基于茶蛋白质组学数据分析植物亚细胞定位预测软件的应用

以500个茶(Camellia sinensis (L.) O. Ktze.)叶片的蛋白质作为数据集,比较TargetP、WoLF PSORT、LocTree和Plant-mPLoc 4种软件预测亚细胞定位的可信度和灵敏度。结果显示,4种软件预测可信度均高于80%,依次排序为TargetP LocTree WoLF PSORT Plant-mPLoc。其中,LocTree对细胞质蛋白和分泌蛋白检测灵敏度最高,但对叶绿体蛋白灵敏度最低; Plant-mPLoc检测核蛋白最灵敏,但对细胞质蛋白最不敏感;TargetP检测叶绿体蛋白最灵敏,但仅能区分3个亚细胞器官; WoLF PSORT对分泌蛋白检测灵敏度最低,但对其他蛋白均较灵敏。基于上述结果,该研究针对4种软件提出了合理的使用建议。     

马铃薯晚疫病菌全基因组分泌蛋白的初步分析

利用马铃薯晚疫病菌全基因组测序结果,结合计算机技术和生物信息学的方法,对马铃薯晚疫病菌的蛋白进行分析,为明确该病原菌与寄主互作的分子机制奠定基础。文章应用信号肽预测软件SignalP v3.0和PSORT,跨膜螺旋结构预测软件TMHMM-2.0和THUMBUP,GPI锚定位点预测软件big-PI Predictor,亚细胞器中蛋白定位分布预测软件TargetP v1.01,对已经公布的马铃薯晚疫病菌全基因组22 658个蛋白质氨基酸序列进行分析。结果发现,晚疫病菌全基因组编码蛋白中有671个为潜在的分泌型蛋白,占编码蛋白总数的3.0%。其中有45个分泌蛋白有功能方面的描述,其功能涉及细胞代谢、信号转导等方面;此外,还有一些与激发子类似的分泌蛋白,它们可能与晚疫病菌的毒性有关。     

粗糙脉孢菌基因组分泌蛋白的初步分析

文章报道利用信号肽预测软件SignalP v3.0和PSORT,跨膜螺旋结构预测软件TMHMMv2.0和THUMBUP,GPI-锚定位点预测软件big-PI Predictor和亚细胞器中蛋白定位分布预测软件TargetP v1.01对粗糙脉孢菌全基因组数据库中已公布的10 082个氨基酸序列进行预测分析。结果表明在粗糙脉孢菌中有437个蛋白为分泌蛋白,编码这些蛋白最小的可读框（open reading frame,ORF）为252 bp,最大为6 604 bp,平均1 433 bp,分泌蛋白信号肽长度介于15~59个氨基酸之间。在437个分泌蛋白中,205个具有功能描述,主要包括各种酶类、细胞能量生成、运转以及自身修复、防卫等多种功能。这些蛋白所参与的生化过程可能发生在膜外的周质空间或是菌体外的场所,为该物种营养的摄取,以及对环境做出响应服务。      

