基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

数据非依赖采集(data-independent acquisition,DIA)是一种高通量、无偏性的质谱数据采集方法,具有定量结果重现性好,对低丰度蛋白质友好的特点,是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱,包含了多个肽段的碎片离子信息,使得蛋白质鉴定和定量更加困难。目前,DIA数据分析方法分为两大类,即以肽为中心和以谱图为中心。其中,以肽为中心的分析方法鉴定更灵敏,定量更准确,已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程,介绍了基于此流程的数据分析软件及相关比较评估工作,进一步总结了已有的算法改进工作,最后对未来发展方向进行了展望。     

基于数据非依赖采集的蛋白质组质谱数据解析方法研究进展

数据非依赖采集（DIA）是蛋白质组学领域近年来快速发展的质谱采集技术，其通过无偏碎裂隔离窗口内的所有母离子采集二级谱图，理论上可实现蛋白质样品的深度覆盖，同时具有高通量、高重现性和高灵敏度的优点。现有的DIA数据采集方法可以分为全窗口碎裂方法、隔离窗口序列碎裂方法和四维DIA数据采集方法（4D-DIA）3大类。针对DIA数据的不同特点，主要数据解析方法包括谱库搜索方法、蛋白质序列库直接搜索方法、伪二级谱图鉴定方法和从头测序方法4大类。解析得到的肽段鉴定结果需要进行可信度评估，包括使用机器学习方法的重排序和对报告结果集合的假发现率估计两个步骤，实现对数据解析结果的质控。本文对DIA数据的采集方法、数据解析方法及软件和鉴定结果可信度评估方法进行了整理和综述，并展望了未来的发展方向。     

蛋白质组研究中离子阱串联质谱数据搜库结果解释方法

基于离子阱串联质谱仪的鸟枪法是一种高通量的蛋白质鉴定方法。得到的数据一般使用软件SEQUEST搜索蛋白质序列数据库,得到肽段鉴定列表以及相应的打分。为了得到蛋白质鉴定列表,还需要进行肽段鉴定结果的过滤和假阳性率的计算,然后根据肽段鉴定结果组装蛋白质列表。这两个问题目前还没有很好地解决。对已有的方法进行总结和比较,可以给搜库结果解释方法的选择提供参考,对数据质量控制方法的改进也有所帮助。     

鸟枪法蛋白质鉴定质量控制方法研究进展

鸟枪法串联质谱蛋白质鉴定策略由于其高可靠和高效率而被广泛应用于蛋白质组学研究中,这种方法直接对蛋白质混合物进行酶切,以肽段为鉴定单元,继而推导真实的样品蛋白质．由于利用质谱图推导肽段存在一定的假阳性率,而且直接对蛋白质混合物的酶切也导致了肽段和蛋白质之间关联信息的丢失,所鉴定的蛋白质难免存在部分不可靠结果．因此,蛋白质鉴定的质量控制在蛋白质组学研究中极为重要．蛋白质鉴定的质量控制包含两大类主要方法,其一为利用肽段进行蛋白质组装,当前最常用也被证明最有效的方法是使用简约原则,即用最少的蛋白质解释所有鉴定肽段,现有的方法可以分为布尔型和概率型,其二为鉴定蛋白质的可靠性评估,包括单个蛋白质鉴定置信度和蛋白质鉴定整体水平的假阳性率计算．综合各种可辅助蛋白质鉴定的先验信息,构建普适的概率统计模型,是目前蛋白质鉴定质量控制方法的发展趋势．     

一种优化的MALDI-TOF质谱分析多肽C端序列方法

利用基质辅助激光解吸飞行时间 (MALDI TOF)质谱技术 ,测定羧肽酶Y消化蛋白质和多肽 .所产生的缩短肽片段的质量 ,在一张谱图上得到各个不同酶解时间所形成的肽质量梯度 .根据谱图中相邻两肽峰的质量差得到切去氨基酸的信息 ,从而读出C端氨基酸序列 .在pmol水平下对人促肾上腺皮质激素片段 (ACTH 1 3 9) ,人血管紧张肽片段 (angiotensin Ⅰ ,angiotensin Ⅱ )的C端序列进行了测定 .讨论了在不同浓度 ,不同时间 ,不同温度下酶解所得到的序列测定结果 .在优化条件下 ,人ACTH片段得到了C端 2 0个氨基酸残基顺序 ,为目前C端序列分析所得到的最长序列     

蛋白质组学研究中的无标记定量方法

蛋白质定量是探索疾病发生发展状况和寻找新药靶标的重要手段.该领域最常用的技术是比较染色后的二维凝胶上蛋白点的光密度值或综合同位素标记后的质谱峰强度方法.但此二者的样品处理方法都比较麻烦,不利于进行大规模蛋白质组的定量研究.最近几年出现了利用质谱数据进行无标记定量的方法, 根据数据类型这些方法可以分为2类：基于鉴定蛋白的肽段数的方法和基于质谱峰强度的方法,在高通量大规模蛋白组定量研究中有很大优势.本综述主要介绍了这2类无标记定量方法的模型及优缺点,并比较了2类方法的灵敏度和准确度.肽段计数方法在检测蛋白丰度变化时更灵敏,而峰面积强度在评估蛋白比率时更准确.     

