Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells

BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history

Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods.We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs.By defining expression variance (EV),we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database,and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history.We also determined the primary functions of each subnetwork:signal transduction,apoptosis,and cell migration and adhesion for subnetwork A;cell-sustained angiogenesis for subnetwork B;apoptosis for subnetwork C;and,finally,signal transduction and cell replication and proliferation for subnetworks D-G.The probability distribution of the degree of dynamic protein and static protein differed,clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins.There were high correlations among the dynamic proteins,suggesting that the dynamic proteins tend to form specific dynamic modules.We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

Analysis of mechanism on Indigo Naturalis in treating chronic myelocytic leukemia based on two-dimentional model of protein-protein interaction network-moleculardocking technique

To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database （OMIM）, then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes （ proteins） and 2 799 sides （ interactions）. The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node.

Yeast protein-protein interaction network model based on biological experimental data

Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.

Searching maximum quasi-bicliques from protein-protein interaction network

Searching the maximum bicliques or bipartite subgraphs in a graph is a tough question. We proposed a new and efficient method, Searching Quasi-Bicliques (SQB) algorithm, to detect maximum quasi-bicliques from protein-protein interaction network. As a Divide-and-Conquer method, SQB consists of three steps: first, it divides the protein-protein interaction network into a number of Distance-2-Subgraphs;second, by combining top-down and branch-and-bound methods, SQB seeks quasi-bicliques from every Distance-2-Subgraph;third, all the redundant results are removed. We successfully applied our method on the Saccharomyces cerevisiae dataset and obtained 2754 distinct quasi-bicliques.

Protein interaction network related to Helicobacter pylori infection response

AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

Protein-HVGAE:一种双曲空间中的蛋白质编码方法

蛋白质相互作用(PPI)网络中的蛋白质功能预测、蛋白质交互预测和复合物识别是生物信息学的重要任务,非常依赖于对蛋白质的编码。由于PPI网络是由少量中枢节点主导的无标度网络,传统欧氏空间嵌入方法难以捕捉网络中的层次结构,导致蛋白质编码效果并不理想。提出一种基于双曲空间图嵌入的蛋白质自编码器Protein-HVGAE,该模型采用两个双曲图卷积网络作为编码器,计算隐藏层的均值和方差,并在不同曲率的双曲空间中捕捉网络的层次结构,以区分各节点的低维表示;采用Fermi-Dirac函数做解码器,在双曲空间上通过内积运算重构网络。实验结果表明,该模型在3个PPI数据集中的两个下游任务(PPI预测和蛋白质功能预测)上的表现优于以往在欧氏空间中的编码方法(在PPI预测中AUC值高于VGAE模型0.07左右,在蛋白质功能预测中Macro-F1值高于VGAE模型0.02左右)。

酒精性肝炎自噬关键基因的筛选及生物信息学分析

目的筛选酒精性肝炎(AH)的自噬关键基因,探讨AH潜在的生物标志物和治疗靶点。方法采用基因表达综合数据库(GEO)中的2个AH基因芯片和从MSigDB、GeneCards数据库中获得的自噬相关数据集,通过加权基因共表达网络分析(WGCNA)获取关键基因。对筛选的关键基因进行基因本体(GO)、京都基因和基因组百科全书(KEGG)功能富集分析,蛋白质相互作用(PPI)分析,免疫浸润分析,构建信使RNA(mRNA)-微小RNA(miRNA)网络,进行酒精性肝病不同分期的自噬相关关键基因的表达差异分析,并进一步通过实时荧光定量逆转录聚合酶链反应(RT-qPCR)在AH患者和小鼠肝脏组织中验证。结果本研究筛选得到了11个与AH自噬相关的基因(EEF1A2、CFTR、SOX4、TREM2、CTHRC1、HSPB8、TUBB3、PRKAA2、RNASE1、MTCL1、HGF),均为上调基因。在AH患者和小鼠肝脏组织中,SOX4、TREM2、HSPB8、PRKAA2在AH组中的相对表达量均高于对照组。结论SOX4、TREM2、HSPB8、PRKAA2可能是AH潜在的生物标志物和治疗靶点。

Systems understanding of plant-pathogen interactions through genome-wide protein-protein interaction networks

