Optimization of Data-Dependent Acquisition Parameters for Coupling High-Speed Separations with LC-MS/MS for Protein Identifications

Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.     

Determining Protein Complex Connectivity Using a Probabilistic Deletion Network Derived from Quantitative Proteomics

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.     

A Capillary Electrophoresis-Based Assay for Protein Kinases and Protein Phosphatases Using Peptide Substrates

A novel procedure for detection and assay of protein kinase and phosphatase activities in complex biological mixtures was developed. By means of capillary zone electrophoresis (CZE) methodology, the phosphorylated and dephosphorylated forms of the peptide Kemptide, a 46-amino-acid fragment from protein phosphatase inhibitor-1 and a peptide fragment corresponding to the R_II subunit of cAMP-dependent protein kinase (PKA), were rapidly resolved. This facilitated nonradioactive detection of PKA and protein phosphatase-2B (calcineurin) in rabbit skeletal muscle extracts. In addition, the CZE procedure enabled a site-specific assay of a 14-amino-acid peptide from the glycogen-binding subunit of protein phosphatase-1 monophosphorylated on distinct sites by PKA and casein kinase-II. These results suggest that CZE may prove to be extremely useful for the analysis of peptides that are phosphorylated at multiple sites in vivo.     

A Novel Cell-Penetrating Peptide Derived from Human Eosinophil Cationic Protein

Cell-penetrating peptides (CPPs) are short peptides which can carry various types of molecules into cells; however, although most CPPs rapidly penetrate cells in vitro, their in vivo tissue-targeting specificities are low. Herein, we describe cell-binding, internalization, and targeting characteristics of a newly identified 10-residue CPP, denoted ECP^32–41, derived from the core heparin-binding motif of human eosinophil cationic protein (ECP). Besides traditional emphasis on positively charged residues, the presence of cysteine and tryptophan residues was demonstrated to be essential for internalization. ECP^32–41 entered Beas-2B and wild-type CHO-K1 cells, but not CHO cells lacking of cell-surface glycosaminoglycans (GAGs), indicating that binding of ECP^32–41 to cell-surface GAGs was required for internalization. When cells were cultured with GAGs or pre-treated with GAG-digesting enzymes, significant decreases in ECP^32–41 internalization were observed, suggesting that cell-surface GAGs, especially heparan sulfate proteoglycans were necessary for ECP^32–41 attachment and penetration. Furthermore, treatment with pharmacological agents identified two forms of energy-dependent endocytosis, lipid-raft endocytosis and macropinocytosis, as the major ECP^32–41 internalization routes. ECP^32–41 was demonstrated to transport various cargoes including fluorescent chemical, fluorescent protein, and peptidomimetic drug into cultured Beas-2B cells in vitro, and targeted broncho-epithelial and intestinal villi tissues in vivo. Hence this CPP has the potential to serve as a novel vehicle for intracellular delivery of biomolecules or medicines, especially for the treatment of pulmonary or gastrointestinal diseases.     

