一类蛋白质相互作用网络比对的线性规划算法

随着越来越多的蛋白质相互作用数据被公布,网络比对在预测蛋白质的新功能和推测蛋白质网络进化历史上发挥着越来越重要的作用。但是,目前主要的网络比对方法要么忽略蛋白质的同源信息或蛋白质网络的结构信息,要么采用启发式算法。文章作者通过将网络比对转化为线性规划问题给出了一个精确的网络比对算法,并且针对水痘病毒和卡波济(氏)肉瘤病毒的蛋白质相互作用数据进行了比对分析。     

蛋白质序列的矩阵图谱表达

基于经典HP模型,利用蛋白质序列的矩阵图谱表达法(MGR)及数值刻画的思想提出了一种新的蛋白质序列的比对方法,通过观察蛋白质序列的数值刻画图及计算两蛋白质序列之间的欧氏距离d,对木聚糖酶两家族的蛋白质序列进行了相似性分析.发现被划分为同一木聚糖酶家族的蛋白质序列之间的相似性更大,而且蛋白质序列的相似性程度与分子大小、结构和分子进化相关.     

构建适用于蛋白质家族分类的相似性网络

图聚类用于蛋白质分类问题可以获得较好结果,其前提是将蛋白质之间复杂的相互关系转化为适当的相似性网络作为图聚类分类的输入数据。本文提出一种基于BLAST检索的相似性网络构建方法,从目标蛋白质序列出发,通过若干轮次的BLAST检索逐步从数据库中提取与目标蛋白质直接或间接相关的序列,构成关联集。关联集中序列之间的相似性关系即相似性网络,可作为图聚类算法的分类依据。对Pfam数据库中依直接相似关系难以正确分类的蛋白质的计算表明,按本文方法构建的相似性网络取得了比较满意的结果。     

Signal-LMS:一种局部序列匹配相似度预测信号肽的方法

信号肽预测是蛋白质功能预测中最重要的问题之一。为了避免使用滑动窗口造成的样本不平衡等问题,序列比对方法被有效地运用到了信号肽预测中。考虑到信号肽是蛋白质序列局部片段所体现的生物特性,本文提出一种局部序列匹配相似度的方法来预测信号肽,在采用氨基酸相对疏水性编码方案的基础上,搜索蛋白质局部匹配子序列,根据替换矩阵BLOSUM62来度量两个蛋白质的相似性,最后采用k最近邻思想进行分类。在目前广泛使用的SwissProt数据集上进行实验,结果表明该方法具有一定的高预测率。     

缺失数据下蛋白质多结构叠加的迭代方法

蛋白质三维结构叠加面临的主要问题是,参与叠加的目标蛋白质的氨基酸残基存在某些缺失,但是多结构叠加方法却大多数需要完整的氨基酸序列,而目前通用的方法是直接删去缺失的氨基酸序列,导致叠加结果不准确。由于同源蛋白质间结构的相似性,因此,一个蛋白质结构中缺失的某个区域,可能存在于另一个同源蛋白质结构中。基于此,本文提出一种新的、简单、有效的缺失数据下的蛋白质结构叠加方法（ITEMDM）。该方法采用缺失数据的迭代思想计算蛋白质的结构叠加,采用优化的最小二乘算法结合矩阵SVD分解方法,求旋转矩阵和平移向量。用该方法成功叠加了细胞色素C家族的蛋白质和标准Fischer’s 数据库的蛋白质（67对蛋白质）,并且与其他方法进行了比较。数值实验表明,本算法有如下优点：①与THESEUS算法相比较,运行时间快,迭代次数少;②与PSSM算法相比较,结果准确,运算时间少。结果表明,该方法可以更好地叠加缺失数据的蛋白质三维结构。     

基于贝叶斯网络和相互作用可信度的蛋白质功能预测方法

蛋白质功能注释是后基因组时代研究的核心内容之一,基于蛋白质相互作用网络的蛋白质功能预测方法越来越受到研究者们的关注.提出了一种基于贝叶斯网络和蛋白质相互作用可信度的蛋白质功能预测方法.该方法在功能预测过程中为待注释的蛋白质建立贝叶斯网络预测模型,并充分考虑了蛋白质相互作用的可信度问题.在构建的芽殖酵母数据集上的三重交叉验证测试表明,在功能预测过程中考虑蛋白质可信度能够有效地提高功能预测的性能.与现有一些算法相比,该方法能够给出令人满意的预测效果.     