Plants are frequently affected by pathogen infections.To effectively defend against such infections,two major modes of innate immunity have evolved in plants;pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity.Although the molecular components as well as the corresponding pathways involved in these two processes have been identified,many aspects of the molecular mechanisms of the plant immune system remain elusive.Recently,the rapid development of omics techniques(e.g.,genomics,proteomics and transcriptomics) has provided a great opportunity to explore plant–pathogen interactions from a systems perspective and studies on protein–protein interactions(PPIs) between plants and pathogens have been carried out and characterized at the network level.In this review,we introduce experimental and computational identification methods of PPIs,popular PPI network analysis approaches,and existing bioinformatics resources/tools related to PPIs.Then,we focus on reviewing the progress in genome-wide PPI networks related to plant–pathogen interactions,including pathogen-centric PPI networks,plant-centric PPI networks and interspecies PPI networks between plants and pathogens.We anticipate genome-wide PPI network analysis will provide a clearer understanding of plant–pathogen interactions and will offer some new opportunities for crop protection and improvement.

Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network （PRIN; http：//bis.zju.edu.cn/ prin/） presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice （GO annotation, subcellular localizationprediction, and gene expression）, we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box do- main-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.

Structure-based protein-protein interaction networks and drug design

Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

Prediction and systematic study of protein-protein interaction networks of Leptospira interrogans

Leptospira interrogans serovar Lai is a pathogenic bacterium that causes a spirochetal zoonosis in humans and some animals. With its complete genome sequence available, it is possible to analyze protein-protein interactions from a whole- genome standpoint. Here we combine four recently developed computational approaches (gene fusion method, gene neighbor method, phylogenetic profiles method, and operon method) to predict protein-pro- tein interaction networks of Leptospira interrogans strain Lai. Through comprehensive analysis on in- teractions among proteins of motility and chemotaxis system, signal transduction, lipopolysaccaride bio- synthesis and a series of proteins related to adhesion and invasion, we provided information for further studying on its pathogenic mechanism. In addition, we also assigned 203 previously uncharacterized proteins with possible functions based on the known functions of its interacting partners. This work is helpful for further investigating L. interrogans strain Lai.

海马G蛋白偶联雌激素受体1参与癫痫调控的转录组学研究

目的本研究分别将野生型(WT)、Gper1敲低(Gper1-KD)大鼠海马组织进行转录组测序,探讨G蛋白偶联雌激素受体1(G-protein coupled estrogen receptor 1,GPER1)影响癫痫发病可能的信号通路和分子机制。方法海马组织进行RNA提取和cDNA文库构建,与NCBI数据库大鼠基因组及基因注释比对,通过FPKM值,筛选组别间差异表达基因(DEGs)。将DEGs进行GO富集、KEGG富集分析,构建蛋白互作网络,利用RT-qPCR法对关键DEGs进行验证。结果Gper1-KD组与WT组相比,筛选(Fold change>2且FDR<0.01)后,检测到DEGs 2253个,其中上调基因1380个,下调基因873个;GO结果显示,DEGs主要分布在淋巴细胞趋化性、细胞分泌、巨噬细胞趋化性、中性粒细胞趋化性、血管生成正向调控等生物过程;KEGG结果显示,DEGs相关分子信号通路主要有癌症信号通路、PI3K-Akt通路、癌症蛋白聚糖相关信号通路、细胞黏附相关通路、MAPK信号通路等;RT-qPCR结果表明,Mapk12、Pdpk1、Foxo3、Camk2d、Pik3cg等基因表达水平差异具有显著性。结论MAPK、TNF信号通路在GPER1影响癫痫发生中可能起关键作用,GABA能神经元和Pik3cg在GPER1影响癫痫易感性方面可能发挥重要作用。