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

The proteomic composition of the Arabidopsis (Arabidopsis thaliana) Golgi apparatus is currently reasonably well documented; however, little is known about the relative abundances between different proteins within this compartment. Accurate quantitative information of Golgi resident proteins is of great importance: it facilitates a better understanding of the biochemical processes that take place within this organelle, especially those of different polysaccharide synthesis pathways. Golgi resident proteins are challenging to quantify because the abundance of this organelle is relatively low within the cell. In this study, an organelle fractionation approach targeting the Golgi apparatus was combined with a label-free quantitative mass spectrometry (data-independent acquisition method using ion mobility separation known as LC-IMS-MS^E [or HDMS^E]) to simultaneously localize proteins to the Golgi apparatus and assess their relative quantity. In total, 102 Golgi-localized proteins were quantified. These data show that organelle fractionation in conjunction with label-free quantitative mass spectrometry is a powerful and relatively simple tool to access protein organelle localization and their relative abundances. The findings presented open a unique view on the organization of the plant Golgi apparatus, leading toward unique hypotheses centered on the biochemical processes of this organelle.The plant Golgi apparatus plays an important role in protein and lipid glycosylation and sorting as well as biosynthesis of large amounts of extracellular polysaccharides. It contains a large and diverse set of glycosyltransferases and other enzymes that are required for the synthesis and modification of these polysaccharides (Parsons et al., 2012b; Oikawa et al., 2013). The protein composition of this organelle has been the focus of a number of studies; however, these studies largely report a catalog of Golgi-localized proteins, and to date, there are no comprehensive data on the relative abundance of the different protein constituents of the Golgi apparatus (Dunkley et al., 2004, 2006; Sadowski et al., 2008; Nikolovski et al., 2012; Groen et al., 2014). The quantification of the plant Golgi proteome has been considered challenging, because this organelle is proportionally of low abundance in the cell; therefore, its constituent proteins are rarely identified in conventional proteomics experiments. Investigation of such low-abundance proteins generally requires sample fractionation on the organelle, protein, or peptide level (Stasyk and Huber, 2004; Haynes and Roberts, 2007; Di Palma et al., 2012).Here, an organelle fractionation approach in conjunction with label-free quantitative proteomic analysis was used to assess the localization and relative abundance of proteins within the plant Golgi apparatus. Label-free quantification is an increasingly popular alternative to isotopic tagging quantitative methods; it does not require labeling reagents and can be applied to an unlimited number of samples (Neilson et al., 2011; Evans et al., 2012). This is particularly appealing within plant proteomics, because the most conventional labeling strategy, Stable Isotope Labeling by Amino Acids in Cell Culture, is not easily suited for quantitative plant proteomic studies. The average labeling efficiency achieved using exogenous amino acid supply to Arabidopsis (Arabidopsis thaliana) cell cultures was found to be only 70% to 80% (Gruhler et al., 2005). Quantitative strategies with ¹⁵N metabolic labeling have been described for plant proteome analysis; however, care should be taken to ensure complete ¹⁵N incorporation, because even small amounts of ¹⁴N in the labeled sample can have significant detrimental effects on the number of peptide identifications (Nelson et al., 2007; Guo and Li, 2011; Arsova et al., 2012).In all label-free methods, samples under comparison are analyzed during separate mass spectrometry (MS) experiments (Neilson et al., 2011). The information from identified peptides is then used for relative and/or absolute quantification. The simplest label-free method involves taking the number of spectra acquired and assigned to peptides from the same protein as a measure of abundance (Ishihama et al., 2005). In an alternative approach, ion current recorded for a peptide ion is used as a measure of its abundance. The assumption is made that ion intensity is proportional to peptide amount in the sample analyzed, which holds true for nanoflow and microflow liquid chromatography (LC) systems (Levin et al., 2011; Christianson et al., 2013). Comparing peptide ion current between samples is, thus, widely used for relative quantification (Silva et al., 2005). To allow such comparison, a peptide must be identified across all samples under investigation, which is often challenging in LC-MS experiments given the highly complex nature of proteomics samples that contain tens of thousands of different peptides (Michalski et al., 2011). Hence, most relative ion intensity-based label-free approaches usually involve a step of identification transfer (Pasa-Tolíc et al., 2004). This involves matching ions from different acquisitions (in one of which, the ion has not been identified and is assigned the sequence from its matching pair in the other acquisition).Additionally, label-free proteomics can be used for absolute quantification (i.e. to estimate abundance of different proteins relative to each other within a given sample). Several different approaches have been suggested on how to convert peptide intensities to protein amounts (for comparison, see Wilhelm et al., 2014). One of the first such methods was Top-3 described by Silva et al. (2006b), who made a notable and unexpected observation, stating that the average MS signal response for the three most abundant peptides per 1 mol of protein is constant within a coefficient of variation of less than 10% (Silva et al., 2006b).In all these approaches, the peptide ion current is typically computed as the area under the curve of the chromatographic elution profile that is reconstituted from separate MS1 