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.     

基于混合核函数SVM的蛋白质相互作用预测方法

针对蛋白质相互作用的预测问题,提出一种以余弦核和线性差值累加核为基核的对偶混合核函数SVM的蛋白质相互作用预测方法.该方法充分考虑了蛋白质的结构域特征,同时根据蛋白质相互作用数据应具有顺序无关的特点,将"对偶"思想引入SVM核函数中.对两个真实的蛋白质相互作用数据集Yeast PPI和Human PPI的测试结果表明,提出的方法与其它方法相比能够有效地提高蛋白质相互作用预测的准确率.     

基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究进展

蛋白质相互作用网络中的模块化结构通常对应于蛋白质复合物或者蛋白质功能模块。基于蛋白质相互作用网络预测蛋白质复合物和功能模块不仅有助于理解生命有机体的细胞生物过程,而且可为探讨疾病的发生、发展和治疗以及合理的药物开发提供重要的基础。本文通过回顾近二十年来基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究的发展历程,按照静态蛋白质相互作用网络(SPIN)和动态蛋白质相互作用网络(DPIN)两个方向分别梳理预测算法所涉及的方法和技术,同时归纳常用的数据集并分析所面临的问题,为进一步研究提供有价值的参考。     

建筑用户在室行为预测新方法

准确预测建筑用户在室行为可显著提高建筑能耗模拟精度,并进一步帮助建筑设计及运行控制优化.当前进行在室行为预测时所采用的主要是基于隐马尔可夫链方法的数学模型,该模型考虑了在室行为的时间关联性,可平稳有效地预测在室行为.然而现有隐马尔科夫模型难以准确描述在室行为动态变化规律以及在室行为与可观测参数之间的关联,降低了模型预测精度.针对该问题,本文提出一种基于状态转移的时变隐马尔科夫模型.该模型采用时变状态转移概率矩阵量化不同时刻在室行为的动态变化特征及关联,同时该模型基于状态转移计算可观测参数的概率分布以定量描述在室行为对可观测参数的影响.本文采用比利时某办公室在室行为数据库进行了相关建模和验证,结果表明该模型可更有效地捕捉在室状态变化,从而提高了在室行为预测精度.     

基于关联规则挖掘的蛋白质相互作用的预测

利用蛋白质的一级结构信息,采用三肽频数方法刻画蛋白质序列,将关联规则(association rule,AR)挖掘应用于蛋白质相互作用(protein-protein interactions,PPIs)的预测.计算结果表明,提出的方法在半胱氨酸不同分类的情况下都能够准确地预测蛋白质相互作用.最后,比较半胱氨酸的不同分类对预测结果的影响.     

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-seale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indirates that comprehensive integration and analysis of public large-seale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.     

蛋白质二级结构预测的结构表达方法研究

蛋白质二级结构预测时,描述窗口内氨基酸残基序列的各种物理化学和分子结构参数的数量非常大,而且不能很好地直接体现氨基酸的连接顺序,这样就造成二级结构预测工作既费时效果又低下,因此,对这些数据预处理是非常必要的.研究发现,这些初始数据对应的变换矩阵的本征值谱与氨基酸残基序列情况无关,只与氨基酸的类别、数量性质有关,而其本征函数数据能够直接反映氨基酸残基的序列情况,利用有显著大小的本征值对应的本征函数作为描述参数,在进行蛋白质二级结构预测时,不但能够显著减少描述参数的个数,而且它体现了氨基酸顺序的变化,这将有效提高蛋白质二级结构预测研究效果.     

非理想多元物系精馏点效率预测

提出计算非理想多元物系精馏点效率的方法，考虑了各组分分子间的交互作用对扩散传质过程的影响和塔板流体力学状对汽液相际传传质接触面积的影响，对３个多元物系的计算表明，该法能预测霜些组分点效率的奇异性，且理论计算点效率值与实测结果比较一致。     

Reaching for high-hanging fruit in drug discovery at protein-protein interfaces

Targeting the interfaces between proteins has huge therapeutic potential, but discovering small-molecule drugs that disrupt protein-protein interactions is an enormous challenge. Several recent success stories, however, indicate that protein-protein interfaces might be more tractable than has been thought. These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein-protein interactions. Remarkably, these small molecules bind deeper within the contact surface of the target protein, and bind with much higher efficiencies, than do the contact atoms of the natural protein partner. Some of these small molecules are now making their way through clinical trials, so this high-hanging fruit might not be far out of reach.     

The protein-protein interaction map of Helicobacter pylori

With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.     

A systems biology analysis of the Drosophila phagosome

Phagocytes have a critical function in remodelling tissues during embryogenesis and thereafter are central effectors of immune defence. During phagocytosis, particles are internalized into 'phagosomes', organelles from which immune processes such as microbial destruction and antigen presentation are initiated. Certain pathogens have evolved mechanisms to evade the immune system and persist undetected within phagocytes, and it is therefore evident that a detailed knowledge of this process is essential to an understanding of many aspects of innate and adaptive immunity. However, despite the crucial role of phagosomes in immunity, their components and organization are not fully defined. Here we present a systems biology analysis of phagosomes isolated from cells derived from the genetically tractable model organism Drosophila melanogaster and address the complex dynamic interactions between proteins within this organelle and their involvement in particle engulfment. Proteomic analysis identified 617 proteins potentially associated with Drosophila phagosomes; these were organized by protein-protein interactions to generate the 'phagosome interactome', a detailed protein-protein interaction network of this subcellular compartment. These networks predicted both the architecture of the phagosome and putative biomodules. The contribution of each protein and complex to bacterial internalization was tested by RNA-mediated interference and identified known components of the phagocytic machinery. In addition, the prediction and validation of regulators of phagocytosis such as the 'exocyst', a macromolecular complex required for exocytosis but not previously implicated in phagocytosis, validates this strategy. In generating this 'systems-based model', we show the power of applying this approach to the study of complex cellular processes and organelles and expect that this detailed model of the phagosome will provide a new framework for studying host-pathogen interactions and innate immunity.     

蛋白质-蛋白质相互作用的实验检测方法

生物体内的蛋白质分子常常通过与其他蛋白质分子发生相互作用来发挥其生物功能. 因此, 研究蛋白质-蛋白质相互作用 (protein-protein interaction, PPI) 对于阐明蛋白质分子的生物功能以及分子作用机理具有重要的意义. 主要介绍基于生物物理和生物化学原理的检测蛋白质-蛋白质相互作用的实验研究方法, 并对发展趋势做出展望.     

重组人肿瘤坏死因子蛋白TNF-α的纯化与功能鉴定

 串联亲和纯化系统（TAP）是近年来广泛使用的一种可在接近于生理条件下探究蛋白间相互作用的生化纯化方法。目前许多关于TAP 的研究报道均使用细胞内转录因子为目标蛋白,探索细胞内与其相互作用的蛋白分子。本文尝试建立一种细胞外配体-受体刺激介导的信号转导初始复合体的纯化体系,为此选择了一种多功能的免疫分子配体蛋白--肿瘤坏死因子蛋白超家族（TNFSF）中的核心成员TNF-α。TNF-α 是一个强效促炎因子,可介导炎症反应、细胞凋亡和激活等生理效应,是至今研究最多的TNFSF 成员。利用原核表达系统纯化出了一种带有多个标签的重组人源TNF-α 蛋白,通过检测其受体下游信号通路鉴定该蛋白与天然TNF-α 蛋白的生理活性相似,从而证明该蛋白可以用于胞外TAP 系统探究TNFR 下游信号通路,为配体-受体刺激介导的信号转导初始复合体的胞外串联亲和纯化系统的建立奠定了基础。     

自适应动态三次指数平滑法在交通预测中的应用

针对传统三次指数平滑法预测模型中平滑系数固定不变、难以跟踪时间序列随时间变化的因素、无法反应不同时间段历史数据对预测结果的影响等缺点,提出了一种改进的动态三次指数平滑法.该方法通过误差平方、最小原则和地毯式搜索算法来获得动态调整的平滑因子,对波动范围较大且呈非线性变化规律的数据有很强的适应性.将自适应动态三次指数平滑法与SARIMA模型、三次指数平滑法进行对比,由铁路旅客发送量的仿真实例分析结果可知,自适应动态三次指数平滑法能更好地适应时间序列的变化趋势,具有良好的预测精度.     

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.     

基于序列剖面和可及表面积的蛋白质相互作用位点的预测

蛋白质相互作用位点的预测对于突变设计和蛋白质相互作用网络的重构都是至关重要的.由于实验确定的蛋白质复合物和蛋白质配体复合物的结构依然相当少,预测蛋白质相互作用位点的计算方法就显得十分重要.该文提出了一种以支持向量机为分类器,以邻近残基的序列剖面和可及表面积为输入数据来预测蛋白质相互作用位点的方法.计算结果显示,界面残基和非界面残基被识别的准确率为75.12%,假阳性率为28.04%.与输入数据仅有序列剖面的方法相比,界面残基和非界面残基被识别的准确率提高了4.34%,假阳性率降低了4.63%.     

应用改进的共鸣识别模型预测蛋白质相互作用

蛋白质是所有生命活动的载体,它们之间的相互作用在生命活动中起着至关重要的作用.该文介绍了原有的用于预测蛋白质相互作用的共鸣识别模型,并对该模型运用小波变换进行改进,提出了改进后的共鸣识别模型.该模型的最大特点在于直接通过蛋白质的一级结构预测蛋白质之间的相互作用,改进后的模型较原模型更加适合于蛋白质相互作用的预测.运用改进的共鸣识别模型进行了数值试验,取得了较好的预测效果.     

Millisecond-timescale motions contribute to the function of the bacterial response regulator protein Spo0F.

Protein backbones and side chains display varying degrees of flexibility, which allows many slightly different but related conformational substates to occur. Such fluctuations are known to differ in both timescale and magnitude, from rotation of methyl groups (nanoseconds) to the flipping of buried tyrosine rings (seconds). Because many mechanisms for protein function require conformational change, it has been proposed that some of these ground-state fluctuations are related to protein function. But exactly which aspects of motion are functionally relevant remains to be determined. Only a few examples so far exist where function can be correlated to structural fluctuations with known magnitude and timescale. As part of an investigation of the mechanism of action of the Bacillus subtilis response regulator SpoOF, we have explored the relationship between the motional characteristics and protein-protein interactions. Here we use a set of nuclear magnetic resonance 15N relaxation measurements to determine the relative timescales of SpoOF backbone fluctuations on the picosecond-to-millisecond timescale. We show that regions having motion on the millisecond timescale correlate with residues and surfaces that are known to be critical for protein-protein interactions.