骨形态发生蛋白/Wnt信号通路调控成骨:揭示骨骼形成和重塑的分子机制

背景:成骨细胞是负责骨骼形成和重塑的主要细胞类型,其功能的正常发挥受到多种信号通路的精密调控,其中,骨形态发生蛋白和Wnt信号通路在成骨过程中起着关键作用。目的:综述了骨形态发生蛋白/Wnt信号通路在调控成骨细胞功能中的作用,并分析其在不同生理和病理条件下的变化,以期进一步揭示骨骼形成和重塑的分子机制。方法:以中文检索词“BMP信号通路,Wnt信号通路,成骨”及英文检索词“BMP signaling pathway,Wnt signaling pathway,osteogenesis”在中国知网、万方和PubMed数据库中检索研究原著,检索时限为各数据库建库至2023年6月的相关文献,最终筛选61篇文献进行分析总结。采用文献综述的方法,对骨形态发生蛋白/Wnt信号通路调控成骨作用的研究进行梳理和分析。结果与结论:①骨形态发生蛋白和Wnt信号通路在成骨细胞的分化、增殖和成熟过程中发挥重要作用。骨形态发生蛋白信号通路主要通过激活Smad蛋白来调控成骨相关基因的表达,Smad蛋白被激活后进入细胞核,调控与成骨相关的基因表达,与骨形态发生蛋白不同,Wnt信号通路主要依赖于β-catenin的激活来发挥生物学效应。②不同生理和病理状态下,骨形态发生蛋白/Wnt信号通路的调控作用会受到多种因素的影响。生长因子及激素及机械应力等会在一定程度上影响骨形态发生蛋白/Wnt信号通路的活性。③骨形态发生蛋白/Wnt信号通路在调控成骨过程中与其他信号通路相互作用,共同构成复杂的调控网络。④中药和天然化合物可以通过调控信号通路来促进骨骼健康,为中药治疗骨骼疾病提供了新的可能性。⑤未来研究可进一步探讨骨形态发生蛋白/Wnt信号通路与其他信号通路的相互作用以及其在不同生理和病理条件下的变化,解析此复杂网络中的关键节点和调控机制,为骨骼相关疾病的治疗提供更精确的靶点,也为揭示骨骼形成和重塑的分子机制提供新的思路。

深度学习在基于序列的蛋白质互作预测中的应用进展

蛋白质-蛋白质相互作用在细胞信号转导、基因表达和代谢调控等生物过程中发挥重要作用,鉴定蛋白质间的相互作用对于理解复杂生物过程至关重要。预测蛋白质间的相互作用可以为药物发现、蛋白质功能研究和设计等领域提供帮助。近年来,随着人工智能技术的蓬勃发展,深度学习技术在预测蛋白质互作领域做出巨大贡献,其中基于序列的深度学习模型通过学习蛋白质序列信息的深层特征进行互作预测。本文综述了深度学习在基于序列的蛋白质互作预测中的应用,按照算法框架和时间线对该领域进展进行分类归纳,介绍了数据处理、序列编码方法、算法架构以及模型的评估指标等内容,并分析了当前面临的问题以及未来的发展方向。随着深度学习技术的发展,预测蛋白质互作的效率大幅提高,未来需要发展泛化能力更强的预测模型,助力蛋白质互作的预测。

原发性骨质疏松潜在生物标志物的生物信息学分析

背景:原发性骨质疏松症的发病率高,但发病机制尚不完全清楚,目前尚缺乏有效的早期筛查指标和治疗方案。目的:通过综合生物信息学分析,进一步探索原发性骨质疏松症的发生机制。方法:原发性骨质疏松症数据来自公共基因表达数据库,筛选差异基因分别进行GO功能和KEGG通路富集分析。此外,将差异基因进行蛋白质-蛋白质相互作用网络确定原发性骨质疏松症相关核心基因,并通过最小绝对收缩和选择运算算法来识别并验证原发性骨质疏松症相关的生物标志物,并分别进行免疫细胞相关分析、基因富集分析以及药物标靶网络分析。最后将生物标志物行qPCR实验验证。结果与结论:①该研究中共获得126个差异基因以及前列腺素、表皮生长因子受体、丝裂原活化蛋白激酶3、转化生长因子B1和视网膜母细胞瘤基因1等5个生物标志物。②GO分析表明差异基因主要富集在细胞对氧化应激的反应以及自噬的调节等方面;KEGG分析显示主要集中在自噬以及细胞衰老等通路当中。③生物标志物的免疫分析发现,前列腺素,视网膜母细胞瘤基因1、丝裂原活化蛋白激酶3与免疫细胞密切相关。④基因富集分析表明,生物标志物与免疫相关途径有关。⑤药物标靶网络分析显示5个生物标志物与原发性骨质疏松症药物相关。⑥qPCR结果表明,前列腺素、表皮生长因子受体、丝裂原活化蛋白激酶3和转化生长因子B1在原发性骨质疏松症样本中,与对照样本相比表达显著上升(P<0.001),而视网膜母细胞瘤基因1在原发性骨质疏松症样本中,与对照样本相比表达显著下降(P<0.001)。⑦总之,该研究筛选并验证了5个原发性骨质疏松潜在生物标志物,为进一步深入探究原发性骨质疏松症的发病机制、早期筛查诊断及靶向治疗提供了参考依据。

