学习方法预测 TP53 突变的潜力尚未得到充分评估。在本文中，我们全面评估了从癌症基因组图谱（TCGA）中获得的992名NSCLC患者的组织微生物组、宿主基因表达特征和组织病理学切片，并利用临床蛋白质组肿瘤分析联盟（CPTAC）中332名NSCLC患者的多组学数据验证了这些发现。与LUSC相比，有TP53基因突变和没有TP53基因突变的LUAD患者在所有三个方面都表现出更大的差异。在LUAD中，我们的研究结果表明组织微生物组与TIME中的免疫细胞成分之间存在潜在联系，并显示免疫细胞的丰度反映在组织学切片中。此外，我们还提出了一种新型多模态深度学习模型，该模型侧重于组织病理学图像，在 LUAD 中的曲线下面积（AUC）达到了 0.84。总之，TP53突变导致LUAD的瘤内微生物群、TIME和组织学发生了比LUSC更显著的变化。组织病理学图像可用于预测LUAD的TP53突变，准确性较高。

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引用次数: 0

Autoencoder-based drug synergy framework for malignant diseases 基于自动编码器的恶性疾病药物协同作用框架。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-06 DOI: 10.1016/j.compbiolchem.2024.108273

Pooja Rani , Kamlesh Dutta , Vijay Kumar

Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O’Neil datasets. Autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.

联合用药已成为治疗恶性疾病的可行方案。通过提高疗效、降低毒性和克服耐药性，联合用药优于单一疗法。由于存在巨大的组合空间，要找到可行的药物组合很难通过经验进行追踪。机器学习和深度学习方法可用于在巨大的组合空间中发现新型协同药物组合。在此，我们提出了一种基于自动编码器的新型药物协同框架 AESyn，该框架采用词袋编码技术，适用于恶性疾病。词袋编码技术用于提取药物靶向基因。该框架利用了 NCI-ALMANAC 和 O'Neil 数据集的筛选数据。自动编码器将带有药物靶向基因的药物嵌入作为输入进行处理。拟议框架中的自动编码器用于提取药物特征。拟议框架在分类和回归指标上进行了评估。将拟议框架的性能与现有的药物协同方法进行了比较。结果表明，拟议框架的准确率高达 95%，AUROC 为 94.2%，MAPE 为 7.2。基于自动编码器的恶性疾病框架使用编码技术提供了一种稳定的、与阶次无关的药物协同作用预测方法。

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引用次数: 0

HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES HiMolformer：整合图形和序列表示法，利用 SMILES 预测肝脏微粒体的稳定性。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-05 DOI: 10.1016/j.compbiolchem.2024.108263

Seokwoo Yun , Gibeom Nam , Jahwan Koo

In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic stability of new molecules is crucial. Recently, research on pre-trained deep learning for molecular property prediction has been actively progressing, with various models being made open-source. However, most of these models rely on either 2D graph or 1D sequence for training, and the representation varies depending on the data format used. Consequently, combining multiple representations can broaden the scope of learning and may potentially be a manageable and most effective method to enhance performance.

Therefore, we propose a novel hybrid model for predicting metabolic stability, which integrates representations from both graph-based and sequence-based models pre-trained for molecular features. This approach utilizes the combined strengths of 2D topological and 1D sequential information of molecules. HiMol, a graph-based graph neural network (GNN) model, and Molformer, a sequence-based Transformer model, were selected for integration, thus we named it HiMolformer. HiMolformer demonstrated superior performance compared to other models. We also focus on regression task for prediction with a empirical dataset from Korea Chemical Bank (KCB), comprising 3,498 molecules with mouse liver microsome (MLM) and human liver microsome (HLM) data obtained from actual metabolic reaction experiments. To the best of our knowledge, it is the first attempt to develop MLM and HLM prediction models using regression with a single SMILES input. The source code of this model is available at https://github.com/YUNSEOKWOO/HiMolformer.

在药物发现或临床前研究的初始阶段，了解新分子的代谢稳定性至关重要。最近，用于分子性质预测的预训练深度学习研究取得了积极进展，各种模型已被开源。然而，这些模型大多依赖于二维图或一维序列进行训练，而且所使用的数据格式不同，表示方法也不尽相同。因此，结合多种表示方法可以拓宽学习范围，并有可能成为一种易于管理且最有效的提高性能的方法。因此，我们提出了一种预测代谢稳定性的新型混合模型，该模型综合了基于图和基于序列的模型的表征，并针对分子特征进行了预先训练。这种方法综合利用了分子的二维拓扑信息和一维序列信息。我们选择了基于图的图神经网络（GNN）模型 HiMol 和基于序列的 Transformer 模型 Molformer 进行整合，因此将其命名为 HiMolformer。与其他模型相比，HiMolformer 表现出了卓越的性能。我们还重点利用韩国化学库（KCB）的经验数据集进行回归预测，该数据集包括从实际代谢反应实验中获得的小鼠肝脏微粒体（MLM）和人类肝脏微粒体（HLM）数据，共 3498 个分子。据我们所知，这是首次尝试利用单一 SMILES 输入使用回归法开发 MLM 和 HLM 预测模型。该模型的源代码见 https://github.com/YUNSEOKWOO/HiMolformer。

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引用次数: 0

Genome-wide identification of alternative splicing related with transcription factors and splicing regulators in breast cancer stem cells responding to fasting-mimicking diet 全基因组范围内鉴定乳腺癌干细胞对禁食模拟饮食反应中与转录因子和剪接调节因子相关的替代剪接

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-11-02 DOI: 10.1016/j.compbiolchem.2024.108272

Hongshuang Qin, Qian Zhang, Yanxiang Guo

Fasting-mimicking diet (FMD) can effectively inhibit the viability of breast cancer stem cells (CSCs). However, the molecular mechanisms underlying the inhibitory function of FMD on breast CSCs remain largely unknown. Elucidating the mechanisms by which FMD suppresses breast CSCs is beneficial to targeting breast CSCs. Herein, we systematically analyze alternative splicing and RNA binding protein (RBP) expression in breast CSCs during FMD. The analysis results show that a large number of regulated alternative splicing (RAS) and differentially expressed genes (DEGs) appear responding to FMD. Further studies show that there are potential regulatory relationships between transcription factors (TFs) with RAS (RAS-TFs) and their differentially expressed target genes (RAS-TF-DEGs). Moreover, differentially expressed RNA binding proteins (DERBPs) exhibit potential regulatory functions on RAS-TFs. In short, DERBPs potentially control the alternative splicing of TFs (RAS-TFs), regulating their target gene (RAS-TF-DEG) expression, which leads to the regulation of biological processes in breast CSCs during FMD. In addition, the alternative splicing and DEGs are compared between breast CSCs and differentiated cancer cells during FMD, providing new interpretations for the different responses of the two types of cells. Our studies will shed light on the understanding of the molecular mechanisms underlying breast CSC inhibition induced by FMD.

模拟空腹饮食（FMD）能有效抑制乳腺癌干细胞（CSCs）的活力。然而，FMD抑制乳腺癌干细胞的分子机制仍不为人知。阐明FMD抑制乳腺癌干细胞的机制有利于靶向治疗乳腺癌干细胞。在此，我们系统分析了FMD过程中乳腺CSCs的替代剪接和RNA结合蛋白（RBP）表达。分析结果显示，大量受调控的替代剪接（RAS）和差异表达基因（DEGs）出现了对FMD的响应。进一步的研究表明，带有 RAS 的转录因子（TFs）（RAS-TFs）与其差异表达的靶基因（RAS-TF-DEGs）之间存在潜在的调控关系。此外，差异表达的 RNA 结合蛋白（DERBPs）对 RAS-TFs 具有潜在的调控功能。简而言之，DERBPs 有可能控制 TFs（RAS-TFs）的替代剪接，调节其靶基因（RAS-TF-DEGs）的表达，从而调控 FMD 期间乳腺 CSCs 的生物学过程。此外，我们还比较了乳腺癌 CSCs 和分化癌细胞在 FMD 期间的替代剪接和 DEGs，为这两类细胞的不同反应提供了新的解释。我们的研究将有助于了解 FMD 诱导乳腺癌 CSC 抑制的分子机制。

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引用次数: 0

Design and characterization of defined alpha-helix mini-proteins with intrinsic cell permeability 设计并鉴定具有内在细胞渗透性的定义α-螺旋小蛋白。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-31 DOI: 10.1016/j.compbiolchem.2024.108271

Xin-Chun Chen , Xiang-Wei Kong , Pin Chen , Zi-Qian Li , Nan Huang , Zheng Zhao , Jie Yang , Ge-Xin Zhao , Qing Mo , Yu-Tong Lu , Xiao-Ming Huang , Guo-Kai Feng , Mu-Sheng Zeng

