利用串联质谱鉴定氨基酸突变的生物信息学算法

氨基酸突变能够改变蛋白的结构和功能,影响生物体的生命过程.基于串联质谱的鸟枪法蛋白质组学是目前大规模研究蛋白质组学的主要方法,但是现有的质谱数据鉴定流程为了提高鉴定结果的灵敏度往往会有意压缩数据库中的氨基酸突变信息.因此,如何挖掘数据中的氨基酸突变信息成为当前质谱数据鉴定的一个重要部分.当前应用于氨基酸突变鉴定的串联质谱鉴定方法大致可以分为3大类:基于序列数据库搜索的方法、基于序列标签搜索的算法以及基于图谱库搜索的算法.本文首先详细介绍了这3种氨基酸突变鉴定算法,并分析了各种方法的特点和不足,然后介绍了氨基酸突变鉴定的研究现状和发展方向.随着基于串联质谱的蛋白质组学的不断发展,蛋白序列中的氨基酸突变信息将被更好地解析出来,从而得以深入探讨由氨基酸突变引起的蛋白结构和功能改变,为揭示氨基酸突变的生物学意义奠定基础.     

A bioinformatics workflow for variant peptide detection in shotgun proteomics

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics.     

Terminal proteomics: N- and C-terminal analyses for high-fidelity identification of proteins using MS

In proteomics, MS plays an essential role in identifying and quantifying proteins. To characterize mature target proteins from living cells, candidate proteins are often analyzed with PMF and MS/MS ion search methods in combination with computational search routines based on bioinformatics. In contrast to shotgun proteomics, which is widely used to identify proteins, proteomics based on the analysis of N- and C-terminal amino acid sequences (terminal proteomics) should render higher fidelity results because of the high information content of terminal sequence and potentially high throughput of the method not requiring very high sequence coverage to be achieved by extensive sequencing. In line with this expectation, we review recent advances in methods for N- and C-terminal amino acid sequencing of proteins. This review focuses mainly on the methods of N- and C-terminal analyses based on MALDI-TOF MS for its easy accessibility, with several complementary approaches using LC/MS/MS. We also describe problems associated with MS and possible remedies, including chemical and enzymatic procedures to enhance the fidelity of these methods.     

Detection and validation of non-synonymous coding SNPs from orthogonal analysis of shotgun proteomics data

Orthogonal analysis of amino acid substitutions as a result of SNPs in existing proteomic datasets provides a critical foundation for the emerging field of population-based proteomics. Large-scale proteomics datasets, derived from shotgun tandem MS analysis of complex cellular protein mixtures, contain many unassigned spectra that may correspond to alternate alleles coded by SNPs. The purpose of this work was to identify tandem MS spectra in LC-MS/MS shotgun proteomics datasets that may represent coding nonsynonymous SNPs (nsSNP). To this end, we generated a tryptic peptide database created from allelic information found in NCBI's dbSNP. We searched this database with tandem MS spectra of tryptic peptides from DU4475 breast tumor cells that had been fractioned by pI in the first-dimension and reverse-phase LC in the second dimension. In all we identified 629 nsSNPs, of which 36 were of alternate SNP alleles not found in the reference NCBI or IPI protein databases. Searches for SNP-peptides carry a high risk of false positives due both to mass shifts caused by modifications and because of multiple representations of the same peptide within the genome. In this work, false positives were filtered using a novel peptide pI prediction algorithm and characterized using a decoy database developed by random substitution of similarly sized reference peptides. Secondary validation by sequencing of corresponding genomic DNA confirmed the presence of the predicted SNP in 8 of 10 SNP-peptides. This work highlights that the usefulness of interpreting unassigned spectra as polymorphisms is highly reliant on the ability to detect and filter false positives.     

Enhanced recovery of lyophilized peptides in shotgun proteomics by using an LC‐ESI‐MS compatible surfactant

LC‐ESI/MS/MS‐based shotgun proteomics is currently the most commonly used approach for the identification and quantification of proteins in large‐scale studies of biomarker discovery. In the past several years, the shotgun proteomics technologies have been refined toward further enhancement of proteome coverage. In the complex series of protocols involved in shotgun proteomics, however, loss of proteolytic peptides during the lyophilization step prior to the LC/MS/MS injection has been relatively neglected despite the fact that the dissolution of the hydrophobic peptides in lyophilized samples is difficult in 0.05–0.1% TFA or formic acid, causing substantial loss of precious peptide samples. In order to prevent the loss of peptide samples during this step, we devised a new protocol using Invitrosol (IVS), a commercially available surfactant compatible with ESI‐MS; by dissolving the lyophilized peptides in IVS, we show improved recovery of hydrophobic peptides, leading to enhanced coverage of proteome. Thus, the use of IVS in the recovery step of lyophilized peptides will help the shotgun proteomics analysis by expanding the proteome coverage, which would significantly promote the discovery and development of new diagnostic markers and therapeutic targets.     

