线性短模体：介导蛋白质相互作用的新模块

线性短模体是天然无序蛋白实现生物学功能的重要组件.线性短模体具有柔性结构和短小的序列,可以介导瞬时、可逆的蛋白质相互作用,并在发生相互作用时表现出杂泛性.随着实验技术的更新和预测手段的发展,越来越多的线性短模体被发现和重新定义,例如BH3线性短模体.本文重点总结了线性短模体在结构、生物学功能以及进化等方面的特点.对线性短模体功能的研究将为解析细胞信号转导网络、疾病靶标确认、新药发现等领域带来新的思路.     

细胞黏附分子互作网络的构建与分析

借助网络分析可对基因调控、蛋白质互作和信号转导等细胞活动进行全局和局部性质分析.以细胞黏附的蛋白质相互作用为对象,通过数据挖掘和可视化软件构建了整合蛋白介导的黏附分子互作网络,该分子互作网络由156种蛋白质通过690种相互作用相连,其平均节点度为8.66、平均聚集系数为0.24,平均路径长度为2.6.黏附分子互作网络中包含数个功能模块,这些模块涉及网络内部多种分子相互作用的启动与停止,并进一步影响细胞的黏附、迁移和骨架组织.对黏附分子网络进行模体筛选和比较,发现一些数量相对较少、以三元复合物为主要结构的关键模体,同时对各网络模块和模体对细胞黏附的调控作用进行了探讨.     

结构域相互作用数据库的产生、发展与应用

结构域是进化上的保守序列单元,是蛋白质的结构和功能的标准组件．典型的两个蛋白质间的相互作用涉及特殊结构域间的结合,而且识别相互作用结构域对于在结构域水平上彻底理解蛋白质的功能与进化、构建蛋白质相互作用网络、分析生物学通路等十分重要．目前,依赖于对实验数据的进一步挖掘和对各种不同输入数据的计算预测,已识别出了一些相互作用/功能连锁结构域对,并由此构建了内容丰富、日益更新的结构域相互作用数据库．综述了产生结构域相互作用的8种计算预测方法．介绍了5个结构域相互作用公共数据库3DID、iPfam、InterDom、DIMA和DOMINE的有关信息和最新动态．实例概述了结构域相互作用在蛋白质相互作用计算预测、可信度评估,蛋白质结构域注释,以及在生物学通路分析中的应用．     

植物锚蛋白研究进展

锚蛋白重复序列模体是生物体内最普遍的蛋白质序列模体之一,在多种细胞活动中主要介导蛋白质-蛋白质的相互作用。综述了近年来有关锚蛋白参与植物信号传导的研究进展。     

大规模酵母双杂交技术的发展及其在蛋白质相互作用组学中的应用

酵母双杂交技术作为研究蛋白质相互作用的主要方法,在规模化蛋白质相互作用研究中占据举足轻重的地位。虽然蛋白质相互作用的数据逐年递增,但是还远不能满足"大数据"的实际需求。为了使蛋白质相互作用组学研究更加高效、快捷、准确,以及使酵母双杂交适用于全基因组规模筛选和蛋白质相互作用数据高度覆盖的研究需求,近年来对酵母双杂交技术进行了一系列的改进和发展。综述了近年来在规模化蛋白质相互作用组学研究中,酵母双杂交技术的最新改进和发展。     

蛋白质相互作用研究进展

蛋白质行使功能时,需要与其他蛋白质或者其他分子相互作用才能完成.在蛋白质相互作用水平上研究蛋白质对理解蛋白质功能、疾病与进化具有重要的意义.就蛋白质相互作用的预测、常用的蛋白质相互作用数据库以及蛋白质相互作用网络的研究进行了介绍.     

预测蛋白质间相互作用的生物信息学方法

后基因组时代的研究模式,已从原来的序列-结构-功能转向基因表达-系统动力学-生理功能。建立蛋白质间相互作用的完全网络,即蛋白质相互作用组(interactome),将有助于从系统角度加深对细胞结构和功能的认识,并为新药靶点的发现和药物设计提供理论基础。一系列系统分析蛋白质相互作用的实验方法已经建立,近年来,出现了多种预测蛋白质相互作用的生物信息学方法,这些方法不仅是对传统实验方法的有价值的补充,而且能够扩展实验方法的预测范围;同时,在开发这些方法的过程中建立了一些重要的分子进化和分子生物学慨念。本文综述了9种生物信息学方法的原理、方法评估、存在的问题．并分析了这个领域的发展前景。     

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。     

利用基因共表达网络分析蛋白质的相互作用

基于Hom in的基因共表达网络的比较分析,发现人类基因共表达网络和蛋白质相互作用数据之间存在一定的相关性。采用基因本体论对这两个网络重叠区域进行基因分类后发现,这些编码的蛋白质主要集中在对刺激物的应答途径之中。通过对该途径中的蛋白质相互作用网络作图,获得了两个独立的功能模块。通过对模块中的基因分类和关键基因分析得出两者分别对应于内外源刺激物的应答功能。本研究对于利用不断丰富的核酸公共数据信息挖掘蛋白质相互作用的研究具有积极的促进作用。     

计算方法在蛋白质相互作用研究中的应用

计算方法在蛋白质相互作用研究的各个阶段扮演了一个重要的角色。对此,作者将从以下几个方面对计算方法在蛋白质相互作用及相互作用网络研究中的应用做一个概述：蛋白质相互作用数据库及其发展;数据挖掘方法在蛋白质相互作用数据收集和整合中的应用;高通量方法实验结果的验证;根据蛋白质相互作用网络预测和推断未知蛋白质的功能;蛋白质相互作用的预测。     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

Improving evolutionary models of protein interaction networks

MOTIVATION: Theoretical models of biological networks are valuable tools in evolutionary inference. Theoretical models based on gene duplication and divergence provide biologically plausible evolutionary mechanics. Similarities found between empirical networks and their theoretically generated counterpart are considered evidence of the role modeled mechanics play in biological evolution. However, the method by which these models are parameterized can lead to questions about the validity of the inferences. Selecting parameter values in order to produce a particular topological value obfuscates the possibility that the model may produce a similar topology for a large range of parameter values. Alternately, a model may produce a large range of topologies, allowing (incorrect) parameter values to produce a valid topology from an otherwise flawed model. In order to lend biological credence to the modeled evolutionary mechanics, parameter values should be derived from the empirical data. Furthermore, recent work indicates that the timing and fate of gene duplications are critical to proper derivation of these parameters. RESULTS: We present a methodology for deriving evolutionary rates from empirical data that is used to parameterize duplication and divergence models of protein interaction network evolution. Our method avoids shortcomings of previous methods, which failed to consider the effect of subsequent duplications. From our parameter values, we find that concurrent and existing existing duplication and divergence models are insufficient for modeling protein interaction network evolution. We introduce a model enhancement based on heritable interaction sites on the surface of a protein and find that it more closely reflects the high clustering found in the empirical network.     

Making the right connections: biological networks in the light of evolution

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein–protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre‐stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.     

Network evolution: rewiring and signatures of conservation in signaling

The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.     

Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum

Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication–divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene duplications with immediate divergence alone may explain more than 60% of biological network data in both domains.     

Functional evolution of the yeast protein interaction network

Protein interactions are central to most biological processes. We investigated the dynamics of emergence of the protein interaction network of Saccharomyces cerevisiae by mapping origins of proteins on an evolutionary tree. We demonstrate that evolutionary periods are characterized by distinct connectivity levels of the emerging proteins. We found that the most-connected group of proteins dates to the eukaryotic radiation, and the more ancient group of pre-eukaryotic proteins is less connected. We show that functional classes have different average connectivity levels and that the time of emergence of these functional classes parallels the observed connectivity variation in evolution. We take these findings as evidence that the evolution of function might be the reason for the differences in connectivity throughout evolutionary time. We propose that the understanding of the mechanisms that generate the scale-free protein interaction network, and possibly other biological networks, requires consideration of protein function.     

How the global structure of protein interaction networks evolves

Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time-scales. For instance, more than 100 interactions may be added to the yeast network every million years, a fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain (without natural selection on global network structure) the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d) of proteins with d neighbours, which is broad-tailed and consistent with a power law, that is: P(d) proportional, variant d (-gamma).     

The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.     

Pairwise alignment of protein interaction networks.

With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost.