蛋白质亚细胞定位的生物信息学研究

细胞中蛋白质合成后被转运到特定的细胞器中,只有转运到正确的部位才能参与细胞的各种生命活动,如果定位发生偏差,将会对细胞功能甚至生命产生重大影响.蛋白质的亚细胞定位是蛋白质功能研究的重要方面,也是生物信息学中的热点问题,数据库的构建和亚细胞定位分析及预测加速了蛋白质结构和功能的研究.     

基于序列保守性和蛋白质相互作用的真核蛋白质亚细胞定位预测

蛋白质的亚细胞定位是进行蛋白质功能研究的重要信息.蛋白质合成后被转运到特定的细胞器中,只有转运到正确的部位才能参与细胞的各种生命活动,有效地发挥功能.尝试了将保守序列及蛋白质相互作用数据的编码信息结合传统的氨基酸组成编码,采用支持向量机进行蛋白质亚细胞定位预测,在真核生物中5轮交叉验证精度达到91.8%,得到了显著的提高.     

植物细胞外源蛋白亚细胞定位的机制及其应用

为将不同的生理功能区隔化,植物细胞分化出具有特异性结构特征的细胞器.分化的细胞内膜系统和分子水平的蛋白质转运调控机制为外源蛋白亚细胞定位表达提供了显著的有利条件.当蛋白质被加载适当的定位信号或启动子时,蛋白质的分选途径便确定下来,同时决定蛋白质的表达终点.本文根据相关研究主题分析了蛋白质亚细胞定位机制,重点阐述包括ER腔、质外体、液泡、蛋白体等细胞器和内膜结构在内的蛋白定位因素,同时探讨了目前蛋白定位因素的应用情况.本文确定亚细胞定位因素将成为植物基因工程领域控制亚细胞水平表达的重要技术策略.     

植物蛋白质亚细胞定位相关研究概述

蛋白质在植物细胞内的定位是了解蛋白质功能、 基因调控和蛋白质-蛋白质相互作用的关键.近年来随着各种蛋白质亚细胞定位方法的快速发展和技术的不断提升,蛋白质亚细胞定位实现了高通量、活体动态研究.本文总结了植物蛋白质亚细胞定位的常用技术,以及常用细胞器特异性标记的研究进展,并对此领域研究的发展前景做出了展望.     

叶绿体蛋白质组研究进展

亚细胞蛋白质组学是近年来蛋白组学研究中的一个热点。通过细胞器的纯化和亚细胞组分的分离,降低了样品的复杂性,增大了相应蛋白质组分的富集,有利于由此分离获得的蛋白质的序列分析及功能鉴定。叶绿体蛋白质组为植物亚细胞蛋白质组学研究中相对全面的一部分,利用亚细胞分离结合双向电泳技术系统地鉴定叶绿体中蛋白质组分是获取叶绿体蛋白质信息、确定其功能的重要技术手段。本文就近年来植物叶绿体蛋白质组涵盖的叶绿体内、外被膜、叶绿体基质、类囊体膜和类囊体腔蛋白的研究进行综述,以全面认识叶绿体蛋白的组成、特点及其在叶绿体生理生化代谢网络中的作用。     

低温胁迫对两种圆柏属植物亚细胞抗氧化酶活性的影响

以祁连圆柏和圆柏幼苗为材料,研究不同处理时间下低温胁迫对圆柏属植物叶片亚细胞抗氧化酶活性的影响,探讨其在圆柏属植物叶片中的亚细胞定位。结果表明：低温胁迫下,丙二醛(MDA)含量和抗氧化酶活性随时间变化均呈先升后降趋势,祁连圆柏中抗氧化酶的种类比圆柏的多且活性强,而 MDA 含量低于圆柏,表明祁连圆柏在低温胁迫下具有更广泛的适应性。此外,两种圆柏植物叶片超氧化物歧化酶(SOD)和抗坏血酸过氧化物酶(APX)定位为叶绿体＞细胞溶质＞线粒体,过氧化氢酶(CAT)定位为线粒体＞叶绿体＞细胞溶质,谷胱甘肽还原酶(GR)定位为线粒体＞细胞溶质＞叶绿体,祁连圆柏过氧化物酶(POD)定位为细胞溶质＞叶绿体＞线粒体,圆柏POD定位为细胞溶质＞线粒体＞叶绿体,且抗氧化酶SOD、APX和 GR在亚细胞中分布差异达到极显著,这说明抗氧化酶在其中一种亚细胞中发挥主要作用,为克隆亚细胞组分中的抗氧化酶基因提供了理论依据。     

快速发展的亚细胞蛋白质组学

亚细胞蛋白质组是蛋白质组学领域中的一支新生力量 ,已成为蛋白质组学新的主流方向 ,通过多种策略和技术方法 ,一些重要的亚细胞结构的蛋白质组不断的得到分析 ,到目前为止 ,几乎所有亚细胞结构的蛋白质组学研究都有报道 ,而且已经深入到亚细胞器和复合体水平 ;另外 ,不仅局限于对亚细胞结构的蛋白组成进行简单分析 ,而且更注重功能性分析 ,将定量技术和差异分析引入亚细胞蛋白质组学 ,来观察此亚细胞结构的蛋白质组在某些生理或病理条件下的变化 ,这已经成为亚细胞蛋白质组学新的发展方向 .亚细胞蛋白质组学最大的困难在于怎样确认鉴定出来蛋白质的定位 ,是在提取过程中的污染还是真正在此亚细胞结构中有定位 ?这将是亚细胞蛋白质组学需要努力解决的挑战 .文章全面介绍了亚细胞蛋白质组学的最新研究进展 ,阐述了亚细胞蛋白质组学面临的挑战 ,并对亚细胞蛋白质组学的发展方向作了展望 .     

