利用分组重量编码预测细胞凋亡蛋白的亚细胞定位

从氨基酸的物化特性出发,利用物理学中“粗粒化”和“分组”的思想,提出了一种新的蛋白质序列特征提取方法——分组重量编码方法。采用组分耦合算法作为分类器,从蛋白质一级序列出发对细胞凋亡蛋白的亚细胞定位进行研究。针对Zhou和Doctor使用的数据集,Re—substitution和Jackknife检验总体预测精度分别为98、O％和85．7％,比基于氨基酸组成和组分耦合算法的总体预测精度提高了7．2％和13．2％;针对陈颖丽和李前忠使用的数据集,Re—substitution和Jackknife检验总体预测精度分别为94．0％和80、1％,比基于二肽组成和离散增量算法的总体预测精度提高了5．9％和2、0％。针对我们自己整理的最新数据集,通过Re—substitution和Jackknife检验,总体预测精度分别为97．33％和75、11％。实验结果表明蛋白质序列的分组重量编码对于细胞凋亡蛋白的定位研究是一种有效的特征提取方法。     

基于位置特异性谱和输入加权神经网络的蛋白质亚细胞定位预测

蛋白质必须处于正确的亚细胞位置才能行使其功能。文章利用PSI-BLAST工具搜索蛋白质序列,提取位点特异性谱中的位点特异性得分矩阵作为蛋白质的一类特征,并计算4等分序列的氨基酸含量以及1～7阶二肽含量作为另外两类特征,由这三类特征一共得到蛋白质序列的12个特征向量。通过设计一个简单加权函数对各类特征向量加权处理,作为神经网络预测器的输入,并使用Levenberg-Marquardt算法代替传统的EBP算法来调整网络权值和阈值,大大提高了训练速度。对具有4类亚细胞位置和12类亚细胞位置的两种蛋白质数据集分别进行＂留一法＂测试和5倍交叉验证测试,总体预测精度分别达到88.4%和83.3%。其中,对4类亚细胞位置数据集的预测效果优于普通BP神经网络、隐马尔可夫模型、模糊K邻近等预测方法,对12类亚细胞位置数据集的预测效果优于支持向量机分类方法。最后还对三类特征采取不同加权比例对预测精度的影响进行了讨论,对选择的八种加权比例的预测结果表明,分别给予三类特征合适的权值系数可以进一步提高预测精度。     

基于蛋白序列的IDQD算法预测蛋白质相互作用

基于蛋白质序列组分信息,提出一个离散增量结合二次判别分析法(IDQD)预测蛋白质相互作用的模型,对人类蛋白质相互作用进行预测.自洽检验的识别精度达到75.89%,3-fold交叉检验的敏感性和特异性分别为64.22%和64.68%.结果表明IDQD算法可以用于蛋白质相互作用的预测.     

相似性比对预测蛋白质亚细胞区间

【目的】对蛋白质所属的亚细胞区间进行预测,为进一步研究蛋白质的生物学功能提供基础。【方法】以蛋白质序列的氨基酸组成、二肽、伪氨基酸组成作为序列特征,用BLAST比对改进K最近邻分类算法(K-nearest neighbor,KNN)实现蛋白序列所属亚细胞区间预测。【结果】在Jackknife检验下,数据集CH317三种特征的成功率分别为91.5%、91.5%和89.3%,数据集ZD98成功率分别为93.9%、92.9%和89.8%。【结论】BLAST比对改进KNN算法是预测蛋白质亚细胞区间的一种有效方法。     

基于分段伪氨基酸组成成分特征提取方法预测蛋白质亚细胞定位

蛋白质的亚细胞定位与蛋白质的功能密切相关,其定位预测有助于人们了解蛋白质功能.文章提出一种分段伪氨基酸组成成分特征提取方法,采用支持向量机算法对Chou构建的两个蛋白质亚细胞定位数据集(C2129,CS2423)进行了分类研究,并采用总分类精度Q3、内容平衡精度指数Q9等参数评估预测分类系统性能.预测结果表明,基于分段伪氨基酸组成成分特征提取方法的预测性能,优于基于完整蛋白质序列的伪氨基酸组成成分特征提取方法.例如,基于分段矩描述子伪氨基酸组成成分特征提取方法,数据集C2129的Q3和Q9分别为84.7%和60.8%,比基于完整蛋白质序列的矩描述子伪氨基酸组成成分特征提取方法分别提高1.8和2.2个百分点,且Q3比现有Xiao等人的方法提高了9.1个百分点.基于分段伪氨基酸组成成分特征提取方法构成的特征向量不仅包含残基之间的位置信息,而且还包含蛋白质子序列之问的耦合信息,另外蛋白质分段子序列可能和蛋白质的功能域有一定的联系,从而使这一方法能够有效地预测蛋白质亚细胞定位.     