Proteins with intrinsic cell permeability that can access intracellular targets represent a promising strategy for novel drug development; however, a general design principle is still lacking. Here, we established a library of 46,678 de novo-designed mini-proteins and performed cell permeability screening via phage display. Analyses revealed a characteristic neighboring distribution of positive charges across helices among enriched mini-proteins of CPP7, CPP11, CPP55, CPP109 and CPP112. Compared with the state-of-the-art cell-penetrating mini-protein ZF5.3, the optimized mini-protein CPP11D36R exhibited a sevenfold increase in cell permeability. Endocytosis uptake and early endosome release are the key penetrating mechanisms. A machine learning model with high-throughput data achieved an F1 score of 0.41, significantly outperforming the previously reported CPP prediction models, including MLACP, CPPpred and CellPPD, by 41 %. Overall, our findings validate the effectiveness of a helical structure with a cationic distribution as a design principle on a large scale and present a robust approach for the development of cell-permeable mini-protein drugs.

具有内在细胞渗透性并能进入细胞内靶点的蛋白质是一种很有前景的新型药物开发策略；然而，目前仍缺乏一种通用的设计原则。在这里，我们建立了一个包含 46,678 个全新设计的迷你蛋白质库，并通过噬菌体展示进行了细胞渗透性筛选。分析表明，在 CPP7、CPP11、CPP55、CPP109 和 CPP112 的富集迷你蛋白中，正电荷在各螺旋之间呈邻近分布。与最先进的细胞穿透小蛋白 ZF5.3 相比，优化后的小蛋白 CPP11D36R 的细胞渗透性提高了七倍。内吞摄取和早期内质体释放是关键的穿透机制。利用高通量数据建立的机器学习模型的 F1 得分为 0.41，比之前报道的 CPP 预测模型（包括 MLACP、CPPpred 和 CellPPD）高出 41%。总之，我们的研究结果验证了具有阳离子分布的螺旋结构作为大规模设计原则的有效性，并为开发细胞渗透性微型蛋白药物提供了一种稳健的方法。

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引用次数: 0

Identify the key genes and pathways of melatonin in age-dependent mice hippocampus regulation by transcriptome analysis 通过转录组分析确定褪黑激素在年龄依赖性小鼠海马调控中的关键基因和通路

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-30 DOI: 10.1016/j.compbiolchem.2024.108267

Yujia Gu , Jiayu Zhou , Qingchun Zhao , Xiaowen Jiang , Huiyuan Gao

Context

Dysregulation of energy metabolism is a fundamental contributor to all the hallmarks of brain aging. Melatonin, primarily secreted by the pineal gland, is closely associated with molecules and signaling pathways that sense and affect energy metabolism. However, the impact of melatonin on age-related mRNA expression in the hippocampus of mice at different ages remains poorly understood.

Objective

The present study conducted transcriptome analysis of the hippocampus in melatonin-exposed mice at 9, 13, and 25 months of age. Differential gene analysis, GO and KEGG pathway enrichment analysis, GSEA analysis, as well as weighted gene co-expression network analysis (WGCNA), were performed on the transcriptome data.

Results

Our study demonstrated that melatonin exerts a more pronounced regulatory effect on the transcriptome of 25-month old mice, and significantly enhances the expression level of TTR in the hippocampus of 13-month old mice. WGCNA analysis revealed that melatonin primarily modulates the energy metabolism of mouse hippocampus through the mTOR signaling pathway and AMPK signaling pathway.

Conclusions

In conclusion, our study provides new insights into the comprehensive understanding of the mechanism of melatonin's age-dependent regulation of the mice hippocampus.