拟南芥中一个未知功能蛋白的叶绿体亚细胞定位研究

生物信息学分析表明,模式植物拟南芥叶绿体中含有大约4000多种蛋白质,目前只分离得到1000多种,其他预测的叶绿体蛋白的实验验证对叶绿体功能研究有重要意义。本文对一个预测的叶绿体未知功能蛋白AT5G48790进行了亚细胞定位研究。我们克隆了该基因5端长178bp的DNA片段,与绿色荧光蛋白（GFP）基因构建重组载体pMON530-cTP-GFP。转基因植株通过激光共聚焦显微镜观察,GFP只在叶绿体中特异表达。实验结果表明,AT5G48790的确为叶绿体蛋白。本实验方法也可用于其他预测的蛋白质的实验验证。     

拟南芥中一个未知功能蛋白的叶绿体亚细胞定位研究

生物信息学分析表明, 模式植物拟南芥叶绿体中含有大约4 000多种蛋白质, 目前只分离得到1 000多种, 其他预测的叶绿体蛋白的实验验证对叶绿体功能研究有重要意义。本文对一个预测的叶绿体未知功能蛋白AT5G48790进行了亚细胞定位研究。我们克隆了该基因5'端长178 bp的DNA片段, 与绿色荧光蛋白(GFP)基因构建重组载体pMON530-cTP-GFP。转基因植株通过激光共聚焦显微镜观察, GFP只在叶绿体中特异表达。实验结果表明, AT5G48790的确为叶绿体蛋白。本实验方法也可用于其他预测的蛋白质的实验验证。     

3D-SIM结构照明超分辨率显微镜实现蛋白质在植物亚细胞器内的定位

基因表达产物蛋白质的亚细胞定位是解析基因生物学功能的重要证据之一。近年来出现的超分辨率光学成像技术已成功应用于人类和动物细胞中,预示着显微成像技术继激光共聚焦技术后的又一重要进步。由于植物细胞的特殊性和成像技术的研发取向,超分辨率光学成像技术在植物细胞蛋白质亚细胞定位的应用尚未见报道。该研究利用Delta Vision OMX显微镜技术,克服了叶绿体基粒中叶绿素自发荧光与融合蛋白荧光不易区分的缺陷,解决了受分辨率局限无法将植物细胞中蛋白质在亚细胞器内可视化精确定位的技术难题,成功地将植物蔗糖合成酶Zm SUS-SH1定位在烟草表皮细胞叶绿体基粒周围。该研究同时建立了一套基于撕片制片法的简便OMX显微镜制片方法,并针对OMX显微成像技术在植物细胞中蛋白质亚细胞定位的应用进行了讨论。     

SubChlo: Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm

The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.     

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: Approach from the strategy of discrete wavelet transform feature extraction

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).     

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.     

Predicting multisite protein subcellular locations: progress and challenges

In the last two decades, predicting protein subcellular locations has become a hot topic in bioinformatics. A number of algorithms and online services have been developed to computationally assign a subcellular location to a given protein sequence. With the progress of many proteome projects, more and more proteins are annotated with more than one subcellular location. However, multisite prediction has only been considered in a handful of recent studies, in which there are several common challenges. In this special report, the authors discuss what these challenges are, why these challenges are important and how the existing studies gave their solutions. Finally, a vision of the future of predicting multisite protein subcellular locations is given.     

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

Background  

Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins.     

Multi-label multi-kernel transfer learning for human protein subcellular localization

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.     

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%.     

Predicting 22 protein localizations in budding yeast

According to the recent experiments, proteins in budding yeast can be distinctly classified into 22 subcellular locations. Of these proteins, some bear the multi-locational feature, i.e., occur in more than one location. However, so far all the existing methods in predicting protein subcellular location were developed to deal with only the mono-locational case where a query protein is assumed to belong to one, and only one, subcellular location. To stimulate the development of subcellular location prediction, an augmentation procedure is formulated that will enable the existing methods to tackle the multi-locational problem as well. It has been observed thru a jackknife cross-validation test that the success rate obtained by the augmented GO-FnD-PseAA algorithm [BBRC 320 (2004) 1236] is overwhelmingly higher than those by the other augmented methods. It is anticipated that the augmented GO-FunD-PseAA predictor will become a very useful tool in predicting protein subcellular localization for both basic research and practical application.     

ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

ABSTRACT: BACKGROUND: Understanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of major organelles in the cell. Additionally, the majority of methods predict only a single location, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function. FINDINGS: We present a software package and a web server for predicting subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively. CONCLUSIONS: ngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu.