蛋白质功能位点预测

在 IBM-PC 机上开发了蛋白质功能位点预测软件:PROSITE.根据 EMBL发布于激光光盘上的蛋白质功能位点氨基酸片段的保守模式数据库,对给定的蛋白质序列,可按19类443个氨基酸保守模式来探测蛋白质的所属家族,各种功能区的位置和活性部位等性质,通过52个序列的验证结果和 SWWISS 蛋白质数据库相一致.此外该软件还具有操作灵活,多种输入输出方式等特点。     

蛋白质磷酸化修饰的研究进展

蛋白质磷酸化是最常见、最重要的一种蛋白质翻译后修饰方式,它参与和调控生物体内的许多生命活动。通过蛋白质的磷酸化与去磷酸化,调控信号转导、基因表达、细胞周期等诸多细胞过程。随着蛋白质组学技术的发展和应用,蛋白质磷酸化的研究越来越受到广泛的重视。我们介绍了蛋白质磷酸化修饰的主要类型与功能、磷酸化蛋白质分析样品的富集及制备、磷酸化蛋白的鉴定及磷酸化位点的预测、蛋白分离后磷酸化蛋白的检测,及蛋白质磷酸化的分子机制,并综述了近年来国内外的主要相关研究进展。     

植物蛋白质磷酸化的研究技术

本文介绍植物蛋白质磷酸化研究的技术及其应用情况,并对这些技术的应用前景作了展望。     

用蛋白质组学方法解析磷酸化蛋白质

蛋白质磷酸化和去磷酸化这一可逆过程参与了高等真核生物细胞信号转导、细胞分化和细胞生长等重要过程,并与许多疾病、肿瘤的发生密切相关。蛋白质组学技术的不断发展和完善,可以更好、更多地识别和鉴定磷酸化蛋白质,为解析磷酸化蛋白质提供了可能。章综述了用于分离和鉴定磷酸化蛋白质的蛋白质组学方法。     

蛋白质组中蛋白质磷酸化研究进展

随着后基因组时代的到来 ,对生命体器官、组织或细胞的全部蛋白质的表达、修饰及相互作用的研究已成为蛋白质组学的重要任务。蛋白质磷酸化是细胞内信号转导和酶调控最常见的机制之一 ,人类基因组约 2 %的基因编码 5 0 0种激酶和 10 0种磷酸酶。蛋白质磷酸化和去磷酸化作为原核和真核细胞表达调控的关键环节 ,了解其对功能的影响可以深入理解生命系统在分子水平的调控状况。目前蛋白质组磷酸化研究仍是功能基因组面临的重大课题 ,本文对此作一综述     

真核蛋白质的亚细胞位点预测研究进展

研究真核蛋白质的亚细胞位点是了解真核蛋白质功能,深入研究蛋白质相关信号通路内在机制的基础。同时,可以为了解 疾病发病机制及为新药研发提供帮助。因此,研究真核蛋白质的亚细胞位点意义十分重大。随着基因组测序的完成,真核蛋白质 序列信息增长迅速,为真核蛋白质亚细胞位点的研究提出了更多的挑战。传统的实验法难以满足蛋白质信息量迅速增长的需求。 而采用生物信息学手段处理大规模数据的计算预测方法,可在较短时间内获得大量真核蛋白质亚细胞位点信息,弥补了实验法 的不足。因此,运用计算预测法预测真核蛋白质的亚细胞位点成为生物信息学领域的研究热点之一。本文主要从提取真核蛋白质 的特征信息、计算预测方法及预测效果的评价三个方面,介绍近年来真核蛋白质亚细胞位点预测的研究进展。     

蛋白质组氨酸磷酸化研究进展

摘要：蛋白质磷酸化是一种可逆的翻译后修饰,这种翻译后修饰可以改变蛋白质的构象,进而使蛋白质活化或者失活。组氨酸磷酸化在细胞信号传导过程中发挥着重要作用,且组氨酸磷酸化与人类某些疾病密切相关,然而,由于组氨酸磷酸化含有P-N键,具备不稳定性,有关于组氨酸磷酸化的报道远远少于其它磷酸化的报道。本综述系统的总结了组氨酸磷酸化在生物学过程中的作用,以及近些年取得的重要研究进展,以期对深入研究组氨酸磷酸化提供理论参考。     

质谱技术解析磷酸化蛋白质组

蛋白质磷酸化是生物体内存在的一种普遍的调节方式,在细胞信号传递中占有极重要的地位.质谱已逐渐被人们认为是挑战这一领域的有利工具.综述了目前利用质谱技术分析磷酸化蛋白质的方法,包括利用固定化的金属亲和层析柱、抗体和化学标签技术富集目的分子,肽片段质量图和前体离子扫描（precusor ion scans）等技术检测磷酸化肽段,串联质谱对磷酸化肽段测序鉴定磷酸化位点,以及引入质量标签对蛋白质的磷酸化水平进行定量等.虽然现在已经有很多可行的方法用于分析磷酸化蛋白质,但要达到从少量生物样品中解析其全部磷酸化蛋白质仍需要有很多技术上的突破.     

磷酸化蛋白质组研究中的富集策略

蛋白质的磷酸化与去磷酸化过程,调控着包括信号转换、基因表达、细胞周期等诸多细胞过程。因此,对蛋白质磷酸化修饰的分析是蛋白质组研究中的重要内容。但由于磷酸化蛋白的丰度较低,难以用质谱直接检测。为了解决这个问题,改善质谱对磷酸肽的信号响应,需要对磷酸化蛋白质或磷酸肽进行富集。目前主要的富集方法包括免疫沉淀、固相金属离子亲和色谱、金属氧化物/氢氧化物亲和色谱等。     

玉米根V-ATPase的A亚基磷酸化位点的鉴定

以玉米(Zea mays L)根的高纯度液泡膜为材料进行的磷酸化反应表明,液泡膜蛋白的磷酸化可明显提高v型H -ATPase(V-ATPase)的ATP水解活性和H 转运活性.进一步研究表明,纯化的液泡膜蛋白能被硫代磷酸化,用V-ATPase的A亚基抗体将一条约69 kD的条带鉴定为A亚基.为了测定V-ATPase的A亚基的磷酸化位点,从硫代磷酸化的凝胶中切下A亚基条带并用胰蛋白酶彻底消化.用RP-HPLC分离纯化酶解片断,收集纯化的硫代磷酸化肽段进行质谱分析所测定的分子量为573.83 Da.A亚基胰蛋白酶彻底消化后能产生61个肽段,只有F56肽段的分子量573.66 Da与573.83 Da最接近,而且F56肽段上只有第525位的丝氨酸可以被磷酸化.因此可以确定,玉米根V-AT-Pase A亚基的潜在磷酸化位点为Ser525.就我们所知,这是首次确定植物V-ATPase A亚基的磷酸化位点.     

Predicting protein functional sites with phylogenetic motifs

In this report, we demonstrate that phylogenetic motifs, sequence regions conserving the overall familial phylogeny, represent a promising approach to protein functional site prediction. Across our structurally and functionally heterogeneous data set, phylogenetic motifs consistently correspond to functional sites defined by both surface loops and active site clefts. Additionally, the partially buried prosthetic group regions of cytochrome P450 and succinate dehydrogenase are identified as phylogenetic motifs. In nearly all instances, phylogenetic motifs are structurally clustered, despite little overall sequence proximity, around key functional site features. Based on calculated false-positive expectations and standard motif identification methods, we show that phylogenetic motifs are generally conserved in sequence. This result implies that they can be considered motifs in the traditional sense as well. However, there are instances where phylogenetic motifs are not (overall) well conserved in sequence. This point is enticing, because it implies that phylogenetic motifs are able to identify key sequence regions that traditional motif-based approaches would not. Further, phylogenetic motif results are also shown to be consistent with evolutionary trace results, and bootstrapping is used to demonstrate tree significance.     

Predicting protein sumoylation sites from sequence features

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.     

Phosphospecific proteolysis for mapping sites of protein phosphorylation

Protein phosphorylation is a dominant mechanism of information transfer in cells, and a major goal of current proteomic efforts is to generate a system-level map describing all the sites of protein phosphorylation. Recent efforts have focused on developing technologies for enriching and quantifying phosphopeptides. Identification of the sites of phosphorylation typically relies on tandem mass spectrometry to sequence individual peptides. Here we describe an approach for phosphopeptide mapping that makes it possible to interrogate a protein sequence directly with a protease that recognizes sites of phosphorylation. The key to this approach is the selective chemical transformation of phosphoserine and phosphothreonine residues into lysine analogs (aminoethylcysteine and beta-methylaminoethylcysteine, respectively). Aminoethylcysteine-modified peptides are then cleaved with a lysine-specific protease to map sites of phosphorylation. A blocking step enables single-site cleavage, and adaptation of this reaction to the solid phase facilitates phosphopeptide enrichment and modification in one step.     

NetPhosYeast: prediction of protein phosphorylation sites in yeast

We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast--an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. AVAILABILITY: The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/.     

In vitro protein kinase C phosphorylation sites of placental lipocortin

Human placental lipocortin is a high-affinity substrate for rat brain protein kinase C in vitro with phosphorylation occurring on serine and threonine residues in a ratio of approximately 2 to 1. Comparison of the ability of various N-terminal-truncated derivatives of lipocortin to serve as phosphorylation substrates, and direct analysis of the N-terminal peptides cleaved from 32P-labeled lipocortin, indicated that threonine-24, serine-27, and serine-28 were the phosphorylation sites. The possibility is discussed that a lysine residue near the carboxy side of the phosphorylation site was involved in lipocortin interaction with the catalytic site of protein kinase C.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

Nonmuscle myosin phosphorylation sites for calcium-dependent and calcium-independent protein kinases

Thymus myosin, light chains and a synthetic peptide (S-S-K-R-A-K-A-K-T-T-K-K-R-P-Q-R-A-T-S-N-V-F-S) corresponding to the N-terminal sequence of smooth muscle myosin light chains were compared as substrates for calcium/calmodulin-dependent protein kinase (MLCK), calcium/phospholipid-dependent protein kinase (PKC), and a MgATP-activated protein kinase (H4PK) from lymphoid cells. All protein kinases catalyzed phosphorylation of the substrates although H4PK showed higher affinity for isolated light chains and the peptide. Phosphoamino acid analysis and analysis of thermolysin peptides established that PKC catalyzed phosphorylation of threonine-9 or 10. In addition, PKC and H4PK catalyzed phosphorylation at serine-19, the MLCK site. Collectively the data support the hypothesis that myosin filament assembly in nonmuscle cells may be regulated by a variety of calcium-dependent and calcium-independent protein kinases.