基于罗汉果治疗糖尿病肾病机制的网络药理学和分子对接分析

目的:利用网络药理学分析罗汉果对糖尿病肾病(DN)的改善作用,阐明其可能的相关机制。方法:采用中药系统药理学数据库和分析平台(TCMSP)确定罗汉果中的有效成分及其作用靶点。通过DisGeNET数据库和GeneCards数据库筛选DN靶基因。将罗汉果与DN靶点进行对比,获取罗汉果对DN的关键靶点。通过STRING数据库和Cytoscape软件构建蛋白-蛋白互作(PPI)网络图,通过Cytoscape软件进行基因本体论(GO)功能富集分析和京都基因与基因组百科全书(KEGG)信号通路富集分析。采用分子对接技术预测DN核心靶点与罗汉果主要活性成分的结合能力。结果:采用TCMSP数据库结合选入标准共筛选出罗汉果5种活性成分(ZINC03860434、Perlolyrine、beta-sitosterol、Kaempferol和Flazin)及丝氨酸/苏氨酸蛋白激酶1 (AKT1)、转录因子RELA、c-Jun氨基末端激酶(JUN)和肿瘤坏死因子(TNF)为代表的85个靶点,其中kaempferol所含靶点最多。筛选出的85个靶点中与DN相关的靶点有34个。GO功能富集分析主要涉及氧化应激、炎症及凋亡调控和细胞信号传导等生物学过程(BP)。KEGG信号通路富集分析涉及晚期糖基化终产物(AGE)-AGE受体(AGE-RAGE)信号通路、 TNF信号通路和C型凝集素受体信号通路等。罗汉果主要活性成分与DN靶点蛋白分子对接分析,5种活性成分分子对接结合能均在-8.00~-5.00 kJ·mol^(-1)之间。结论:Kaempferol是罗汉果中对DN治疗最有效的活性成分,其作用机制主要与抑制炎症有关。

牛HSPA6蛋白特性分析及蛋白互作网络构建

通过构建牛热休克蛋白A6(heat shock protein A6,HSPA6)序列与其他生物的系统进化树,以及运用生物信息学方法分析牛HSPA6蛋白的基本理化性质、亲疏水性等,并结合蛋白互作网络,探究牛HSPA6基因编码蛋白的结构和功能特性。结果显示,牛HSPA6蛋白与羊、长江江豚等哺乳动物的氨基酸序列相似性较高;牛HSPA6蛋白分子质量为70 570.64 u,理论等电点为5.66,为酸性亲水性蛋白,无跨膜结构和信号肽;可能存在11个得分>0.900的磷酸化位点,与N-糖基化激活位点可能位于后端碱基;牛HSPA6蛋白是一种主要由40.38%的α-螺旋和33.65%的无规卷曲组成的二级结构相对稳定的蛋白质,包含N-端核苷酸结合域和C-端多肽结合域两个主要的结构域,主要在细胞质中发挥作用;蛋白质互作网络构建结果显示,牛HSPA6蛋白主要与BAG1、DNAJA4、DNAJB1、DNAJC2等蛋白发生互作,参与腺苷酸交换因子活性、ATP酶调节活性、伴侣绑定等,表明牛HSPA6蛋白在牛机体能量代谢等过程中发挥潜在生物学功能。这些多重生物信息学分析为深入探讨牛HSPA6蛋白对肉品质的影响机制提供了理论依据。