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。     

Protein interaction mapping on a functional shotgun sequence of Rickettsia sibirica

Protein interaction maps can reveal novel pathways and functional complexes, allowing ‘guilt by association’ annotation of uncharacterized proteins. To address the need for large-scale protein interaction analyses, a bacterial two-hybrid system was coupled with a whole genome shotgun sequencing approach for microbial genome analysis. We report the first large-scale proteomics study using this system, integrating de novo genome sequencing with functional interaction mapping and annotation in a high-throughput format. We apply the approach by shotgun sequencing and annotating the genome of Rickettsia sibirica strain 246, an obligate intracellular human pathogen among the Spotted Fever Group rickettsiae. The bacteria invade endothelial cells and cause lysis after large amounts of progeny have accumulated. Little is known about specific Rickettsial virulence factors and their mode of pathogenicity. Analysis of the combined genomic sequence and protein–protein interaction data for a set of virulence related Type IV secretion system (T4SS) proteins revealed over 250 interactions and will provide insight into the mechanism of Rickettsial pathogenicity.     

Proteomics-grade de novo sequencing approach

The conventional approach in modern proteomics to identify proteins from limited information provided by molecular and fragment masses of their enzymatic degradation products carries an inherent risk of both false positive and false negative identifications. For reliable identification of even known proteins, complete de novo sequencing of their peptides is desired. The main problems of conventional sequencing based on tandem mass spectrometry are incomplete backbone fragmentation and the frequent overlap of fragment masses. In this work, the first proteomics-grade de novo approach is presented, where the above problems are alleviated by the use of complementary fragmentation techniques CAD and ECD. Implementation of a high-current, large-area dispenser cathode as a source of low-energy electrons provided efficient ECD of doubly charged peptides, the most abundant species (65-80%), in a typical trypsin-based proteomics experiment. A new linear de novo algorithm is developed combining efficiency and speed, processing on a conventional 3 GHz PC, 1000 MS/MS data sets in 60 s. More than 6% of all MS/MS data for doubly charged peptides yielded complete sequences, and another 13% gave nearly complete sequences with a maximum gap of two amino acid residues. These figures are comparable with the typical success rates (5-15%) of database identification. For peptides reliably found in the database (Mowse score > or = 34), the agreement with de novo-derived full sequences was >95%. Full sequences were derived in 67% of the cases when full sequence information was present in MS/MS spectra. Thus the new de novo sequencing approach reached the same level of efficiency and reliability as conventional database-identification strategies.     

A high-throughput <Emphasis Type="Italic">de novo</Emphasis> sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

Background  

High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms.     

Expanding the organismal scope of proteomics: cross-species protein identification by mass spectrometry and its implications

Due to the limited applicability of conventional protein identification methods to the proteomes of organisms with unsequenced genomes, researchers have developed approaches to identify proteins using mass spectrometry and sequence similarity database searches. Both the integration of mass spectrometry with bioinformatics and genomic sequencing drive the expanding organismal scope of proteomics.     

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.     

Protein identification and quantification from riverbank grape,Vitis riparia: Comparing SDS‐PAGE and FASP‐GPF techniques for shotgun proteomic analysis

Protein sample preparation optimisation is critical for establishing reproducible high throughput proteomic analysis. In this study, two different fractionation sample preparation techniques (in‐gel digestion and in‐solution digestion) for shotgun proteomics were used to quantitatively compare proteins identified in Vitis riparia leaf samples. The total number of proteins and peptides identified were compared between filter aided sample preparation (FASP) coupled with gas phase fractionation (GPF) and SDS‐PAGE methods. There was a 24% increase in the total number of reproducibly identified proteins when FASP‐GPF was used. FASP‐GPF is more reproducible, less expensive and a better method than SDS‐PAGE for shotgun proteomics of grapevine samples as it significantly increases protein identification across biological replicates. Total peptide and protein information from the two fractionation techniques is available in PRIDE with the identifier PXD001399 ( http://proteomecentral.proteomexchange.org/dataset/PXD001399 ).     

