肽段反相色谱保留时间预测算法及其在蛋白质鉴定中的应用

液相色谱-质谱(LC-MS)联用是当今规模化蛋白质鉴定的主流技术。肽段在反相液相色谱(RPLC)中的保留时间主要是由肽段的理化性质和LC条件(固定相、流动相)决定的。可以通过分析肽段的理化性质,并量化它们对肽段色谱行为的影响来预测保留时间。预测结果可以用于帮助提高蛋白质鉴定的数量和可信度,也可用于肽段的翻译后修饰等研究。现在已有的保留时间预测算法主要有保留系数法和机器学习法两大类,得到的预测保留时间与实际保留时间相关系数可达到0.93。随着色谱和质谱技术的不断发展,肽段色谱行为的稳定性和重现性越来越好,保留时间预测结果也越来越准确。预测肽段保留时间将成为提高蛋白质鉴定结果的重要技术手段之一。     

蛋白质组质谱分析中基于串并联支持向量机的肽段色谱保留时间预测方法

基于质谱的大规模蛋白质鉴定中,在线液相色谱分离发挥了重要作用。色谱保留时间(retention time,RT)是肽段鉴定和定量的重要信息。由于整个色谱分析运行时间中,流动相中的有机相采用了非线性浓度曲线以及样品中肽段之间的相互影响等因素,基于肽段序列的RT预测还存在精度不高、模型推广性能差等问题。本文提出了一种基于串并联支持向量机(serial and parallel support vector machine,SP-SVM)的RT预测方法,能够表征洗脱过程中有机相浓度的非线性变化和肽段之间的相互影响,显著提高了肽段保留时间预测的精度。利用复杂样本数据集验证结果表明,预测RT和实验RT之间的决定系数达到了0.95,超过95%的鉴定肽段的RT预测误差范围小于总运行时间的20%,超过70%的鉴定肽段的RT预测误差范围小于总运行时间的10%。本文提出的模型的性能达到了目前已知的最好水平。     

毛细管反相液相色谱-串联质谱用于肽的鉴定和相对定量分析

建立了一种基于毛细管反相液相色谱-串联质谱联用技术和质谱峰强度数据处理的肽段鉴定和相对定量分析方法。该方法无需对样品中的肽进行化学标记,在对样品进行反相色谱分离和串联质谱分析后,将二级质谱扫描数据进行蛋白质数据库搜索,获得所鉴定肽段的序列、保留时间、质荷比、带电荷数等定性信息;再以此为定位依据,在全扫描质谱数据中提取该肽段对应的离子峰并以该离子峰的峰强度作为定量信息,从而实现对不同样品中的共有肽段进行差异比较分析。以标准蛋白酶解混合肽段为实验对象,以肽段相对强度的相对标准偏差为指标,考察了该方法用于肽段相对定量分析的重现性、检测动态范围以及浓度标准曲线等,为将该方法用于生物样品中内源性肽的差异分析奠定了基础。     

依赖色谱保留时间的智能化选择反应监测质谱方法的建立及其初步应用

建立了依赖色谱保留时间的智能化选择反应监测质谱方法,并与非依赖色谱保留时间的智能化选择反应监测质谱分析方法对不同体系(牛血清白蛋白酶切物、6种标准蛋白质混合物酶切物、腾冲嗜热菌蛋白提取液酶切物)的分析结果进行了系统比较。结果表明,引入色谱保留时间后的智能化选择反应监测质谱方法能够显著提高肽段及蛋白质的鉴定量,并且在复杂体系（如腾冲嗜热菌蛋白提取液酶切物）中效果尤为明显,鉴定到的肽段及蛋白质的覆盖率可分别达到目标肽段和蛋白质数量的89.62%和92.41%,并且灵敏度高、重复性好,能够实现对质荷比相同但保留时间有差异的肽段的准确鉴定。该方法将在复杂生物样本目标蛋白质组高通量、高灵敏度的鉴定、验证和确认中发挥独特作用。     

基于保留时间和质荷比匹配的液相色谱-质谱联用技术用于非标记肽段的差异分析

建立了一种无需化学标记的,基于纳升级毛细管液相色谱-电喷雾离子阱质谱联用技术和质谱数据处理的肽段差异分析方法。本方法采用定量差异分析与肽序列鉴定分析分别进行的策略,首先对样品进行质谱全扫描的液质全谱式分析,在全扫描质谱数据中提取肽特征点信息,通过保留时间和质荷比参数匹配不同样品中的共有肽特征点,比较其相对峰强度有无差异。最后对样品中存在丰度差异的肽特征点进行选择性二级质谱分析和序列鉴定,从而实现复杂样品中肽段的差异比较分析。以血浆蛋白酶解混合物为实验对象,考察了本方法用于肽段相对定量分析的重现性以及浓度信号响应曲线等。结果表明:提取的肽特征点峰强度相对标准偏差的中值<22%,肽段离子强度动态范围达3个数量级,在5～1000fmol范围内对肽段定量具有良好线性关系。本方法可用于不同条件样品中具有倍数差异的内源性肽的比较分析。     