结核分枝杆菌Rv0081基因编码蛋白的生物信息学分析

运用生物信息学分析软件预测结核分枝杆菌（Mycobacterium tuberculosis, Mtb）Rv0081蛋白的生物学特征及筛选潜在的优势抗原表位。 从NCBI数据库获取Mtb Rv0081蛋白的氨基酸序列，利用生物信息学分析软件ProtParam、ProtScale及TMPRED分析Rv0081蛋白的理化性质及亲疏水性；TMHMM、SignalP-5.0 Server预测蛋白的跨膜区及信号肽；NetNGlyc-1.0 Server、NetPhos 3.1 Server分别预测蛋白的糖基化位点及磷酸化位点；STRING预测能与Rv0081相互作用的蛋白；分别运用SOPMA、SWISS-MODEL预测蛋白的二、三级结构；综合运用softberry、WoLF PSORT预测蛋白的亚细胞定位；运用DNAStar预测蛋白的B细胞抗原表位；综合运用SYFPEITHI、NetCTL 1.2 Server、Net MHC pan 4.1 server预测蛋白的CTL细胞抗原表位；综合运用SYFPEITHI、Net MHCII pan 4.0 server预测蛋白的Th细胞抗原表位。 结果表明，Rv0081蛋白由114个氨基酸组成，相对分子质量为12 356.32，亚细胞定位于细胞质中，为稳定的疏水性蛋白，无跨膜区和信号肽，含有1个糖基化位点及9个磷酸化位点；二级结构主要由α-螺旋和无规则卷曲构成，结构较松散；与hycE、hycP、Rv0088、Rv0083、hycD、hycQ、Rv0082、devR、Rv0080及Rv0079蛋白存在相互作用关系；综合分析各软件预测结果筛选出6个优势B细胞抗原表位、6个优势CTL细胞抗原表位及7个优势Th细胞抗原表位。Mtb Rv0081蛋白具有较多潜在的候选B、T细胞抗原表位，可作为研发新型结核疫苗的候选抗原。     

生物信息学预测与实验验证相结合策略筛选鉴定新的人分泌蛋白基因

使用生物信息学预测结合实验验证的策略筛选鉴定人新的分泌蛋白基因。用SignalP、SOSUI、PSORT和BLAST等程序对UniProt蛋白数据库进行生物信息学分析 ,筛选出用于实验验证的 1 4个功能未知基因。采用RT PCR方法 ,克隆得到 1 4个基因的全长编码序列 ,并构建到真核表达载体pcDNA3.1 ( - ) Myc His质粒。采用蛋白质印迹与免疫荧光分析 ,检测到其中 7个基因的表达。除其中一个在细胞核表达外 ,其余 6个只在细胞质中表达 ;其中的 4个基因的表达产物在细胞培养液中可被检测到 ,鉴定为 4个新的分泌蛋白基因。     

内生菌KM-1-2全基因组ORFs信号肽和分泌蛋白预测及功能分析

【目的】内生菌普遍存在于植物中,与宿主在长期的进化中形成了互利共生的关系。目前对内生菌和植物之间的互作机制研究较少,为深入了解银杏叶内生菌KM-1-2与寄主植物作用机制,本研究对其分泌蛋白进行预测,并明确其特征。【方法】组合使用信号肽分析软件Signal P,跨膜螺旋结构分析软件TMHMM 2.0和Phobius,蛋白质细胞定位软件PSORT,亚细胞定位软件Target P和GPI锚定位点分析软件big-PI Predictor,预测KM-1-2基因组范围内所有分泌蛋白,定义为分泌组。【结果】KM-1-2全基因组5299条蛋白序列中发现271个具有典型信号肽的分泌蛋白,占全基因组的2.4%;编码这些蛋白的ORF最短为61 bp,最大为2105 bp,平均为373 bp;引导它们的信号肽长度分布在15–37 aa之间,平均为24 aa。信号肽中出现频率最高的氨基酸依次为丙氨酸、亮氨酸和缬氨酸,信号肽切割类型多属于A-X-A型,即SPI切割类型。共66个蛋白质有功能描述,其中包括26个酶类。这些酶主要包括各种糖苷水解酶、酯酶、蛋白酶、碳氧裂解酶等。【结论】通过上述生物信息学分析方法有效实现了银杏叶内生菌KM-1-2分泌蛋白的预测,这些分泌蛋白功能涉及较多的酶类以及其他未知功能,为进一步研究内生菌和植物的互作提供了基础。     

Dnd1的蛋白亚细胞定位及其对HeLa细胞增殖的抑制作用

小鼠睾丸生殖细胞瘤易感基因Dnd1编码的蛋白是一个在进化中保守的RNA结合蛋白.为探讨小鼠Dnd1的蛋白亚细胞定位和对细胞增殖的影响及其机制, 利用生物信息学技术, 采用组合的亚细胞定位分析软件对Dnd1进行真核生物亚细胞定位预测; 利用融合绿色荧光蛋白(green fluorescent protein, GFP)定位的方法, 通过构建pEGFP-Dnd1重组质粒, 将重组质粒pEGFP-Dnd1转染HeLa细胞和GC-1细胞, 在荧光显微镜下观察Dnd1的蛋白亚细胞定位; 用MTT法和流式细胞技术测定Dnd1过表达对HeLa细胞的增殖能力的影响和细胞周期的改变; 在HeLa细胞系中检测Dnd1对AP-1转录活性的影响. 结果表明: ① 生物信息学预测Dnd1主要在细胞核表达, 在细胞质中也有少量表达; 荧光显微镜下观察发现,Dnd1蛋白主要定位在细胞核, 在细胞质中也有少量分布; ② Dnd1基因在HeLa细胞系中的过表达抑制细胞增殖和诱导细胞周期G1期阻滞;③ Dnd1抑制AP-1的转录活性,从而抑制AP-1介导的转录是Dnd1抑制细胞增殖的可能机制.本研究初步明确了Dnd1的蛋白亚细胞定位及其对HeLa细胞的生长抑制作用, 这为进一步研究Dnd1基因的功能建立基础.     

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。     

利用荧光共振能量转移检测外源信号分子对拟南芥ATP水平的影响

本研究使用ATP特异性荧光共振能量转移(Fluorescence resonance energy transfer,FRET)为基础的荧光蛋白传感器(Ateam1.03-nD/nA),分析了4种外源信号分子(细胞外ATP、Ca²⁺、H₂O₂和NO)对拟南芥(Arabidopsis thaliana(L.)Heynh.)幼苗叶绿体和细胞质中ATP水平的影响。结果显示,细胞质ATP水平整体高于叶绿体,在4种不同浓度的信号分子处理下,叶绿体Ateam1.03-nD/nA的FRET比值仅在1.2 ~ 1.8波动;细胞质Ateam1.03-nD/nA 的FRET比值仅在2.2 ～ 3.0之间波动,未产生显著变化。结果表明在以上外源信号分子的作用下,植物细胞质和叶绿体ATP均维持在较为稳定的水平。     

基于不同物种的热休克蛋白90的生物信息学分析

热休克蛋白90(Heat shock protein 90,Hsp90)是生物体受到刺激时发生应激反应而产生的一类应激蛋白。Hsp90包含Hsp90A,Hsp90B,Hsp90C,TRAP和Htp G5个亚家族。本文采用生物信息学方法对所选11个物种的Hsp90基因进行了分析。统计Hsp90亚家族在物种间的分布情况,验证了Hsp90亚家族在物种间的分布规律,即Hsp90A亚家族分布于除细菌外的其他所有物种中,Hsp90B和TRAP1亚家族在物种间的分布无明显规律,Hsp90C亚家族只存在于植物中,Htp G亚家族大部分存在于细菌中。通过构建系统发育树,发现Hsp90家族在进化过程中具有保守性。使用Cell-PLoc,Sub Loc v1.0,PSORT II和Multi Loc四种亚细胞定位软件对所选的11个物种的Hsp90进行亚细胞定位分析,发现Hsp90A,Htp G亚家族偏好出现在细胞质中,Hsp90B亚家族除存在于细胞质外还存在于内质网中,Hsp90C亚家族则集中于细胞质和线粒体中,TRAP1亚家族基本位于线粒体中。     

Functional assignment to maize group 1 LEA protein EMB564 within the cell nucleus using computational analysis

Maize protein EMB564 is a member of group 1 LEA (late embryogenesis abundant) proteins. Currently, the molecular functions of group 1 LEA proteins remain largely unclear. We here report on the functional assignment to EMB564 by computational analysis. EMB564 is predicted as nuclear localization by five different predictors including CELLO, Plant-mPLoc, WoLF PSORT, Predotar and TargetP. EMB564 is found to be remote homologous with DNA/RNA helicases and single-stranded DNA-binding proteins, and their sequences contains similar DNA/RNA binding sites. Furthermore, the three-dimensional (3D) model of EMB564 structurally resembles a variety of nuclear and DNA/RNA-binding proteins, especially those involving in the regulation of cell division, chromosomal replication and DNA unwinding or repairing. Our results reveal that EMB564 protein is most likely to function within the cell nucleus.     