Perfluorooctanoic acid for shotgun proteomics

Here, we describe the novel use of a volatile surfactant, perfluorooctanoic acid (PFOA), for shotgun proteomics. PFOA was found to solubilize membrane proteins as effectively as sodium dodecyl sulfate (SDS). PFOA concentrations up to 0.5% (w/v) did not significantly inhibit trypsin activity. The unique features of PFOA allowed us to develop a single-tube shotgun proteomics method that used all volatile chemicals that could easily be removed by evaporation prior to mass spectrometry analysis. The experimental procedures involved: 1) extraction of proteins in 2% PFOA; 2) reduction of cystine residues with triethyl phosphine and their S-alkylation with iodoethanol; 3) trypsin digestion of proteins in 0.5% PFOA; 4) removal of PFOA by evaporation; and 5) LC-MS/MS analysis of the resulting peptides. The general applicability of the method was demonstrated with the membrane preparation of photoreceptor outer segments. We identified 75 proteins from 1 μg of the tryptic peptides in a single, 1-hour, LC-MS/MS run. About 67% of the proteins identified were classified as membrane proteins. We also demonstrate that a proteolytic (18)O labeling procedure can be incorporated after the PFOA removal step for quantitative proteomic experiments. The present method does not require sample clean-up devices such as solid-phase extractions and membrane filters, so no proteins/peptides are lost in any experimental steps. Thus, this single-tube shotgun proteomics method overcomes the major drawbacks of surfactant use in proteomic experiments.     

Shotgun protein sequencing: assembly of peptide tandem mass spectra from mixtures of modified proteins

Despite significant advances in the identification of known proteins, the analysis of unknown proteins by MS/MS still remains a challenging open problem. Although Klaus Biemann recognized the potential of MS/MS for sequencing of unknown proteins in the 1980s, low throughput Edman degradation followed by cloning still remains the main method to sequence unknown proteins. The automated interpretation of MS/MS spectra has been limited by a focus on individual spectra and has not capitalized on the information contained in spectra of overlapping peptides. Indeed the powerful shotgun DNA sequencing strategies have not been extended to automated protein sequencing. We demonstrate, for the first time, the feasibility of automated shotgun protein sequencing of protein mixtures by utilizing MS/MS spectra of overlapping and possibly modified peptides generated via multiple proteases of different specificities. We validate this approach by generating highly accurate de novo reconstructions of multiple regions of various proteins in western diamondback rattlesnake venom. We further argue that shotgun protein sequencing has the potential to overcome the limitations of current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.     

The proteomic response of Mycobacterium smegmatis to anti-tuberculosis drugs suggests targeted pathways

Mycobacterium smegmatis is a fast-growing model mycobacterial system that shares many features with the pathogenic Mycobacterium tuberculosis while allowing practical proteomics analysis. With the use of shotgun-style mass spectrometry, we provide a large-scale analysis of the M. smegmatis proteomic response to the anti-tuberculosis (TB) drugs isoniazid, ethambutol, and 5-chloropyrazinamide and elucidate the drugs' systematic effects on mycobacterial proteins. A total of 2550 proteins were identified with approximately 5% false-positive identification rate across 60 experiments, representing approximately 40% of the M. smegmatis proteome ( approximately 6500 proteins). Protein differential expression levels were estimated from the shotgun proteomics data, and 485 proteins showing altered expression levels in response to drugs were identified at a 99% confidence level. Proteomic comparison of anti-TB drug responses shows that translation, cell cycle control, and energy production are down-regulated in all three drug treatments. In contrast, systems related to the drugs' targets, such as lipid, amino acid, and nucleotide metabolism, show specific protein expression changes associated with a particular drug treatment. We identify proteins involved in target pathways for the three drugs and infer putative targets for 5-chloropyrazinamide.     

A workflow to increase the detection rate of proteins from unsequenced organisms in high-throughput proteomics experiments

We present and evaluate a strategy for the mass spectrometric identification of proteins from organisms for which no genome sequence information is available that incorporates cross-species information from sequenced organisms. The presented method combines spectrum quality scoring, de novo sequencing and error tolerant BLAST searches and is designed to decrease input data complexity. Spectral quality scoring reduces the number of investigated mass spectra without a loss of information. Stringent quality-based selection and the combination of different de novo sequencing methods substantially increase the catalog of significant peptide alignments. The de novo sequences passing a reliability filter are subsequently submitted to error tolerant BLAST searches and MS-BLAST hits are validated by a sampling technique. With the described workflow, we identified up to 20% more groups of homologous proteins in proteome analyses with organisms whose genome is not sequenced than by state-of-the-art database searches in an Arabidopsis thaliana database. We consider the novel data analysis workflow an excellent screening method to identify those proteins that evade detection in proteomics experiments as a result of database constraints.