survey scans in which intact precursors are recorded. Determining a chromatographic profile accurately requires that the MS1 scans are performed at optimal frequency (Lange et al., 2008) and for optimal duration to record the MS1 signal at a high signal-to-noise ratio. In typical data-dependent acquisitions, however, the mass spectrometer oscillates between MS1 survey scans recording the mass/charge (m/z) for precursor peptide ions and then, a series of MS2 scans fragmenting one peptide ion precursor at a time, producing fragmentation spectra necessary for identification (Sadygov et al., 2004). As a result, the duration and frequency of MS2 scans determine the identification rate in data-dependent acquisition experiments but compromise time spent in MS1 required for accurate area under the curve quantification. Several groups have suggested data-independent acquisition, in which individual peptide ions are not selected for fragmentation but rather, groups of peptides of similar m/z are fragmented together. The exact number of cofragmented precursors depends on the speed and sensitivity of instrument configuration (for review, see Law and Lim, 2013). The simplest approach involves alternating between low-energy and high-energy scans of equal duration; low-energy scans record precursor peptide ions, whereas in high-energy scans, all precursors entering the mass spectrometer are cofragmented, and their fragments are recorded simultaneously. The method was called MS^E for Waters qTOF Mass Spectrometers (Geromanos et al., 2009) or all-ion fragmentation for Thermo Orbitrap Mass Spectrometers (Geiger et al., 2010). The analysis required downstream of this type of data acquisition is challenging given that the information of fragment origin (i.e. from what precursor peptide ion fragment was generated) is lost completely and that the high number of coeluting peptides is expected to create highly overlapping fragment spectra on fragmentation. To address this problem, Hoaglund-Hyzer and Clemmer (2001) have suggested fractionating peptides by ion mobility separation before fragmentation and MS and assigning fragments to precursors based on similarity of both chromatographic and mobility profiles (Hoaglund-Hyzer and Clemmer, 2001). The method was termed parallel fragmentation, and since that time, it has been commercialized by Waters as IMS-MS^E or HDMS^E (Shliaha et al., 2013).To date, the application of label-free quantitative proteomics to plant biology has been very limited. Recently, Helm et al. (2014) applied the LC-IMS-MS^E with Top-3 quantification to quantify the Arabidopsis chloroplast stroma proteome, allowing quantitative modeling of chloroplast metabolism. Two other works used the LC-MS^E method to assess the quantitative changes of cytosolic ribosomal proteins in response to Suc feeding and the extracellular proteome in response to salicylic acid (Cheng et al., 2009; Hummel et al., 2012).A number of proteomics approaches have been described to assess protein localization on a large scale (for review, see Gatto et al., 2010). Purification approaches attempt to isolate organelles to high levels of purity and subsequently identify and quantify proteins using LC-MS; however, such attempts yield limiting success and high false discovery rates (Andersen et al., 2002; Parsons et al., 2012a). A known limitation of this technique is the inability to completely isolate an organelle of interest, which combined with high proteome dynamic range, can result in some more abundant contaminants being identified and quantified at higher amounts than the target organelle residents. Moreover, even if a target organelle could be isolated to a certain degree of purity, it would still be impossible to deconvolute organelle residents from transient proteins that traffic through the target organelle. This becomes especially challenging for the organelles of the secretory pathway. To address these challenges, several groups applied fractionation of all organelles by gradient centrifugation and subsequent protein quantification by LC-MS. This produces distributions across the gradient for all quantified proteins, which are then used to assign organelle localization based on the specific distributions of organelle marker proteins. This effectively solves the problem of organelle contamination and protein trafficking, because a protein is expected to have a distribution characteristic of its organelle of residence, even if it is identified in all fractions, including those enriched in other organelles. Current variations of this method differ mostly by the LC-MS strategy used for quantification; for example, spectral counting was applied for protein-correlating profiles (Andersen et al., 2003), isobaric mass tagging (Nikolovski et al., 2012) and isotope-coded affinity tagging (Dunkley et al., 2004) were applied for localization of organelle proteins by isotope tagging (LOPIT), and Stable Isotope Labeling by Amino Acids in Cell Culture was applied for nucleolus/nucleus/cytosolic fractionation (Boisvert and Lamond, 2010).Here, a label-free LC-IMS-MS^E method was used for the analysis of density ultracentrifugation fractions enriched for the Golgi apparatus. First, we use relative label-free quantification involving identification transfer using the previously published synapter algorithm (Bond et al., 2013) to assess distributions of Golgi-localized proteins across the density gradient. These distributions are significantly different from those of residents of other organelles, which results in unambiguous protein assignment to the Golgi apparatus by multivariate data analysis. Second, the Top-3 absolute quantification method as implemented in Protein Lynx Global Server (PLGS) was used to rank order the Golgi-localized proteins by abundance in the fraction most enriched for Golgi apparatus. In conclusion, we present the analysis of protein distribution and abundances of the Golgi apparatus-enriched portion of the ultracentrifugation density gradient, allowing for simultaneous protein quantification and localization and leading to the assessment of relative abundances of 102 Golgi-localized proteins.     

A Rapid and Sensitive Fluorimetric Protocol for the Quantification of Green Fluorescent Protein in Soluble Protein Extracts from Transgenic Arabidopsis

Green fluorescent protein (GFP) is a popular qualitative reporter protein used to study different aspects of plant biology. However, to be used as a reliable quantitative reporter in expression studies using fluorescence based assays, methods to eliminate interfering endogenous molecules must be considered. Therefore, a standard curve based solid phase fluorescent immunoassay that eliminates the effects of interfering endogenous molecules was developed to quantify the GFP levels in soluble green extracts prepared from plants. Microtiter plates coated with anti-GFP were used to capture GFP from soluble plant extracts, interfering endogenous molecules was eliminated by washing without disturbing the anti-GFP binding of GFP, and then the fluorescence intensity of bound GFP was measured using a spectrofluorometer. We report in this study the use of this method to quantify the expression levels of soluble modified GFP in transgenic Arabidopsis thaliana.     

A Software Toolkit and Interface for Performing Stable Isotope Labeling and Top3 Quantification Using Progenesis LC-MS

Abstract Numerous software packages exist to provide support for quantifying peptides and proteins from mass spectrometry (MS) data. However, many support only a subset of experimental methods or instrument types, meaning that laboratories often have to use multiple software packages. The Progenesis LC-MS software package from Nonlinear Dynamics is a software solution for label-free quantitation. However, many laboratories using Progenesis also wish to employ stable isotope-based methods that are not natively supported in Progenesis. We have developed a Java programming interface that can use the output files produced by Progenesis, allowing the basic MS features quantified across replicates to be used in a range of different experimental methods. We have developed post-processing software (the Progenesis Post-Processor) to embed Progenesis in the analysis of stable isotope labeling data and top3 pseudo-absolute quantitation. We have also created export ability to the new data standard, mzQuantML, produced by the Proteomics Standards Initiative to facilitate the development and standardization process. The software is provided to users with a simple graphical user interface for accessing the different features. The underlying programming interface may also be used by Java developers to develop other routines for analyzing data produced by Progenesis.     

Quantification of 4 antidepressants and a metabolite by LC-MS for therapeutic drug monitoring

