Using the theory of coevolution to predict protein-protein interactions in non-small cell lung cancer

Systems biology has become an effective approach for understanding the molecular mechanisms underlying the development of lung cancer.In this study,sequences of 100 non-small cell lung cancer (NSCLC)-related proteins were downloaded from the National Center for Biotechnology Information (NCBI) databases.The Theory of Coevolution was then used to build a protein-protein interaction (PPI) network of NSCLC.Adopting the reverse thinking approach,we analyzed the NSCLC proteins one at a time.Fifteen key proteins were identified and categorized into a special protein family F(K),which included Cyclin D1 (CCND1),E-cadherin (CDH1),Cyclin-dependent kinase inhibitor 2A (CDKN2A),chemokine (C-X-C motif) ligand 12 (CXCL12),epidermal growth factor (EGF),epidermal growth factor receptor (EGFR),TNF receptor superfamily,member 6(FAS),FK506 binding protein 12-rapamycin associated protein 1 (FRAP1),O-6-methylguanine-DNA methyltransferase (MGMT),parkinson protein 2,E3 ubiquitin protein ligase (PARK2),phosphatase and tensin homolog (PTEN),calcium channel voltage-dependent alpha 2/delta subunit 2 (CACNA2D2),tubulin beta class I (TUBB),SWI/SNF-related,matrix-associated,actin-dependent regulator of chromatin,subfamily a,member 2 (SMARCA2),and wingless-type MMTV integration site family,member 7A (WNT7A).Seven key nodes of the sub-network were identified,which included PARK2,WNT7A,SMARCA2,FRAP1,CDKN2A,CCND1,and EGFR.The PPI predictions of EGFR-EGF,PARK2-FAS,PTEN-FAS,and CACNA2D2-CDH1 were confirmed experimentally by retrieving the Biological General Repository for Interaction Datasets (BioGRID) and PubMed databases.We proposed that the 7 proteins could serve as potential diagnostic molecular markers for NSCLC.In accordance with the developmental mode of lung cancer established by Sekine et al.,we assumed that the occurrence and development of lung cancer were linked not only to gene loss in the 3p region (WNT7A,3p25) and genetic mutations in the 9p region but also to similar events in the regions of 1p36.2 (FRAP1),6q25.2-q27 (PARK2),and 11q13 (CCND1).Lastly,the invasion or metastasis of lung cancer happened.

Prediction of Protein-Protein Interactions by a Novel Model Based on Domain Information

Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.

A hybrid method for extraction of protein-protein interactions from literature

In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.

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。

货币供给与PPI的动态响应机制和结构性差异

PPI对经济走势具有较好的预见性,货币政策应更多地关注PPI。本文构建了含交互效应的面板SVAR模型,基于我国工业34个行业,实证分析货币供给对各行业PPI影响的结构性差异,以及PPI对各种主要冲击因素的动态响应机制。研究发现:货币供给冲击对各工业行业PPI波动的影响差异很大,贡献度从0.02%～74%不等。其中,重工业以及产业链上游行业PPI对货币供给冲击最为敏感。在给定货币环境下,PPI波动的主要支配力量是通货膨胀惯性,其贡献度高达70%;而固定资产投资的冲击效应则很弱,仅为2%。所以,将PPI价格波动主要归因于大规模固定资产投资是缺乏依据的。而货币政策的调控应该基于PPI响应机制的结构性差异,着重于价格波动预期的管理。

RNA sequencing of exosomes secreted by fibroblast and Schwann cells elucidates mechanisms underlying peripheral nerve regeneration

Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.

快速的基于蚁群聚类的PPI网络功能模块检测方法

针对蚁群聚类在蛋白质相互作用(protein-protein interaction,PPI)网络中进行功能模块检测问题上时间性能的不足,提出一种快速的基于蚁群聚类的PPI网络功能模块检测(fast ant colony clustering for functional module detection,FACC-FMD)方法.该算法计算每个蛋白质与核心组蛋白质的相似度,根据拾起放下模型进行聚类,得到的初始聚类结果中功能模块之间相似度很小,省去了原始蚁群聚类算法中的合并和过滤操作,缩短了求解时间.同时该算法根据蛋白质的关键性对蚁群聚类中的拾起放下操作做了更严格的约束,以减少拾起放下的次数,加速了聚类的过程.在多个PPI网络上的实验表明:与原始蚁群聚类方法相比,FACC-FMD大幅度提高了时间性能,同时取得了良好的检测质量,而且与近年来的一些经典算法相比在多项性能指标上也具有一定的优势.

面向PCP-MS数据的PPI网络推断算法

随着蛋白质组学的发展,研究者们开始聚焦于人类的全部蛋白质相互作用(Protein-Protein Interaction,PPI)网络的建立,质谱分析技术已成为预测蛋白质相互作用的代表方法。质谱技术是构建蛋白质相互作用网络的主要实验手段之一,基于质谱技术产生了大量的蛋白质纯化数据,如AP-MS数据和PCP-MS数据等。这些数据为PPI网络的构建提供了重要的数据支持,但是通过人工的手段来构建PPI网络不仅低效,而且很不现实。因此,面向PCP-MS数据的网络推断算法是生物信息学研究的一个热点问题。文中针对一类主流的质谱(PCP-MS)数据的PPI网络构建算法问题开展研究,从解决目前存在的瓶颈问题出发,达到构建高质量PPI网络的目的。现有的面向PCP-MS数据的PPI网络推断算法的研究还处于初级阶段,相关方法较少。同时,算法结果的质量还存在着一些问题:1)很多错误的相互作用被包含在不同的推断算法结果中,同时一些正确的相互作用在结果中被遗漏;2)不同的推断算法在同一数据集上的表现差异较大;3)对于不同的数据集,同一算法表现性能的波动方差较大。因此,为了从PCP-MS数据中推断出结构可靠、质量较高的PPI网络,文中提出一种基于相关性分析与排序整合的PPI评分方法。该方法基于无监督学习,包括以下两个步骤:1)计算蛋白质之间的相关系数,得到多组相关性结果;2)采用排序整合的方法对多组结果进行整合,得到整合后的PPI分数。实验结果表明,所提方法在不使用参考标准的情况下,可以达到与有监督学习方法接近的结果。

基于文化算法的PPI网络功能模块检测方法

为了解决蛋白质相互作用(protein-protein interaction,PPI)网络功能模块检测问题,提出一种基于文化算法的PPI网络功能模块检测(CA-FMD)方法.首先,每个个体采用基于节点邻居有序表的编码方式表示功能模块检测问题的一个可行解.然后,利用文化算法的双层进化机制获得最优解,其中,上层机制用来模拟信念空间中群体经验的进化,下层机制用来刻画种群空间中个体的进化.最后,借助2个空间的相互作用和影响完成解的优化.在3个数据集上的实验结果表明:与其他算法相比,CA-FMD方法在多项评价指标上都具有明显的优势.

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.

核酸条形码技术:扩展蛋白质-蛋白质相互作用检测通量的新方法

蛋白质-蛋白质相互作用(protein-protein interaction,PPI)几乎参与了机体内所有重要的生物学过程,在细胞的基本生命过程中扮演了至关重要的角色,开发高通量的PPI检测新方法具有重要的生物学意义。目前,下一代测序技术(next-generation sequencing,NGS)发展快速,能在几天内测定超过10亿个模板的DNA序列。由于并行DNA测序技术所特有的敏感性、特异性、高通量和多路复用优势,其已被用作广谱分子计数器,应用于基因组测序和转录物组测序等领域。核酸条形码技术通过将寡核苷酸标签与目标蛋白质连接起来,从而标记编码蛋白质。之后,利用高通量的测序方法检测相互作用的蛋白质,实现了PPI的高通量检测。这一技术推动了PPI检测方法的飞速发展,提升了单次实验检测的通量,为构建PPI网络提供了强有力的技术支持。本文详细阐述了核酸条形码在PPI检测方法中的设计、生成和读取;通过分析核酸条形码技术在PPI研究中的应用范例,探讨了各自的优势和不足,并评估了数据的可靠性,讨论了基于核酸条形码技术的PPI检测方法未来的发展趋势。

基于PPI网络与机器学习的蛋白质功能预测方法

针对现有的基于蛋白质相互作用(PPI)网络的蛋白质功能预测方法预测精度不高、易受数据噪声影响的问题,提出一种基于机器学习(层次聚类、主成分分析和多层感知器)的蛋白质功能预测方法 HPMM。该方法综合考虑蛋白质宏观和微观层面的信息,将蛋白质家族、结构域和重要位点信息作为顶点属性整合到PPI网络中以减轻网络中数据噪声的影响。首先,基于层次聚类和主成分分析进行特征提取,得到功能模块和属性主成分特征,然后训练多层感知器模型,建立多特征与多功能之间的映射关系以用于功能预测。在三个分别被分子功能(MF)、生物过程(BP)和细胞组件(CC)注释的人类PPI网络上进行测试,对HPMM、余弦迭代算法(CIA)和有向PPI网络基因本体术语传播(GoDIN)算法的功能预测效果进行比较分析。实验结果表明,相比CIA和GoDIN这两种完全基于PPI网络的方法,HPMM的精确度与F值更高。

PPI网络聚类的评价方法的研究与应用

蛋白质相互作用网络(Protein-Protein Interaction,PPI)聚类结果的评价方法的研究是检测PPI网络功能模块聚类结果正确与否的关键。介绍并分析了4种有代表性的PPI网络聚类的评价方法,即p-value、匹配统计量、基于准确率和查全率的综合评价以及基于层结构的hF-measure,在此基础上考虑了主错误划分类与该预测类的相似性,提出了新的罚分函数和新的Sf-measure评价方法。仿真结果表明了各评价方法的特点及Sf-measure评价方法的有效性及合理性。

不确定性PPI网络链接预测

蛋白质交互网络预测是后基因组时代生物学中很重要的研究内容。到目前为止,对蛋白质交互网络相互作用的预测都是假设相互作用是确定的。但是,蛋白质交互网络和其它的一些生物数据会因为实验检测方法的局限性而呈现出不确定性。提出了一种基于信息传播的不确定性PPI网络的链接预测算法。在每个顶点对上按其出现链接的概率定义了链接信息量,该算法将边上的链接信息量在图上以一定的概率来传播。利用标准数据集进行测试,实验结果表明,所提出的算法具有很好的准确率和良好的生物统计特性。

基于不确定PPI网络的功能模块挖掘

近年来,挖掘具有生物学意义的功能模块,吸引了很多人的关注。但是,生物信息学中的蛋白质交互(PPI)网络和其他的一些生物数据常常会由于实验检测方法的局限性而呈现出不确定性。以具有不确定性的PPI数据为研究对象,挖掘蛋白质复合物。引入了一些新概念,并给出了一个深度优先算法。使用MIPS数据库评估实验结果表明,该算法在精确度和覆盖率两个方面性能优良。在基因拓扑上分析实验结果证实了所得到的大多数蛋白质复合物具有很高的相似性。最后也对算法的可扩展性进行了验证。总之,可以有效地从不确定PPI网络中挖掘出功能模块。

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

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.

A novel computational method for protein-protein interaction networks prediction of alpha-synuclein

Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.

Blogel-SPINAL:分布式PPI网络比对算法

高通量技术的发展使蛋白质-蛋白质相互作用(PPI)网络的规模日益增大,需要高速算法对其进行全局比对。为此,分析集中式全局比对算法SPINAL,将该算法中耗时超过95%的计算估计值阶段移植到分布式平台Blogel下运算,求比对图阶段则仍保持集中式运算,以此得到Blogel-SPINAL算法。理论分析和实验结果表明,与SPINAL相比,Blogel-SPINAL能提升比对速度,具有较好的扩展性。

基于局部优化与二分图匹配的PPI网络比对算法

生物蛋白质相互作用网络,简称PPI网络,是一种生物信息学中用来表示蛋白质之间相互作用关系的图模型。不同物种PPI网络之间的比对,有着重要的生物学意义,一个好的PPI网络比对算法,显得尤为重要。针对该问题,首次提出了LOBM(Local Optimization based on Bipartite graph Matching)算法。LOBM是一种能够局部优化既有比对结果,并且利用二分图匹配这一经典图论模型,来提高既有比对算法的比对效果。实验表明,LOBM相比一些现有的比对算法,在比对结果上有较大的提升。