Protein Subcellular Location: The Gap Between Prediction and Experimentation

Newly synthesized proteins in eukaryotic cells can only function well after they are accurately transported to specific organelles. The establishment of protein databases and the development of programs have accelerated the study of protein subcellular locations, but their comparisons and evaluations of the prediction accuracy of subcellular location programs in plants are lacking. In this study, we built a random test set of maize proteins to evaluate the accuracy of six commonly used programs of subcellular locations: iLoc-Plant, Plant-mPLoc, CELLO, WoLF PSORT, SherLoc2, and Predotar. Our results showed that the accuracy of prediction varied greatly depending on the programs and subcellular locations involved. The programs using homology search methods (iLoc-Plant and Plant-mPLoc) performed better than those using feature search methods (CELLO, WoLF PSORT, SherLoc2, and Predotar). In particular, iLoc-Plant achieved an 84.9 % accuracy for proteins whose subcellular locations have been experimentally determined and a 74.3 % accuracy for all of the proteins in the test set. Regarding locations, the highest prediction accuracies for subcellular locations were obtained for the nucleus, followed by the cytoplasm, mitochondria, plastids, endoplasmic reticulum, and vacuoles, while the lowest were obtained for cell membrane, secreted, and multiple-location proteins. We discussed the accuracy of the six programs in this article. This study will assist plant biologists in choosing appropriate programs to predict the location of proteins and provide clues regarding their function, especially for hypothetical or novel proteins.     

Meta-prediction of protein subcellular localization with reduced voting

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10⁻⁶, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.     

Locating proteins in the cell using TargetP, SignalP and related tools

Determining the subcellular localization of a protein is an important first step toward understanding its function. Here, we describe the properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts, and sketch a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties. We then outline how to use a number of internet-accessible tools to arrive at a reliable subcellular localization prediction for eukaryotic and prokaryotic proteins. In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark. In addition, we describe and provide web references to other useful subcellular localization predictors. Finally, we discuss predictive performance measures in general and the performance of TargetP and SignalP in particular.     

PredSL： A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein＇s function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups： chloroplast, thylakoid, mitochondrion, secretory pathway, and ＂other＂. When tested in a fivefold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL： http：//bioinformatics.biol.uoa.gr/PredSL/.     

Predicting subcellular localization of proteins based on their N-terminal amino acid sequence

A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed. Using N-terminal sequence information only, it discriminates between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and "other" localizations with a success rate of 85% (plant) or 90% (non-plant) on redundancy-reduced test sets. From a TargetP analysis of the recently sequenced Arabidopsis thaliana chromosomes 2 and 4 and the Ensembl Homo sapiens protein set, we estimate that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%. TargetP also predicts cleavage sites with levels of correctly predicted sites ranging from approximately 40% to 50% (chloroplastic and mitochondrial presequences) to above 70% (secretory signal peptides). TargetP is available as a web-server at http://www.cbs.dtu.dk/services/TargetP/.     

Transit peptide cleavage sites of integral thylakoid membrane proteins

A set of 58 nuclearly encoded thylakoid-integral membrane proteins from four plant species was identified, and their amino termini were assigned unequivocally based upon mass spectrometry of intact proteins and peptide fragments. The dataset was used to challenge the Web tools ChloroP, TargetP, SignalP, PSORT, Predotar, and MitoProt II for predicting organelle targeting and transit peptide proteolysis sites. ChloroP and TargetP reliably predicted chloroplast targeting but only reliably predicted transit peptide cleavage sites for soluble proteins targeted to the stroma. SignalP (eukaryote settings) accurately predicted the transit peptide cleavage site for soluble proteins targeted to the lumen. SignalP (Gram-negative bacteria settings) reliably predicted peptide cleavage of integral thylakoid proteins inserted into the membrane via the "spontaneous" pathway. The processing sites of more common thylakoid-integral proteins inserted by the signal recognition peptide-dependent pathway were not well predicted by any of the programs. The results suggest the presence of a second thylakoid processing protease that recognizes the transit peptide of integral proteins inserted via the spontaneous mechanism and that this mechanism may be related to the secretory mechanism of Gram-negative bacteria.     

Sorting signals, N-terminal modifications and abundance of the chloroplast proteome

Characterization of the chloroplast proteome is needed to understand the essential contribution of the chloroplast to plant growth and development. Here we present a large scale analysis by nanoLC-Q-TOF and nanoLC-LTQ-Orbitrap mass spectrometry (MS) of ten independent chloroplast preparations from Arabidopsis thaliana which unambiguously identified 1325 proteins. Novel proteins include various kinases and putative nucleotide binding proteins. Based on repeated and independent MS based protein identifications requiring multiple matched peptide sequences, as well as literature, 916 nuclear-encoded proteins were assigned with high confidence to the plastid, of which 86% had a predicted chloroplast transit peptide (cTP). The protein abundance of soluble stromal proteins was calculated from normalized spectral counts from LTQ-Obitrap analysis and was found to cover four orders of magnitude. Comparison to gel-based quantification demonstrates that 'spectral counting' can provide large scale protein quantification for Arabidopsis. This quantitative information was used to determine possible biases for protein targeting prediction by TargetP and also to understand the significance of protein contaminants. The abundance data for 550 stromal proteins was used to understand abundance of metabolic pathways and chloroplast processes. We highlight the abundance of 48 stromal proteins involved in post-translational proteome homeostasis (including aminopeptidases, proteases, deformylases, chaperones, protein sorting components) and discuss the biological implications. N-terminal modifications were identified for a subset of nuclear- and chloroplast-encoded proteins and a novel N-terminal acetylation motif was discovered. Analysis of cTPs and their cleavage sites of Arabidopsis chloroplast proteins, as well as their predicted rice homologues, identified new species-dependent features, which will facilitate improved subcellular localization prediction. No evidence was found for suggested targeting via the secretory system. This study provides the most comprehensive chloroplast proteome analysis to date and an expanded Plant Proteome Database (PPDB) in which all MS data are projected on identified gene models.     

A high-throughput approach for subcellular proteome: identification of rat liver proteins using subcellular fractionation coupled with two-dimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis

Four fractions from rat liver (a crude mitochondria (CM) and cytosol (C) fraction obtained with differential centrifugation, a purified mitochondrial (PM) fraction obtained with nycodenz density gradient centrifugation, and a total liver (TL) fraction) were analyzed with two-dimensional liquid chromatography tandem mass spectrometry analysis. A total of 564 rat proteins were identified and were bioinformatically annotated according to their physicochemical characteristics and functions. While most extreme alkaline ribosomal proteins were identified in the TL fraction, the C fraction mainly included neutral enzymes and the PM fraction enriched alkaline proteins and proteins with electron transfer activity or oxygen binding activity. Such characteristics were more apparent in proteins identified only in the TL, C, or PM fraction. The Swiss-Prot annotation and the bioinformatic prediction results proved that the C and PM fractions had enriched cytoplasmic or mitochondrial proteins, respectively. Combination usage of subcellular fractionation with two-dimensional liquid chromatography tandem mass spectrometry was proved to be a high-throughput, sensitive, and effective analytical approach for subcellular proteomics research. Using such a strategy, we have constructed the largest proteome database to date for rat liver (564 rat proteins) and its cytosol (222 rat proteins) and mitochondrial fractions (227 rat proteins). Moreover, the 352 proteins with Swiss-Prot subcellular location annotation in the 564 identified proteins were used as an actual subcellular proteome dataset to evaluate the widely used bioinformatics tools such as PSORT, TargetP, TMHMM, and GRAVY.