蛋白质氨基酸网络研究进展

氨基酸网络是运用复杂网络工具对蛋白质结构-功能关系研究的新方法。本文回顾了氨基酸网络中常用网络参量的计算方法,如：度分布,聚集系数,平均最短路径等。结合本研究小组的工作,介绍了常用的网络构建和分析方法,并总结了氨基酸网络在蛋白质折叠以及蛋白质分子对接问题中的应用。最后,分析了氨基酸网络研究目前存在的主要问题,并对未来的工作进行了展望。     

不同类型氨基酸网络参量与蛋白质折叠的关系

理论和实验研究表明,蛋白质天然拓扑结构对其折叠过程具有重要的影响．采用复杂网络的方法分析蛋白质天然结构的拓扑特征,并探索蛋白质结构特征与折叠速率之间的内在联系．分别构建了蛋白质氨基酸网络、疏水网、亲水网、亲水-疏水网以及相应的长程网络,研究了这些网络的匹配系数(assortativity coefficient)和聚集系数(clustering coefficient)的统计特性．结果表明,除了亲水-疏水网,上述各网络的匹配系数均为正值,并且氨基酸网和疏水网的匹配系数与折叠速率表现出明显的线性正相关,揭示了疏水残基间相互作用的协同性有助于蛋白质的快速折叠．同时,研究发现疏水网的聚集系数与折叠速率有明显的线性负相关关系,这表明疏水残基间三角结构(triangle construction)的形成不利于蛋白质快速折叠．还进一步构建了相应的长程网络,发现序列上间距较远的残基接触对的形成将使蛋白质折叠进程变慢．     

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．     

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.     

点饱和突变技术及其在蛋白质工程中的应用

点饱和突变技术是蛋白质工程中的一门新兴技术,它通过对目的蛋白的编码基因进行改造,短时间内获取靶位点氨基酸分别被其它19种氨基酸替代的突变子。此技术不仅是蛋白质定向改造的强有力工具,而且是蛋白质结构－功能关系研究的重要手段。本文概述了几种常用点饱和突变技术,介绍了其在蛋白质工程中的应用状况,讨论了其在应用中的问题,展望了其应用前景。     

基因调控网络的模块化组织研究

基因调控网络表现的是大量基因受到转录因子的调控而最终转录翻译为蛋白质进而实现生物功能的复杂信息, 是人们理解生物过程和基因功能的重要内容。为了理解基因调控网络中的调控机理, 网络的拓扑结构及其组织方式是极其重要的研究内容之一。它不仅能说明网络的局部特征, 并且能揭示调控网络的构造方法, 同时还能对调控信号通路进行全面系统的分析。调控网络可分为4层结构: 调控元件、Motif、模块和整个网络。当前, 这种层次结构受到人们越来越多的认可。文中重点讨论motif和模块两层, 比较分析了近年来对网络组织结构的多方面研究内容, 阐述了各个研究结果与结论具有的生物学意义, 并指出了其中存在的问题。在此基础上, 文中还针对这些问题提出了可能存在的研究方向, 并展望了基因调控网络模块化组织的研究前景。     

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。     

规模化蛋白质相互作用的研究技术

蛋白质相互作用既是蛋白质执行功能的主要方式,也是细胞功能调控网络的结构基础。蛋白质间异常的相互作用及其连锁网络的紊乱是引起许多病理改变的原因。作为功能基因组和蛋白质组研究的重要内容,规模化蛋白质相互作用研究已成为近年国际上研究的热点之一。文章综述了当前规模化蛋白质相互作用研究中的常用技术和常用蛋白质相互作用数据库,研究者可根据研究需要和技术特点利用这些资源。     

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.     

基于相互作用的蛋白质功能预测

蛋白质功能预测是后基因时代研究的热点问题。基于相互作用的蛋白质功能预测方法目前应用比较广泛,但是当"伙伴蛋白质"(interacting partners)数目k较小时,其预测准确率不高。从蛋白质相互作用网络入手,结合"小世界网络"特性,有效解决了k较小时预测准确率不高的问题。对酵母(Saccharomyces cerevisiae)蛋白质的相互作用网络进行预测,当k≤4时其预测准确率比相同条件下的GO(global optimization)方法有一定提高。实验结果表明:该方法能够有效的应用于伙伴蛋白质数目较小时的蛋白质功能预测。     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

An evolutionarily conserved network of amino acids mediates gating in voltage-dependent potassium channels

A novel sequence-analysis technique for detecting correlated amino acid positions in intermediate-size protein families (50-100 sequences) was developed, and applied to study voltage-dependent gating of potassium channels. Most contemporary methods for detecting amino acid correlations within proteins use very large sets of data, typically comprising hundreds or thousands of evolutionarily related sequences, to overcome the relatively low signal-to-noise ratio in the analysis of co-variations between pairs of amino acid positions. Such methods are impractical for voltage-gated potassium (Kv) channels and for many other protein families that have not yet been sequenced to that extent. Here, we used a phylogenetic reconstruction of paralogous Kv channels to follow the evolutionary history of every pair of amino acid positions within this family, thus increasing detection accuracy of correlated amino acids relative to contemporary methods. In addition, we used a bootstrapping procedure to eliminate correlations that were statistically insignificant. These and other measures allowed us to increase the method's sensitivity, and opened the way to reliable identification of correlated positions even in intermediate-size protein families. Principal-component analysis applied to the set of correlated amino acid positions in Kv channels detected a network of inter-correlated residues, a large fraction of which were identified as gating-sensitive upon mutation. Mapping the network of correlated residues onto the 3D structure of the Kv channel from Aeropyrum pernix disclosed correlations between residues in the voltage-sensor paddle and the pore region, including regions that are involved in the gating transition. We discuss these findings with respect to the evolutionary constraints acting on the channel's various domains. The software is available on our website     

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein–protein interactions, and for understanding communication within and between proteins.     

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Accurate and large‐scale prediction of protein–protein interactions directly from amino‐acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino‐acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two‐component systems and comprehensively reconstruct two‐component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome‐wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome‐wide two‐component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub’ nodes that distribute and integrate signals to and from up to tens of different interaction partners.     

氨基酸邻域对二级结构的影响程度研究

通过研究神经网络权值矩阵的算法,挖掘蛋白质二级结构与氨基酸序列间的内在规律,提高一级序列预测二级结构的准确度。神经网络方法在特征分类方面具有良好表现,经过学习训练后的神经元连接权值矩阵包含样本的内在特征和规律。研究使用神经网络权值矩阵打分预测;采用错位比对方法寻找敏感的氨基酸邻域;分析测试集在不同加窗长度下的共性表现。实验表明,在滑动窗口长度L=7时,预测性能变化显著;邻域位置P=4的氨基酸残基对预测性能有加强作用。该研究方法为基于局部序列特征的蛋白质二级结构预测提供了新的算法设计。     

Characterization and prediction of linker sequences of multi-domain proteins by a neural network

In this paper, we describe a neural network analysis of sequences connecting two protein domains (domain linkers). The neural network was trained to distinguish between domain linker sequences and non-linker sequences, using a SCOP-defined domain library. The analysis indicated that a significant difference existed between domain linkers and non-linker regions, including intra-domain loop regions. Moreover, the resulting Hinton diagram showed a position-dependent amino acid preference of the domain linker sequences, and implied their non-random nature. We then applied the neural network to predict domain linkers in multi-domain protein sequences. As the result of a Jack-knife test, 58% of the predicted regions matched actual linker regions (specificity), and 36% of the SCOP-derived domain linkers were predicted (sensitivity). This prediction efficiency is superior to simpler methods derived from secondary structure prediction that assume that long loop regions are putative domain linkers. Altogether, these results suggest that domain linkers possess local characteristics different from those of loop regions.     

Prediction of protein secondary structure content using amino acid composition and evolutionary information

Knowing protein structure and inferring its function from the structure are one of the main issues of computational structural biology, and often the first step is studying protein secondary structure. There have been many attempts to predict protein secondary structure contents. Previous attempts assumed that the content of protein secondary structure can be predicted successfully using the information on the amino acid composition of a protein. Recent methods achieved remarkable prediction accuracy by using the expanded composition information. The overall average error of the most successful method is 3.4%. Here, we demonstrate that even if we only use the simple amino acid composition information alone, it is possible to improve the prediction accuracy significantly if the evolutionary information is included. The idea is motivated by the observation that evolutionarily related proteins share the similar structure. After calculating the homolog-averaged amino acid composition of a protein, which can be easily obtained from the multiple sequence alignment by running PSI-BLAST, those 20 numbers are learned by a multiple linear regression, an artificial neural network and a support vector regression. The overall average error of method by a support vector regression is 3.3%. It is remarkable that we obtain the comparable accuracy without utilizing the expanded composition information such as pair-coupled amino acid composition. This work again demonstrates that the amino acid composition is a fundamental characteristic of a protein. It is anticipated that our novel idea can be applied to many areas of protein bioinformatics where the amino acid composition information is utilized, such as subcellular localization prediction, enzyme subclass prediction, domain boundary prediction, signal sequence prediction, and prediction of unfolded segment in a protein sequence, to name a few.     

Discriminating lysosomal membrane protein types using dynamic neural network

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.     

The integrated development of network complexity modulates the diverse evolutionary mutation rates of individual proteins

The rate of evolution-related mutation varies widely among proteins while the unity of the organism implies an integrated evolution of its protein network. Focusing on the yeast interactome, we monitored the structural impact of amino acid substitution on yeast proteins with reported structure. The impact of mutation in creating or deleting structural markers for interactivity varies across proteins and modulates the evolutionary rates, yielding a unified kinetic law of accumulation of connectivities consistent with an integrated evolution of the interactome.