基于最优分割位点的蛋白质亚细胞位点预测方法

蛋白质的亚细胞位点信息有助于我们了解蛋白质的功能以及它们之间的相互作用,同时还可以为新药物的研发提供帮助。目前普遍采用的亚细胞位点预测方法主要是基于N端分选信号或氨基酸组分特征,但研究表明,单纯基于N端分选信号或氨基酸组分的方法都会丢失序列的序信息。为了克服此缺陷,本文提出了一种基于最优分割位点的蛋白质亚细胞位点预测方法。首先,把每条蛋白质序列分割为N端、中间和C端三部分,然后在每个子序列和整条序列中分别提取氨基酸组分、双肽组分和物理化学性质,最后我们把这些特征融合起来作为整条序列的特征。通过夹克刀检验,该方法在NNPSL数据集上得到的总体精度分别是87.8%和92.1%。     

利用伪氨基酸组分和支持向量机预测抗冻蛋白

抗冻蛋白是一类具有提高生物抗冻能力的蛋白质。抗冻蛋白能够特异性的与冰晶相结合,进而阻止体液内冰核的形成与生长。因此,对抗冻蛋白的生物信息学研究对生物工程发展。提高作物抗冻性有重要的推动作用。本文采用由400条抗冻蛋白序列和400条非抗冻蛋白序列构成数据集,以伪氨基酸组分为特征,利用支持向量机分类算法预测抗冻蛋白,对训练集预测精度达到91．3％,对测试集预测精度达到78．8％。该结果证明伪氨基酸组分能够很好的反映抗冻蛋白特性,并能够用于预测抗冻蛋白。     

基于多层次稀疏编码预测蛋白质亚细胞定位

文中提出了一种简单有效的蛋白质亚细胞区间定位预测方法,为进一步了解蛋白质的功能和性质提供理论基础。运用稀疏编码,结合氨基酸组成信息提取蛋白质序列特征,基于不同字典大小对得到的特征进行多层次池化整合,并送入支持向量机进行分类。经Jackknife检验,在数据集ZD98、CH317和Gram1253上的预测成功率分别达到95.9%、93.4%和94.7%。实验证明基于多层次稀疏编码的分类预测算法能显著提高蛋白质亚细胞区间定位的预测精度。     

基于Blast-GO的蛋白质亚线粒体定位预测

本文建立了一个最新的蛋白质亚线粒体定位数据集,包含4个亚线粒体定位的1 293条序列,结合基因本体(GO)信息和同源信息对线粒体蛋白质进行特征提取,利用支持向量机算法建立分类器,经Jackknife检验,对于4个亚线粒体位置的总体预测准确率为93.27%,其中3个亚线粒体位置的总体预测准确率为94.73%.     

集成改进KNN算法预测蛋白质亚细胞定位

基于Adaboost算法对多个相似性比对K最近邻(K-nearest neighbor,KNN)分类器集成实现蛋白质的亚细胞定位预测。相似性比对KNN算法分别以氨基酸组成、二肽、伪氨基酸组成为蛋白序列特征,在KNN的决策阶段使用Blast比对决定蛋白质的亚细胞定位。在Jackknife检验下,Adaboost集成分类算法提取3种蛋白序列特征,3种特征在数据集CH317和Gram1253的最高预测成功率分别为92.4%和93.1%。结果表明Adaboost集成改进KNN分类预测方法是一种有效的蛋白质亚细胞定位预测方法。     

Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition

The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 non-redundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies.     

Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach

The location of a protein in a cell is closely correlated with its biological function. Based on the concept that the protein subcellular location is mainly determined by its amino acid and pseudo amino acid composition (PseAA), a new algorithm of increment of diversity combined with support vector machine is proposed to predict the protein subcellular location. The subcellular locations of plant and non-plant proteins are investigated by our method. The overall prediction accuracies in jackknife test are 88.3% for the eukaryotic plant proteins and 92.4% for the eukaryotic non-plant proteins, respectively. In order to estimate the effect of the sequence identity on predictive result, the proteins with sequence identity 相似文献   

Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy

The function of protein is closely correlated with it subcellular location. Prediction of subcellular location of apoptosis proteins is an important research area in post-genetic era because the knowledge of apoptosis proteins is useful to understand the mechanism of programmed cell death. Compared with the conventional amino acid composition (AAC), the Pseudo Amino Acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, a novel approach is presented to predict apoptosis protein solely from sequence based on the concept of Chou's PseAA composition. The concept of approximate entropy (ApEn), which is a parameter denoting complexity of time series, is used to construct PseAA composition as additional features. Fuzzy K-nearest neighbor (FKNN) classifier is selected as prediction engine. Particle swarm optimization (PSO) algorithm is adopted for optimizing the weight factors which are important in PseAA composition. Two datasets are used to validate the performance of the proposed approach, which incorporate six subcellular location and four subcellular locations, respectively. The results obtained by jackknife test are quite encouraging. It indicates that the ApEn of protein sequence could represent effectively the information of apoptosis proteins subcellular locations. It can at least play a complimentary role to many of the existing methods, and might become potentially useful tool for protein function prediction. The software in Matlab is available freely by contacting the corresponding author.     

Prediction of the subcellular location of apoptosis proteins

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. Based on the concept that the subcellular location of an apoptosis protein is mainly determined by its amino acid sequence, a new algorithm for prediction of the subcellular location of an apoptosis protein is proposed. By using of a distinctive set of information parameters derived from the primary sequence of 317 apoptosis proteins, the increment of diversity (ID), the sole prediction parameter, is calculated. The higher predictive success rates than the previous other algorithms is obtained by the jackknife tests using the expanded dataset. Our prediction results show that the local compositions of twin amino acids and hydropathy distribution are very useful to predict subcellular location of protein.     

Prediction of protein subcellular locations using a new measure of information discrepancy

Given a raw protein sequence, knowing its subcellular location is an important step toward understanding its function and designing further experiments. A novel method is proposed for the prediction of protein subcellular locations from sequences. For four categories of eukaryotic proteins the overall predictive accuracy is 82.0%, 2.6% higher than that by using SVM approach. For three subcellular locations of prokaryotic proteins, an overall accuracy of 89.9% is obtained. In accordance with the architecture of cells, a hierarchical prediction approach is designed. Based on amino acid composition extracellular proteins and intracellular proteins can be identified with accuracy of 97%.     

Identification of the subcellular localization of mycobacterial proteins using localization motifs

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model. These localization motifs are employed as features for identifying the subcellular localization of mycobacterial proteins. Our approach provides more accurate results than previous methods and was tested on an independent dataset recently obtained from an experimental study to provide a first and reasonably accurate prediction of subcellular localization. Our approach can also be used for large-scale prediction of new protein entries in the UniportKB database and of protein sequences obtained experimentally. In addition, our approach identified many local motifs involved with the subcellular localization that also interact with the environment. Thus, our method may have widespread applications both in the study of the functions of mycobacterial proteins and in the search for a potential vaccine target for designing drugs.     

Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach

Information of protein subcellular location plays an important role in molecular cell biology. Prediction of the subcellular location of proteins will help to understand their functions and interactions. In this paper, a different mode of pseudo amino acid composition was proposed to represent protein samples for predicting their subcellular localization via the following procedures: based on the optimal splice site of each protein sequence, we divided a sequence into sorting signal part and mature protein part, and extracted sequence features from each part separately. Then, the combined features were fed into the SVM classifier to perform the prediction. By the jackknife test on a benchmark dataset in which none of proteins included has more than 90% pairwise sequence identity to any other, the overall accuracies achieved by the method are 94.5% and 90.3% for prokaryotic and eukaryotic proteins, respectively. The results indicate that the prediction quality by our method is quite satisfactory. It is anticipated that the current method may serve as an alternative approach to the existing prediction methods.     

基于体积分类库的蛋白质亚细胞定位识别（英文）

This paper establishes the nucleus,cytoplasm,cell membrane and extracellular four database based on the protein subcellular localization,and uses the IDQD method to predict the subcellular localization of the four libraries.     

A novel representation of protein sequences for prediction of subcellular location using support vector machines

As the number of complete genomes rapidly increases, accurate methods to automatically predict the subcellular location of proteins are increasingly useful to help their functional annotation. In order to improve the predictive accuracy of the many prediction methods developed to date, a novel representation of protein sequences is proposed. This representation involves local compositions of amino acids and twin amino acids, and local frequencies of distance between successive (basic, hydrophobic, and other) amino acids. For calculating the local features, each sequence is split into three parts: N-terminal, middle, and C-terminal. The N-terminal part is further divided into four regions to consider ambiguity in the length and position of signal sequences. We tested this representation with support vector machines on two data sets extracted from the SWISS-PROT database. Through fivefold cross-validation tests, overall accuracies of more than 87% and 91% were obtained for eukaryotic and prokaryotic proteins, respectively. It is concluded that considering the respective features in the N-terminal, middle, and C-terminal parts is helpful to predict the subcellular location.