基于质谱的定量蛋白质组学策略和方法研究进展

定量蛋白质组学已经成为组学领域研究的热点之一.相关实验技术和计算方法的不断创新极大地促进了定量蛋白质组学的飞速发展.常用的定量蛋白质组学策略按照是否需要稳定同位素标记可以分为无标定量和有标定量两大类.每类策略又产生了众多定量方法和工具,它们一方面推动了定量蛋白质组学的深入发展;另一方面,也在实验策略与技术的发展过程中不断更新.因此对这些定量实验策略和方法进行系统总结和归纳将有助于定量蛋白质组学的研究.本文主要从方法学角度全面归纳了目前定量蛋白质组学研究的相关策略和算法,详述了无标定量和有标定量的具体算法流程并比较了各自特点,还对以研究蛋白质绝对丰度为目标的绝对定量算法进行了总结,列举了常用的定量软件和工具,最后概述了定量结果的质量控制方法,对定量蛋白质组学方法发展的前景进行了展望.     

基于多次重复液相质谱生物实验数据校准方法研究

在蛋白质组学中,进行液相质谱(LC-MS)实验谱数据处理,发现并分析生物标志物的复杂肽或蛋白质样本的差异是重点,而校准相同样本的多次重复实验中肽链产生的洗脱时间峰信号(LC峰)是进行量化、分析差异的关键。目前多个重复实验数据的校准通常是在重复的实验数据集中根据液相二级质谱(LC-MS/MS)实验标识LC峰的时间特征,然后使用翘曲函数对时间特征进行对齐。由于多重数据的洗脱时间误差产生是随机的,统一使用翘曲函数校准会产生较大误差。为了解决这个问题,本研究重点研究了多个重复实验数据中LC峰的时间校准算法。我们选取了两个重复实验数据,采用机器学习的思路,通过选用两个数据的LC-MS/MS中重复检测到的肽链数据作为可信数据,部分选为训练序列,部分作为测试序列,建立统计数学模型,提出了一种新的校准算法,并采用测试序列对该统计模型进行准确率测试,表明算法的准确性达到95%以上;然后,将该模型应用在两个实验数据的所有LC-MS/MS肽链检测值上,提高检测值在多个数据中的覆盖率,表明覆盖率可以到达85%以上。     

评价新型稳定同位素赖氨酸标记在定量蛋白质组学中的应用

为了评价基于2-甲氧基-4,5-二氢-1氢-咪唑稳定同位素试剂在定量蛋白质组学中的应用价值,合成了轻型(D0)和重型(D4)的2-甲氧基-4,5-二氢-1氢-咪唑,通过对标准蛋白BSA酶解后产物的标记确认标记反应的特异性,并观察了标记物在MALDI-TOF-MS和LC-ESI-MS中定量的准确性,标记肽在串联质谱中的离子特点,以及对反相液相色谱行为的影响。结果表明,2-甲氧基-4,5-二氢-1氢-咪唑只与酶解后的肽段赖氨酸侧链氨基反应,具有良好的标记特异性;差异表达蛋白的定量可以通过MALDI和ESI电离模式实现;标记肽的串联质谱主要产生y离子,测序更为简便;反相液相色谱可以保持较好的分离效果,氘原子的引入不会影响保留时间,侧链修饰可以用于涉及液相色谱分离的蛋白质组学技术。2-甲氧基-4,5-二氢-1氢-咪唑稳定同位素试剂可以用于定量蛋白质组学。     

胰蛋白酶镜像酶LysargiNase的开发及其在蛋白质组学研究中的应用

蛋白质组学是系统鉴定、定量蛋白质及其翻译后修饰形式,并研究这些蛋白质生物学功能的学科。目前,基于质谱的鸟枪法蛋白质组学技术是蛋白质组学研究的主要手段之一,其技术流程是先将蛋白质组样品经位点特异性蛋白酶消化形成肽组,再进行高效液相色谱分离和质谱检测。而位点特异性蛋白酶对蛋白质样品的消化是质谱检测的前提和基础。随着蛋白质组学研究的深入,多种位点特异性蛋白酶被先后开发利用;而切割发生在相应氨基酸的N端,与传统的C端蛋白酶互为镜像的蛋白酶的鉴定、开发、特性研究和广泛使用更是为蛋白质组学研究提供了新的工具。文中对最近发现的胰蛋白酶的镜像酶——赖氨酸精氨酸N端蛋白酶(LysargiNase)的特点及其应用进行综述,为国内外学者更加广泛的使用创造条件。     

蛋白质组学分离检测技术研究进展

蛋白质组学是后基因组时代的新兴学科,是当今生命科学领域新的增长点,而其中的分离检测技术则是蛋白质组学得以迅速发展的重要基石。对蛋白质组学中的分离检测技术-双向凝胶电泳、色谱和质谱等技术近几年的发展现状及最新研究进展进行综述,并对本实验室在蛋白质组学方面的研究结合生物信息学的探索进行概述。     

蛋白质组学技术在植物类囊体膜蛋白研究中的应用

类囊体作为植物光合作用光反应的重要场所,在植物亚细胞蛋白质组学研究中倍受关注.介绍了植物蛋白质组学相关技术,包括双向凝胶电泳(2DE)、高效液相色谱(HPLC)、高效毛细管电泳(HPCE)、质谱(MS)和蛋白质组学数据库在植物类囊体膜蛋白研究中的应用.同时对类囊体膜蛋白质组学的研究趋势进行了探讨.     

植物蛋白质组学研究进展Ⅰ. 蛋白质组关键技术

随着模式植物拟南芥和水稻基因组测序相继完成, 使植物基因组学研究成功迈入到功能基因组学研究的时代。这为蛋白质组学产生及其发展奠定了坚实的基础。文章重点介绍了蛋白质组学的概念、产生背景和蛋白质组学的关键技术。蛋白质组学的关键技术包括双向电泳、高效液相色谱、蛋白芯片、质谱技术、蛋白质组学的相关数据库、定量蛋白组技术、蛋白复合体标签亲和纯化技术和酵母双杂交系统。同时对当前蛋白质组技术面临的挑战和发展前景进行了讨论。     

蛋白质组学技术及其在心血管疾病研究中的应用

蛋白质组学是旨在研究蛋白质表达谱和蛋白质与蛋白质之间相互作用的新的领域。蛋白质组学的研究必须依赖高通量、自动化程度很高的技术。双向电泳、液相色谱和生物质谱技术的发展推动了蛋白质组学的研究。蛋白质组学为疾病发病机制的研究提供了新的思路和方法 ,本文重点介绍了蛋白质组学技术在心血管疾病研究中的应用     

蛋白质组学在木薯育种中的应用

蛋白质组学通过研究整体蛋白质活动来揭示生命运动规律,是解析功能基因组表达的必要手段。目前蛋白质组学为木薯选育种研究提供了重要手段和新思路。介绍了蛋白质组学研究平台并对其在选育高光效高淀粉积累、高蛋白质、高类胡萝卜素及抗逆木薯种质方面的应用进行了综述。     

非模式植物蛋白质组学研究进展

蛋白质组学研究是对基因组学研究的重要补充,它是在蛋白质水平定量、动态、整体性研究生物体。该文简要介绍了蛋白质组学的含义,蛋白质组学及植物蛋白质组学产生的科学背景,蛋白质组学的研究内容。概述了非模式植物蛋白质组学的研究进展,主要包括非模式植物个体及群体蛋白质组学,组织和器官蛋白质组学,亚细胞蛋白质组学,响应环境变化的蛋白质组学以及非模式植物生物环境因子的蛋白质组学的研究情况,同时对植物蛋白质组学的发展前景进行了展望。     

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

Background  

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.     

Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments

MOTIVATION: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool in proteomics studies, but when peptide retention information is used for identification purposes, it remains challenging to compare multiple LC-MS/MS runs or to match observed and predicted retention times, because small changes of LC conditions unavoidably lead to variability in retention times. In addition, non-contiguous retention data obtained with different LC-MS instruments or in different laboratories must be aligned to confirm and utilize rapidly accumulating published proteomics data. RESULTS: We have developed a new alignment method for peptide retention times based on linear solvent strength (LSS) theory. We found that log k(0) (logarithm of retention factor for a given organic solvent) in the LSS theory can be utilized as a 'universal' retention index of peptides (RIP) that is independent of LC gradients, and depends solely on the constituents of the mobile phase and the stationary phases. We introduced a machine learning-based scheme to optimize the conversion function of gradient retention times (t(g)) to log k(0). Using the optimized function, t(g) values obtained with different LC-MS systems can be directly compared with each other on the RIP scale. In an examination of Arabidopsis proteomic data, the vast majority of retention time variability was removed, and five datasets obtained with various LC-MS systems were successfully aligned on the RIP scale.     

Improved de novo peptide sequencing using LC retention time information

Background

Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence.

Results

We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model.

Conclusions

Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.

     

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

Background  

Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.     

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.     

An ultra-fast metabolite prediction algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.     

A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics

Background  

In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.