Changming Xu

LFQuant: A label-free fast quantitative analysis tool for high-resolution LC-MS/MS proteomics data

Database searching based methods for label‐free quantification aim to reconstruct the peptide extracted ion chromatogram based on the identification information, which can limit the search space and thus make the data processing much faster. The random effect of the MS/MS sampling can be remedied by cross‐assignment among different runs. Here, we present a new label‐free fast quantitative analysis tool, LFQuant, for high‐resolution LC‐MS/MS proteomics data based on database searching. It is designed to accept raw data in two common formats (mzXML and Thermo RAW), and database search results from mainstream tools (MASCOT, SEQUEST, and X!Tandem), as input data. LFQuant can handle large‐scale label‐free data with fractionation such as SDS‐PAGE and 2D LC. It is easy to use and provides handy user interfaces for data loading, parameter setting, quantitative analysis, and quantitative data visualization. LFQuant was compared with two common quantification software packages, MaxQuant and IDEAL‐Q, on the replication data set and the UPS1 standard data set. The results show that LFQuant performs better than them in terms of both precision and accuracy, and consumes significantly less processing time. LFQuant is freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/lfquant/.

A Proteomics Strategy for the Identification of FAT10-Modified Sites by Mass Spectrometry

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10 gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.

Functional Proteomics Study Reveals SUMOylation of TFII-I is Involved in Liver Cancer Cell Proliferation

SUMOylation has emerged as a new regulatory mechanism for proteins involved in multiple physiological and pathological processes. However, the detailed function of SUMOylation in liver cancer is still elusive. This study reveals that the SUMOylation-activating enzyme UBA2 is highly expressed in liver cancer cells and clinical samples. Silencing of UBA2 expression could to some extent suppress cell proliferation. To elucidate the function of UBA2, we used a large scale proteomics strategy to identify SUMOylation targets in HepG2 cells. We characterized 827 potential SUMO1-modified proteins that were not present in the control samples. These proteins were enriched in gene expression processes. Twelve candidates were validated as SUMO1-modified proteins by immunoprecipitation-Western blotting. We further characterized SUMOylated protein TFII-I that was identified in this study and determined that TFII-I was modified by SUMO1 at K221 and K240. PIAS4 was an E3 ligase for TFII-I SUMOylation, and SENP2 was responsible for deSUMOylating TFII-I in HepG2 cells. SUMOylation reduced TFII-I binding to its repressor HDAC3 and thus promoted its transcriptional activity. We further show that SUMOylation is critical for TFII-I to promote cell proliferation and colony formation. Our findings contribute to understanding the role of SUMOylation in liver cancer development.

FTDR 2.0: a tool to achieve sub-ppm level recalibrated accuracy in routine LC-MS analysis

Advances in proteomics research involve the use of high-precision and high-resolution mass spectrometry instruments. Although hardware improvements are the main impetus for the acquisition of high-quality data, enhancements in software tools are also needed. In this study, recalibration was verified as an important way to improve data accuracy. A new version tool, known as FTDR 2.0, was developed to recalibrate the mass-to-charge ratio error of most observed parent ions to the sub part per million level in routine experiments. First, many new parameters were introduced and screened as features online to reduce systematic error and to adapt to various data sets. Second, a support vector regression model was trained to characterize the complex nonlinear maps from features to mass-to-charge ratio measurement errors. Third, a specific mass-to-charge ratio error tolerance for each parent ion was estimated by considering the impact of signal intensity. FTDR 2.0 is a user-friendly tool that supports most commonly used data standards and formats. A C++ library and the source code are provided to support the redevelopment and integration into other mass spectrometry data processing tools. The performance of FTDR 2.0 was verified using several experimental data sets from different research programs. Recalibration with FTDR 2.0 has been proved to improve the peptide identification in qualitative, quantitative, and post-translational modification analyses.

Quantitative and In-Depth Survey of the Isotopic Abundance Distribution Errors in Shotgun Proteomics.

Accuracy is an important metric when mass spectrometry (MS) is used in large-scale quantitative proteomics research. For MS-based quantification by extracting ion chromatogram (XIC), both the mass and intensity dimensions must be accurate. Although much research has focused on mass accuracy in recent years, less attention has been paid to intensity errors. Here, we investigated signal intensity measurement errors systematically and quantitatively using the natural properties of isotopic distributions. First, we defined a normalized isotopic abundance error model and presented its merits and demerits. Second, a comprehensive survey of the isotopic abundance errors using data sets with increasing sample complexities and concentrations was performed. We examined parameters such as error distribution, relationships between signal intensities within one isotopic cluster, and correlations between different peak errors in isotopic profiles. Our data demonstrated that the high resolution MS platforms might also generate large isotopic intensity measurement errors (approximately 20%). Meanwhile, this error can be reduced to less than 5% using a novel correction algorithm, which is based on the theoretical isotopic abundance distribution. Finally, a nonlinear relationship was observed as the abundance error decreased in isotopic profiles with higher intensity. Our findings are expected to provide insight into isotopic abundance recalibration in quantitative proteomics.

An improved workflow for identifying ubiquitin/ubiquitin‐like protein conjugation sites from tandem mass spectra

The identification of ubiquitin (Ub) and Ub‐like protein (Ubl) conjugation sites is important in understanding their roles in biological pathway regulations. However, unambiguously and sensitively identifying Ub/Ubl conjugation sites through high‐throughput MS remains challenging. We introduce an improved workflow for identifying Ub/Ubl conjugation sites based on the ChopNSpice and X!Tandem software. ChopNSpice is modified to generate Ub/Ubl conjugation peptides in the form of a cross‐link. A combinatorial FASTA database can be acquired using the modified ChopNSpice (MchopNSpice). The modified X!Tandem (UblSearch) introduces a new fragmentation model for the Ub/Ubl conjugation peptides to match unambiguously the MS/MS spectra with linear peptides or Ub/Ubl conjugation peptides using the combinatorial FASTA database. The novel workflow exhibited better performance in analyzing an Ub and Ubl spectral library and a large‐scale Trypanosoma cruzi small Ub‐related modifier dataset compared with the original ChopNSpice method. The proposed workflow is more suitable for processing large‐scale MS datasets of Ub/Ubl modification. MchopNSpice and UblSearch are freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/maublsearch.

TVNovo: De novo peptide sequencing for high resolution LTQ-FT mass spectrometry using virtual database searching

De novo peptide sequencing is one of the most challenging topics in the field of computational proteomics. In this manuscript, a novel method based on virtual database searching is presented to improve the performance of de novo sequencing for the data from high resolution LTQ-FT mass spectrometry. Our method directly generates a virtual database from each spectrum and applies a search engine to match spectrum against the calculated virtual database. Two datasets from different sources are employed to compare our method to other existing de novo sequencing algorithms and the results show that our method outperforms other methods.

Predicting potential disease-related genes using the network topological features

To help the biomedical scientist pre-confirm the disease-related genes, we considered these gene as a whole research set and analyzed the topological features of their interaction network. Two strategies had been proposed to construct the disease-related gene network from the OMIM database. Using these two constructed sets, we trained two support vector machine prediction models, the accuracy of which are 75.09% and 83.63%. As a result, we gained 27 and 2873 potential disease-related genes respectively. The intersection of the two predicted sets contains 19 genes. In addition, gene locuses with high appearance frequency were listed for further research.