A liquid chromatography method coupled to mass spectrometry was developed for the quantification of bupropion, its metabolite hydroxy-bupropion, moclobemide, reboxetine and trazodone in human plasma. The validation of the analytical procedure was assessed according to Société Fran?aise des Sciences et Techniques Pharmaceutiques and the latest Food and Drug Administration guidelines. The sample preparation was performed with 0.5 mL of plasma extracted on a cation-exchange solid phase 96-well plate. The separation was achieved in 14 min on a C18 XBridge column (2.1 mm×100 mm, 3.5 μm) using a 50 mM ammonium acetate pH 9/acetonitrile mobile phase in gradient mode. The compounds of interest were analysed in the single ion monitoring mode on a single quadrupole mass spectrometer working in positive electrospray ionisation mode. Two ions were selected per molecule to increase the number of identification points and to avoid as much as possible any false positives. Since selectivity is always a critical point for routine therapeutic drug monitoring, more than sixty common comedications for the psychiatric population were tested. For each analyte, the analytical procedure was validated to cover the common range of concentrations measured in plasma samples: 1-400 ng/mL for reboxetine and bupropion, 2-2000 ng/mL for hydroxy-bupropion, moclobemide, and trazodone. For all investigated compounds, reliable performance in terms of accuracy, precision, trueness, recovery, selectivity and stability was obtained. One year after its implementation in a routine process, this method demonstrated a high robustness with accurate values over the wide concentration range commonly observed among a psychiatric population.     

乳抗氧化肽的分离纯化与结构鉴定

本文检测了不同分子量范围的WPI(乳清分离蛋白)酶解物的抗氧化活性,结果表明各个组分显示出不同的抗氧化活性,其中分子量小于5 kDa的组分最强。采用凝胶过滤色谱对分子量小于5 kDa的WPI酶解物进行分离,抗氧化活性强的组分继续采用RP-HPLC进行纯化。通过MALDI-TOF-MS与氨基酸组成分析鉴定该活性肽为His-Ile-Arg。     

用于液质联用分析的少量卵丘颗粒细胞蛋白提取方法的比较

本研究通过对比三种常用的蛋白提取裂解液,建立适合少量卵丘颗粒细胞液质联用分析的蛋白提取方法.收集的卵细胞质内精子注入术患者的卵丘颗粒细胞,分别采用SDT、UED、RIPA裂解液提取卵丘颗粒细胞总蛋白,通过蛋白浓度测定检测蛋白提取效率,SDS-PAGE检测蛋白提取质量,并对酶解后的蛋白进行单针液质联用分析其表达谱,进而对蛋白的检测效果进行评估.蛋白浓度检测表明RIPA裂解液提取的卵丘颗粒细胞蛋白得率较UED裂解液高,蛋白凝胶条带则最为清晰,条带数量最多.液质联用检测发现UED裂解液提取的蛋白鉴定效率最高,RIPA裂解液提取的蛋白质谱峰图质量最佳,通过对鉴定蛋白亚细胞定位分析,发现RIPA裂解液对于膜蛋白鉴定效率上有明显优势,而UED裂解液可能有利于细胞核蛋白的检测.该研究表明三种方法中UED提取法更适合卵丘颗粒细胞的液质联用分析,为临床少量卵丘颗粒细胞的蛋白提取与液质联用分析提供方法依据,并为其他不易获得的少量样本的蛋白质组学提供参考.     

Prediction of Peptide and Protein Propensity for Amyloid Formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation.     

Tools for Fungal Proteomics: Multifunctional Neurospora Vectors for Gene Replacement, Protein Expression and Protein Purification

The completion of genome-sequencing projects for a number of fungi set the stage for detailed investigations of proteins. We report the generation of versatile expression vectors for detection and isolation of proteins and protein complexes in the filamentous fungus Neurospora crassa. The vectors, which can be adapted for other fungi, contain C- or N-terminal FLAG, HA, Myc, GFP, or HAT–FLAG epitope tags with a flexible poly-glycine linker and include sequences for targeting to the his-3 locus in Neurospora. To introduce mutations at native loci, we also made a series of knock-in vectors containing epitope tags followed by the selectable marker hph (resulting in hygromycin resistance) flanked by two loxP sites. We adapted the Cre/loxP system for Neurospora, allowing the selectable marker hph to be excised by introduction of Cre recombinase into a strain containing a knock-in cassette. Additionally, a protein purification method was developed on the basis of the HAT–FLAG tandem affinity tag system, which was used to purify HETEROCHROMATIN PROTEIN 1 (HP1) and associated proteins from Neurospora. As expected on the basis of yeast two-hybrid and co-immunoprecipitation (Co-IP) experiments, the Neurospora DNA methyltransferase DIM-2 was found in a complex with HP1. Features of the new vectors allowed for verification of an interaction between HP1 and DIM-2 in vivo by Co-IP assays on proteins expressed either from their native loci or from the his-3 locus.     