反相高效液相色谱法测定蟾酥中的3种蟾毒内酯

建立了一种基于毛细管反相液相色谱-串联质谱联用技术和质谱峰强度数据处理的肽段鉴定和相对定量分析方法。该方法无需对样品中的肽进行化学标记,在对样品进行反相色谱分离和串联质谱分析后,将二级质谱扫描数据进行蛋白质数据库搜索,获得所鉴定肽段的序列、保留时间、质荷比、带电荷数等定性信息;再以此为定位依据,在全扫描质谱数据中提取该肽段对应的离子峰并以该离子峰的峰强度作为定量信息,从而实现对不同样品中的共有肽段进行差异比较分析。以标准蛋白酶解混合肽段为实验对象,以肽段相对强度的相对标准偏差为指标,考察了该方法用于肽段相对定量分析的重现性、检测动态范围以及浓度标准曲线等,为将该方法用于生物样品中内源性肽的差异分析奠定了基础。。     

蛋白质组学质谱平台肽段可检测性预测研究进展

生物质谱是蛋白质组研究中的核心技术之一,可以实现大规模、高通量的蛋白质定性和定量分析。由于样品和实验过程自身的复杂性,质谱实验的重复性还存在一些问题,肽段鉴定和定量结果有很大的随机性,肽段的质谱检测概率问题在蛋白质组研究中,特别是定量蛋白质组研究中备受关注。本文总结了影响肽段可检测性的重要因素,分析了已经提出的计算预测方法,并对其在实验研究中的应用进行了综述。     

规模化蛋白质生物质谱鉴定中肽段氨基端环化修饰现象

对蛋白质样品制备中引入的氨基酸残基的一种现象--蛋白质酶切肽段氨基端的环化修饰现象的初步研究结果显示,很多以谷氨酰胺(Q)或氨乙酰化修饰的半胱氨酸(CAM_C)残基起始的肽段会发生氨基端的环化修饰,且修饰反应不完全,在同一样本中修饰与非修饰两种状态常同时存在,并且环化修饰后的肽段的反相色谱保留时间发生延迟.在数据库检索时添加环化修饰,可以提高蛋白质的鉴定成功率.本研究结果为大规模的蛋白质质谱数据解析提供了有价值的参考.     

色谱保留时间在蛋白质组研究中的应用

液相色谱与串联质谱联用(LC-MS/MS)技术是蛋白质组学研究中的常见方法。保留时间作为独立于质谱信息的参数已经被用于蛋白质的鉴定和定量工作中。在多肽鉴定领域,多肽的色谱保留时间预测与常规的二级串联质谱数据库搜索算法结合可以提高鉴定的可信度。鉴定的灵敏度也可以通过匹配多次LC-MS实验中具有相同精确质量数和保留时间的峰而提高。另一方面,由于色谱条件的微小改变即会引起保留时间的变化,因此对多次实验结果进行保留时间比对是进行非标记定量的不可或缺的步骤。另外,联合保留时间偏移和质量数信息还可以进行蛋白质翻译后修饰(post-translational modification, PTM)的鉴定。     

互补多酶解法在蛋白质C末端质谱检测中的应用

建立了互补型多酶解法与串联质谱联用鉴定蛋白C末端技术.在大量蛋白的实际检测中,根据蛋白序列分别采用溴化氰、胰蛋白酶、谷氨酸内切酶和糜蛋白酶进行酶解或混合酶解.利用此技术对8个蛋白不同长度的C末端肽段(分子量分布在200～3000 Da之间,目的肽段分别为m/z 272.20,788.45,796.48,944.58,1...     

Prediction of peptide retention time in reversed-phase high-performance liquid chromatography.

Peptide retention in reversed-phase chromatography depends mainly on the amino acid composition of peptides and can therefore be predicted by summing the relative hydrophobic contributions of each constitutive amino acid residue. The prediction is correct for small peptides but overestimates the retention times of peptides larger than 10-15 residues. A new prediction model is proposed in which the contribution to peptide retention of each amino acid residue is not a constant but a decreasing function of peptide length. From the retention times of 104 peptides, the parameters of decreasing functions were estimated by a non-linear multiple regression analysis. The contribution to peptide retention of charged, polar and non-polar residues appears to be differently affected by peptide length. The secondary structure of most peptides during reversed-phase high-performance liquid chromatography could be responsible for this. The high correlation between the predicted and observed retention times of peptides which were not used to establish the model indicates a good predictive accuracy of the new model.     