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.     

肽段的理论串联质谱图预测方法研究进展

基于串联质谱技术的蛋白质组学已经成为生命科学领域的重要工具,其中肽段的理论串联质谱图(通常也被称为二级谱图)预测问题在近年来广受关注.大量高质量质谱数据的积累和计算技术的发展为此问题的解决提供了有效途径.肽段的理论二级谱图预测的方法可以分为两大类,一类是基于物理模型的方法,即基于移动质子模型的方法,例如MassAnalyzer、MS-Simulator;另一类是基于机器学习的方法,包括集成学习相关算法和基于神经网络的方法,例如PeptideART、MS2PIP、MS2PBPI和p Deep等.本文对这两大类方法进行了整理和综述,并简要指出了目前理论谱图预测方法存在的一些不足,展望了未来的发展方向.     

基于马尔科夫链的胰蛋白酶肽段蛋白酶切位点概率预测

在基于质谱技术的蛋白质鉴定过程中,数据库搜索是主要的方法。漏切位点和酶切规则决定了图谱候选肽段的范围,是数据库搜索算法的重要参数。对于常用的胰蛋白酶切来说,除了局部构象、三维结构、实验条件,以及其它偶然因素会影响赖氨酸K或者精氨酸R后的位点能否被酶切外,该位点附近的其它氨基酸也会影响蛋白水解酶的酶切效果。从质谱图谱中时常会鉴定出包含漏切位点的肽段,因此,预测蛋白质的酶切位点能够为数据库搜索算法提供更为可靠的模型,也能够为了解和分析蛋白质的酶切规律提供依据。本文提出了一种基于马尔科夫(Markov)链的预测方法,能够利用蛋白质的序列信息来预测候选酶切位点的酶切概率,在蛋白酶切过程中,预测肽段的覆盖率可以达到85%以上。     

生物活性肽蛋白质组分分离与鉴定方法的建立

目的:评估大规模蛋白质组分离与鉴定技术策略在生物活性肽研究中的应用价值。方法:采用全溶液酶解及胶内分离与酶解方法分离生物活性肽;肽段离子阱串联质谱鉴定时,采用碰撞诱导解离与电子转移解离2种互补的肽段碎裂模式。结果:几种方法联用,鉴定到复杂生物活性肽样品中236个多肽大分子;不同分离和鉴定策略显示出良好的互补性,基于凝胶电泳分离的策略提供了最好的鉴定效果。结论:大规模的蛋白质组学分离与鉴定技术策略可以有效应用于生物活性肽组分的表征。     

pParse: a method for accurate determination of monoisotopic peaks in high-resolution mass spectra

Determining the monoisotopic peak of a precursor is a first step in interpreting mass spectra, which is basic but non-trivial. The reason is that in the isolation window of a precursor, other peaks interfere with the determination of the monoisotopic peak, leading to wrong mass-to-charge ratio or charge state. Here we propose a method, named pParse, to export the most probable monoisotopic peaks for precursors, including co-eluted precursors. We use the relationship between the position of the highest peak and the mass of the first peak to detect candidate clusters. Then, we extract three features to sort the candidate clusters: (i) the sum of the intensity, (ii) the similarity of the experimental and the theoretical isotopic distribution, and (iii) the similarity of elution profiles. We showed that the recall of pParse, MaxQuant, and BioWorks was 98-98.8%, 0.5-17%, and 1.8-36.5% at the same precision, respectively. About 50% of tandem mass spectra are triggered by multiple precursors which are difficult to identify. Then we design a new scoring function to identify the co-eluted precursors. About 26% of all identified peptides were exclusively from co-eluted peptides. Therefore, accurately determining monoisotopic peaks, including co-eluted precursors, can greatly increase peptide identification rate.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.     

Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.     

MS2‐Deisotoper: A Tool for Deisotoping High‐Resolution MS/MS Spectra in Normal and Heavy Isotope‐Labelled Samples

High‐resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2‐Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2‐Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2‐Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non‐SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2‐Deisotoper can be used via a user interface or as a command‐line tool.     