A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications

By directly affecting structure, dynamics and interaction networks of their targets, post-translational modifications (PTMs) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control. Despite the great importance of understanding how PTMs affect proteins at the atomistic level, a systematic framework for treating post-translationally modified amino acids by molecular dynamics (MD) simulations, a premier high-resolution computational biology tool, has never been developed. Here, we report and validate force field parameters (GROMOS 45a3 and 54a7) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs. The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies (RMSE = 4.2 kJ/mol over the validation set). Using this tool, we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids, with the extent of change in many cases being comparable to the complete range spanned by native amino acids.     

StakeMeter: Value-Based Stakeholder Identification and Quantification Framework for Value-Based Software Systems

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called ‘StakeMeter’. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error.     

蛋白A信号肽引导的E.coli外泌高表达异源蛋白

利用葡萄球菌ｐｒｏｔｅｉｎ Ａ信号序列（ＳＰＡ）,我们构建了不同启动子控制的分泌表达质粒。经表达研究,获得了可控性好、表达量高的ＰＬ启动子控制的分泌表达载体。通过菌种的筛选、培养条件和诱导条件的摸索,获得了能将表达产物的绝大部分分泌到培养液中的大肠杆菌高效外泌表达系统,外泌表达量可达１００ｍｇ／Ｌ（菌浓度为１Ａ６００／ｍｌ）以上,如此高的分泌表达量尚未见文献报道。利用该系统成功、高效地外泌表达了ｐ     

Signal Peptide and Propeptide Optimization for Heterologous Protein Secretion in Lactococcus lactis

Lactic acid bacteria are food-grade microorganisms that are potentially good candidates for production of heterologous proteins of therapeutical or technological interest. We developed a model for heterologous protein secretion in Lactococcus lactis using the staphylococcal nuclease (Nuc). The effects on protein secretion of alterations in either (i) signal peptide or (ii) propeptide sequences were examined. (i) Replacement of the native Nuc signal peptide (SP_Nuc) by that of L. lactis protein Usp45 (SP_Usp) resulted in greatly improved secretion efficiency (SE). Pulse-chase experiments showed that Nuc secretion kinetics was better when directed by SP_Usp than when directed by SP_Nuc. This SP_Usp effect on Nuc secretion is not due to a better antifolding activity, since SP_Usp:Nuc precursor proteins display enzymatic activity in vitro, while SP_Nuc:Nuc precursor proteins do not. (ii) Deletion of the native Nuc propeptide dramatically reduces Nuc SE, regardless of which SP is used. We previously reported that a synthetic propeptide, LEISSTCDA, could efficiently replace the native Nuc propeptide to promote heterologous protein secretion in L. lactis (Y. Le Loir, A. Gruss, S. D. Ehrlich, and P. Langella, J. Bacteriol. 180:1895–1903, 1998). To determine whether the LEISSTCDA effect is due to its acidic residues, specific substitutions were introduced, resulting in neutral or basic propeptides. Effects of these two new propeptides and of a different acidic synthetic propeptide were tested. Acidic and neutral propeptides were equally effective in enhancing Nuc SE and also increased Nuc yields. In contrast, the basic propeptide strongly reduced both SE and the quantity of secreted Nuc. We have shown that the combination of the native SP_Usp and a neutral or acidic synthetic propeptide leads to a significant improvement in SE and in the quantity of synthesized Nuc. These observations will be valuable in the production of heterologous proteins in L. lactis.