构建基于折叠核心的全α类蛋白取代矩阵

氨基酸残基取代矩阵是影响多序列比对效果的重要因素,现有的取代矩阵对低相似序列的比对性能较低.在已有的 BLOSUM 取代矩阵算法基础上,定义了基于蛋白质折叠核心结构的序列 结构数据块;提出一种新的基于全α类蛋白质折叠核心结构的氨基酸残基取代矩阵——TOPSSUM25,用于提高低相似度序列的比对效果.将矩阵TOPSSUM25导入多序列比对程序,对相似性小于25%的一组四螺旋束序列 结构数据块的测试结果表明,基于 TOPSSUM25的多序列比对效果明显优于BLOSUM30矩阵;基于一个BAliBASE子集的比对检验也进一步表明, TOPSSUM25在全α类蛋白质的两两序列比对上优于BLOSUM30矩阵.研究结果可为进一步的阐明低同源蛋白质序列 结构 功能关系提供帮助.     

iTRAQ联用串联质谱鉴定食管鳞状细胞癌差异表达蛋白质及蛋白质相互作用网络

基于质谱的蛋白质组学结果不仅具有重复性差和覆盖率低等缺陷,并且针对数十至百个差异表达蛋白质分子的分析非常具有挑战性,而蛋白质与蛋白质相互作用网络（protein-protein interaction network, PPIN）分析能够在一定程度上弥补上述不足,使各种组学研究结果具有一致性和可比性。本研究应用同位素标记相对和绝对定量（iTRAQ）联用串联质谱技术鉴定了与食管鳞状细胞癌（esophageal squamous cell carcinoma,ESCC）相关的差异表达蛋白质244个（ESCC中,升高和降低的蛋白质分别为119个和125个）,基因本体论（gene ontology, GO）富集与肿瘤十大特征相关的17个GO条目|以该17个条目包含的117个蛋白质为种子蛋白搜索STRING（http: //www.string-db.org）数据库,构建包含96个存在相互作用的PPIN和21个离散蛋白质。用CytoHubba算法确定34个中心节点蛋白质和36个瓶颈蛋白质,非重复49个中心节点和/或瓶颈蛋白质中含7个目前已报道的癌基因表达蛋白（PPP2R1A、CTNNB1、ENO1、EZR、TPM4、COL1A1、TPM3）,确定与该7个癌蛋白直接相互作用的4个蛋白质（FN1、ITGB1、TAGLN和YWHAZ）可能为参与食管癌变的关键蛋白质,并应用Western印迹实验验证了 FN1、ITGB1、TAGLN和YWHAZ等4个关键蛋白质在ESCC中具有显著的表达差异,表明PPIN分析是确定具有重要生物学意义分子的有效途经之一。     

蛋白质相互作用网络进化分析研究进展

近年来,随着高通量实验技术的发展和广泛应用,越来越多可利用的蛋白质相互作用网络数据开始出现．这些数据为进化研究提供了新的视角．从蛋白质、蛋白质相互作用、模体、模块直到整个网络五个层次,综述了近年来蛋白质相互作用网络进化研究领域的主要进展,侧重于探讨蛋白质相互作用、模体、模块直到整个网络对蛋白质进化的约束作用,以及蛋白质相互作用网络不同于随机网络特性的起源和进化等问题．总结了前人工作给学术界的启示,探讨了该领域未来可能的发展方向．     

酵母蛋白质相互作用与基因表达谱和亚细胞定位的相关性

研究酵母（yeast）蛋白质相互作用与基因表达谱和蛋白质亚细胞定位的关系.首先,构建了蛋白质相互作用正样本集、负样本集、随机组对负样本集和混合样本集.然后,对于4个数据集中的所有蛋白质对,通过比较它们的基于距离的基因共表达的分布以及它们中具有已知亚细胞定位的蛋白质对的共定位出现率,实现了这些高通量数据的交叉量化分析.结果揭示,与非相互作用蛋白质对相比,相互作用蛋白质对的基因表达谱具有较高的相似性;相互作用蛋白质对更倾向于具有相同的亚细胞定位.结果还揭示出这些蛋白质特征相关的总体趋势.     

A genomewide functional network for the laboratory mouse

Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.     

Cognitive and Clinical Dysfunction,Altered MEG Resting-State Networks and Thalamic Atrophy in Multiple Sclerosis

The relation between pathological findings and clinical and cognitive decline in Multiple Sclerosis remains unclear. Here, we tested the hypothesis that altered functional connectivity could provide a missing link between structural findings, such as thalamic atrophy and white matter lesion load, and clinical and cognitive dysfunction. Resting-state magnetoencephalography recordings from 21 MS patients and 17 gender- and age matched controls were projected onto atlas-based regions-of–interest using beamforming. Average functional connectivity was computed for each ROI and literature-based resting-state networks using the phase-lag index. Structural measures of whole brain and thalamic atrophy and lesion load were estimated from MRI scans. Global analyses showed lower functional connectivity in the alpha2 band and higher functional connectivity in the beta band in patients with Multiple Sclerosis. Additionally, alpha2 band functional connectivity was lower for the patients in two resting-state networks, namely the default mode network and the visual network. Higher beta band functional connectivity was found in the default mode network and in the temporo-parietal network. Lower alpha2 band functional connectivity in the visual network was related to lower thalamic volumes. Beta band functional connectivity correlated positively with disability scores, most prominently in the default mode network, and correlated negatively with cognitive performance in this network. These findings illustrate the relationship between thalamic atrophy, altered functional connectivity and clinical and cognitive dysfunction in MS, which could serve as a bridge to understand how neurodegeneration is associated with altered functional connectivity and subsequently clinical and cognitive decline.     

Functional clustering in hippocampal cultures: relating network structure and dynamics

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.     

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance.     

Evaluating functional network inference using simulations of complex biological systems

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.     

Transient coordinated activity within the developing brain��s default network

The concept of a brain default network postulates that specific brain regions are more active when a person is engaged in introspective mental activity. Transient functional coordination between groups of neurons is thought to be necessary for information processing. Since children develop introspection as they mature, regions of the default network may establish increasing functional coordination with age, resulting in fewer fluctuations in synchronization patterns. We investigated the transient coordinated activity in regions of the default network in seventeen children aged 11 months to 17 years of age using EEG recordings while subjects were resting quietly with eyes closed. The temporal and spatial fluctuations in the phase synchrony patterns were estimated across sites associated with the default network pattern and compared to other regions. Lower variability of the spatio-temporal patterns of phase synchronization associated with the default network was observed in the older group as compared to the younger group. This indicates that functional coordination increases among regions of the default network as children develop.     

Comparing structural and functional graph theory features in the human brain using multimodal MRI

Recent advances in magnetic resonance imaging (MRI) are allowing neuroscientists to gain critical insights into the neural networks mediating a variety of cognitive processes. This work investigates structural and functional connectivity in the human brain under different experimental conditions through multimodal MRI acquisitions. To define the nodes of a full-brain network, a set of regions was identified from resting-state functional MRI (fMRI) data using spatial independent component analysis (sICA) and a hierarchical clustering technique. Diffusion-weighted imaging (DWI) data were acquired from the same subjects and a probabilistic fiber tracking method was used to estimate the structure of this network. Using features originating from graph theory, such as small-world properties and network efficiency, we characterized the structural and functional connectivities of the full-brain network and we compared them quantitatively. We showed that structural and functional networks shared some properties in terms of topology as measured by the distribution of the node degrees, hence supporting the existence of an underlying anatomical substrate for functional networks.     

Role of structural inhomogeneities in resting-state brain dynamics

Brain imaging methods allow a non-invasive assessment of both structural and functional connectivity. However, the mechanism of how functional connectivity arises in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which functional correlations arise from underlying structural connections taking into account inhomogeneities in the interactions between the brain regions of interest. The local dynamics of a neural population is assumed to be of phase-oscillator type. The considered structural connectivity patterns describe long-range anatomical connections between interacting neural elements. We find a dependence of the simulated functional connectivity patterns on the parameters governing the dynamics. We calculate graph-theoretic measures of the functional network topology obtained from numerical simulations. The effect of structural inhomogeneities in the coupling term on the observed network state is quantified by examining the relation between simulated and empirical functional connectivity. Importantly, we show that simulated and empirical functional connectivity agree for a narrow range of coupling strengths. We conclude that identification of functional connectivity during rest requires an analysis of the network dynamics.     

Enhancing biological relevance of a weighted gene co-expression network for functional module identification

Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.