Enhanced peptide mass fingerprinting through high mass accuracy: Exclusion of non-peptide signals based on residual mass

Peptide mass fingerprinting (PMF) is among the principle methods of contemporary proteomic analysis. While PMF is routinely practiced in many laboratories, the complexity of protein tryptic digests is such that PMF based on unrefined mass spectrometric peak lists is often inconclusive. A number of data processing strategies have thus been designed to improve the quality of PMF peak lists, and the development of increasingly elaborate tools for PMF data reduction remains an active area of research. In this report, a novel and direct means of PMF peak list enhancement is suggested. Since the monoisotopic mass of a peptide must fall within a predictable range of residual values, PMF peak lists can in principle be relieved of many non-peptide signals solely on the basis of accurately determined monoisotopic mass. The calculations involved are relatively simple, making implementation of this scheme computationally facile. When this procedure for peak list processing was used, the large number of unassigned masses typical of PMF peak lists was considerably attenuated. As a result, protein identifications could be made with greater confidence and improved discrimination as compared to PMF queries submitted with raw peak lists. Importantly, this scheme for removal of non-peptide masses was found to conserve peptides bearing various post-translational and artificial modifications. All PMF experiments discussed here were performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), which provided the high mass resolution and high mass accuracy essential for this application. Previously reported equations relating the nominal peptide mass to the permissible range of fractional peptide masses were slightly modified for this application, and these adjustments have been illustrated in detail. The role of mass accuracy in application of this scheme has also been explored.     

A general precursor ion-like scanning mode on quadrupole-TOF instruments compatible with chromatographic separation

MS protein identification and quantitation are key proteomic techniques in biological research. Besides identification of proteins, MS is used increasingly to characterize secondary protein modifications. This often requires trimming the analytical strategy to a specific type of modification. Direct analysis of protein modifications in proteomic samples is often hampered by the limited dynamic range of current analytical tools. Here we present a fast, sensitive, multiplexed precursor ion scanning mode--implemented on a quadrupole-TOF instrument--that allows the specific detection of any modified peptide or molecule that reveals itself by a specific fragment ion or pattern of fragment ions within a complex proteomic sample. The high mass accuracy of the TOF mass spectrometer is available for the marker ion specificity and the precursor ion mass determination. The method is compatible with chromatographic separation. Fragment ions and intact molecular ions are acquired quasi-simultaneously by continuously switching the collision energy between elevated and low levels. Using this technique many secondary modifications can be analyzed in parallel; however, the number of peptides carrying a specific modification that can be analyzed successfully is limited by the chromatographic resolution or, more generally, by the depth of the resolved time domain.     

"Unknown genome" proteomics: a new NADP-dependent epimerase/dehydratase revealed by N-terminal sequencing, inverted PCR, and high resolution mass spectrometry

We present here a new approach that enabled the identification of a new protein from a bacterial strain with unknown genomic background using a combination of inverted PCR with degenerate primers derived from N-terminal protein sequences and high resolution peptide mass determination of proteolytic digests from two-dimensional electrophoretic separation. Proteins of the sulfate-reducing bacterium Desulfotignum phosphitoxidans specifically induced in the presence of phosphite were separated by two-dimensional gel electrophoresis as a series of apparent soluble and membrane-bound isoforms with molecular masses of approximately 35 kDa. Inverted PCR based on N-terminal sequences and high resolution peptide mass fingerprinting by Fourier transform-ion cyclotron resonance mass spectrometry provided the identification of a new NAD(P) epimerase/dehydratase by specific assignment of peptide masses to a single ORF, excluding other possible ORF candidates. The protein identification was ascertained by chromatographic separation and sequencing of internal proteolytic peptides. Metal ion affinity isolation of tryptic peptides and high resolution mass spectrometry provided the identification of five phosphorylations identified in the domains 23-47 and 91-118 of the protein. In agreement with the phosphorylations identified, direct molecular weight determination of the soluble protein eluted from the two-dimensional gels by mass spectrometry provided a molecular mass of 35,400 Da, which is consistent with an average degree of three phosphorylations.     

Enhanced identification of peptides lacking basic residues by LC-ESI-MS/MS analysis of singly charged peptides

Peptide sequences lacking basic residues (arginine, lysine, or histidine, referred to as "base-less") are of particular importance in proteomic experiments targeting protein C-termini or employing nontryptic proteases such as GluC or chymotrypsin. We demonstrate enhanced identification of base-less peptides by focused analysis of singly charged precursors in liquid chromatography (LC) electrospray ionization (ESI) tandem mass spectrometry (MS/MS). Singly charged precursors are often excluded from fragmentation and sequence analysis in LC-MS/MS. We generated different pools of base-less and base-containing peptides by tryptic and nontryptic digestion of bacterial proteomes. Focused LC-MS/MS analysis of singly charged precursor ions yielded predominantly base-less peptide identifications. Similar numbers of base-less peptides were identified by LC-MS/M Sanalysis targeting multiply charged precursors. There was little redundancy between the base-less sequences derived by both MS/MS schemes. In the present experimental outcome, additional LC-MS/MS analysis of singly charged precursors substantially increased the identification rate of base-less sequences derived from multiply charged precursors. In conclusion, LC-MS/MS based identification of base-less peptides is substantially enhanced by additional focused analysis of singly charged precursors.