背景能量代谢失调是导致大脑衰老的根本原因。褪黑激素主要由松果体分泌，与感知和影响能量代谢的分子和信号通路密切相关。然而，褪黑激素对不同年龄小鼠海马中与年龄相关的 mRNA 表达的影响仍然知之甚少。结果我们的研究表明，褪黑激素对25月龄小鼠的转录组具有更明显的调控作用，并显著提高了13月龄小鼠海马中TTR的表达水平。结论总之，我们的研究为全面了解褪黑激素对小鼠海马的年龄依赖性调控机制提供了新的见解。

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引用次数: 0

Deconvolution of cell-type-associated markers predictive of response to neoadjuvant radiotherapy 预测新辅助放疗反应的细胞类型相关标记的解卷积。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-29 DOI: 10.1016/j.compbiolchem.2024.108269

Min Zhu , Xiao Sun , Jinman Fang , Xueling Li

Tumor microenvironent contains prognostic molecular markers and therapeutic targets from different cellular sources, which are still not fully revealed in the resistance and recurrence after radiotherapy for rectal cancer. By integrating the scRNA-seq data, we deconvoluted the bulk transcriptomics of rectal cancer collected before preoperative neoadjuvant radiotherapy (nRT) into fractions and gene expression of the six cell types. The inferred cell-type-associated DEGs, abbreviated as caDEGs, of myeloid and stromal cells were enriched for overlapping yet unique biological processes including immunity, angiogenesis, and metabolism, respectively. Ecotyper analysis indicates that the caDEGs reflects cell states and ecotypes in association with nRT response. By mapping the caDEGs onto the context-free and newly built ligand-receptor and collagen-integrin lists from scRNA-Seq data, respectively, we inferred 297 cell-type-specific trans- and/or cis-collagen-integrin and 219 heterotypic ligand-receptor interactions potentially associated with nRT response, including interactions between stromal-associated COL1A2/COL6A1/COL6A2 and stromal or CMS1-associated ITGA1/B1, between epithelial-associated JAG1 and stromal-associated NOTCHs, between CMS2 epithelial-associated CCL15 and proliferating myeloid-associated CCR1, between myeloid-associated CCL4/CD86 and lymphatic endothelial-associated ACKR2, and between myeloid-associated TNFS13B and B cell-associated TNFRSF13B/C, etc. Intriguingly, results suggest a greater number of down-regulated cell-type-related markers in resistant cancers to nRT. Favorable myeloid-associated CD14, epithelial-associated DYM, stromal-associated COL1A2 and COL3A1, and unfavorable epithelial-associated CELSR3 and KCNH8 markers were inferred at least from two independent nCRT datasets of GSE119409, GSE35452, and GSE45404. The results provide insights into roles of the stromal and immune cells beside epithelial cells in resistance to radiotherapy for rectal cancers. The proposed approach can be applicable to other diseases as well. Codes and additional data are available at https://github.com/Xueling21/rectalNRT_deconv.

肿瘤微环境包含不同细胞来源的预后分子标记物和治疗靶点，这些标记物和靶点在直肠癌放疗后的耐药性和复发中仍未得到充分揭示。通过整合scRNA-seq数据，我们将术前新辅助放疗（nRT）前采集的直肠癌大体转录组学数据分解为六个细胞类型的组分和基因表达。推断出的髓细胞和基质细胞的细胞类型相关 DEGs（缩写为 caDEGs）富集于重叠但独特的生物过程，包括免疫、血管生成和新陈代谢。生态型分析表明，caDEGs 反映了与 nRT 反应相关的细胞状态和生态型。通过将 caDEGs 分别映射到scRNA-Seq数据中的无上下文配体-受体和新构建的配体-受体和胶原整合素列表，我们推断出 297 种细胞类型特异的反式和/或顺式胶原整合素和 219 种异型配体-受体相互作用可能与 nRT 反应有关，包括基质相关的 COL1A2/COL6A1/COL6A2 与基质或 CMS1 相关的 ITGA1/B1 之间的相互作用、上皮相关的 JAG1 与基质相关的 NOTCHs 之间、CMS2 上皮相关的 CCL15 与增殖骨髓相关的 CCR1 之间、骨髓相关的 CCL4/CD86 与淋巴内皮相关的 ACKR2 之间、骨髓相关的 TNFS13B 与 B 细胞相关的 TNFRSF13B/C 之间等。有趣的是，研究结果表明，对 nRT 耐药的癌症中有更多下调的细胞类型相关标记物。至少从两个独立的 nCRT 数据集 GSE119409、GSE35452 和 GSE45404 中推断出了有利的髓相关 CD14、上皮相关 DYM、基质相关 COL1A2 和 COL3A1，以及不利的上皮相关 CELSR3 和 KCNH8 标记。这些结果为了解上皮细胞以外的基质细胞和免疫细胞在直肠癌放疗耐药性中的作用提供了启示。所提出的方法也适用于其他疾病。代码和更多数据可在 https://github.com/Xueling21/rectalNRT_deconv 上获取。

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