Retention Characteristics of Peptides in RP-LC: Peptide Retention Prediction

A review of recent results of the use of chromatographic retention data in peptide identification and in the development of procedures for peptide retention prediction is presented. In recent years, reversed phase LC (RP-LC) has become an important tool in the separation of peptides in MS analysis. A challenging problem in a further expansion of RP-LC applications is the use of already available retention information for the identification purposes simultaneously with MS–MS identification. This overview focuses on the retention characteristics suggested in LC. We will discuss the application of the retention index concept in LC, which is widely used in GC to characterize retention of organic compounds. The use of retention indices as retention characteristics of analytes in LC was first suggested at the end of 1970s, however the application of retention indices is still somewhat rare today. There are several reasons for this. One is the relatively high sensitivity and variability of retention indices to the change of parameters of chromatographic systems. Another is the chemical restrictions in the search of the universal set of reference compounds suitable for retention scaling. Several methods were suggested for the prediction of the retention times of peptides. A frequently used approach is based on the additivity scheme and calculation of the elution time through the summation of retention coefficients of amino acids constituting the peptide. Such an approach allows fairly accurate predictions of the retention time of peptides made up of not more then 15–20 amino acid residues. Additional correction factors were suggested to improve predictions including corrections for the peptide length, peptide hydrophobicity, sequence of amino acids, etc. Suggested procedures are discussed in detail. Application of predicted retention times in the identification of peptides is considered. Current status of LC retention data collections is presented.     

Prediction of Peptide Retention in RP-LC

The retention of small peptides can be predicted by summing the hydrophobic contribution to retention of each amino acid of peptides. But the retention time of peptides larger than 10–15 residues are less than that predicted by summing the retention coefficients of each constitutive residue. A new prediction model, considering the effects of the peptide length and contact area of each amino acid with the stationary phase for larger peptides was proposed. The model was validated by 136 peptides identified by nano-flow 2-D-LC-ESI-MS-MS platform and other retention data observed from literature. The high degree of correlation between the observed and predicted retention time by using the new model is not only good evidence for the accuracy of our predictive method, but supports the supposition that the peptide length and contact area of each amino acid with stationary phase are important factors affecting peptide retention time for larger peptide. In addition it is found that the range of peptide length is wider, the accuracy of prediction is better. The ratio coefficient of surface area of non-polar, polar and charged amino residues contacting with the stationary phase were all calculated to be less than one. Revised: 30 June and 11 August 2005     

Retention prediction of small peptides in reversed-phase liquid chromatography

Summary Retention prediction of small peptides (up to four residues) in reversed-phase liquid chromatography has been investigated, considering the contributions of side chains in each position to the peptide retention. In isocratic elution the retention of peptides could be predicted within about 8% relative error.     

Alternative methods for verifying the results of the mass spectrometric identification of peptides in shotgun proteomics

Database search is the most popular approach used for the identification of peptides in contemporary shotgun proteomics; it utilizes only mass spectrometric data. In this work, we introduce three criteria for the verification of peptide identification; these are based on the analysis of data orthogonal to tandem mass spectra. The first one utilizes chromatographic retention times of peptides. The development of such approaches has been hindered by the relatively low accuracy of retention time prediction algorithms. In this work, we suggest the use of two independent models of the liquid chromatography of peptides, which increase the reliability of the results. The second criterion utilizes the mean number of missed tryptic cleavages per peptide. The third one results from the analysis of the difference between theoretical and experimentally measured peptide masses. The proposed criteria were applied to the tandem mass spectra of tryptic peptides from rat kidney tissue, which were processed by the Mascot search engine. All the criteria showed that Mascot significantly overestimated the reliability of an identification. This conclusion was supported by the PeptideProphet algorithm.     

Computer-assisted prediction of small peptide sequencing in reversed-phase liquid chromatography

Summary A computer-assisted prediction system for small peptide sequencing has been constructed based on the retention prediction approach in reversed-phase liquid chromatography. The system has the basic function to predict retention of small peptides at any chromatographic conditions and this function can be useful for the prediction of sequencing. The outline of the system construction and the performance is discussed.     

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.     

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: in silico studies

A two-dimensional (2-D) liquid chromatography (LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2-D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2-D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases.