Peptide-spectrum Matches Research Articles

InSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

Coupling High-Field Asymmetric Ion Mobility Spectrometry with Capillary Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry Improves Protein Identifications in Bottom-Up Proteomic Analysis of Low Nanogram Samples.

In this work, we pioneered the assessment of coupling high-field asymmetric waveform ion mobility spectrometry (FAIMS) with ultrasensitive capillary electrophoresis hyphenated with tandem mass spectrometry (CE-MS/MS) to achieve deeper proteome coverage of low nanogram amounts of digested cell lysates. An internal stepping strategy using three or four compensation voltages per analytical run with varied cycle times was tested to determine optimal FAIMS settings and MS parameters for the CE-FAIMS-MS/MS method. The optimized method applied to bottom-up proteomic analysis of 1 ng of HeLa protein digest standard identified 1314 ± 30 proteins, 4829 ± 200 peptide groups, and 7577 ± 163 peptide spectrum matches (PSMs) corresponding to a 16, 25, and 22% increase, respectively, over CE-MS/MS alone, without FAIMS. Furthermore, the percentage of acquired MS/MS spectra that resulted in PSMs increased nearly 2-fold with CE-FAIMS-MS/MS. Label-free quantitation of proteins and peptides was also assessed to determine the precision of replicate analyses from FAIMS methods with increased cycle times. Our results also identified from 1 ng of HeLa protein digest without any prior enrichment 76 ± 9 phosphopeptides, 18% of which were multiphosphorylated. These results represent a 46% increase in phosphopeptide identifications over the control experiments without FAIMS yielding 2.5-fold more multiphosphorylated peptides.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates.

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS2PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS2PIP was tailored toward tryptic peptides, we have here retrained MS2PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS2PIP models, DeepLC, and Percolator in one software package, MS2Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS2Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS2Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

Abstract 3916: Cyanobacterial natural product Sec61α inhibitors induce broad suppression of the glioma cell secretome

Abstract Coibamide A is a cyclopeptide natural product with potent antiproliferative activity against cultured human cancer cells and subcutaneous U87-MG glioblastoma tumors in mice. We have recently determined that coibamide A binds to the Sec61 translocon channel at a site that is close, but not identical, to the binding site of the peptide/polyketide natural product apratoxin A. As the majority of secreted proteins in mammalian cells are trafficked through the conventional secretory pathway via the Sec61 translocon, this raised the possibility that coibamide A and apratoxin A block import and secretion of different Sec61 client proteins. In the present study, we analyzed and compared the secretome of human U87-MG glioblastoma cells in the presence and absence of both marine natural products. U87-MG glioblastoma cells were treated with coibamide A (5 nM), apratoxin A (5 nM), or vehicle for 24 h. The growth media was then replaced with serum-free media for a further 24 h. Tryptic proteomic samples were generated from cell-free conditioned media and analyzed on a LC-coupled Orbitrap Fusion mass spectrometer. The high-resolution Orbitrap mass analyzer was used to both identify precursor ions for tandem mass (MS2) analysis in the ion trap mass analyzer and to quantitate those ions (MS1). Secreted proteins were identified from peptide–spectrum matches to the human protein database using a decoy database to assign an FDR and quantified from their MS1 ion intensities. Comparison of mass spectrometric data against the human secretome database revealed 175 proteins in the U87-MG cell secretome that were identified with high confidence. Differences between coibamide A and apratoxin A treatment were not significant, however both Sec61 inhibitors induced significant suppression of the U87-MG secretome relative to vehicle-treated cells. Treatment with coibamide A or apratoxin A resulted in the reduction of 75% of quantifiable secretome (131 proteins). Of the remaining 44 proteins, 34 are directed to intracellular organelles or contain a motif associated with retention in the endoplasmic reticulum and 10 were not suppressed with treatment but were hypothesized to be Sec61 clients. Malignant glioblastoma cells secrete proteins into the extracellular space and use complex intercellular signaling mechanisms for a survival advantage. Our results indicate that broad-acting inhibitors of Sec61-dependent co-translational translocation block the progression of a significant percentage of the U87-MG glioblastoma cell secretome including several proteins that have previously been detected as biomarkers in the cerebrospinal fluid, blood, or urine of brain tumor patients. Taken together, our work illustrates the feasibility of inducing a reversible suppression of numerous (undrugged) extracellular signals by targeting Sec61-dependent protein biogenesis in the secretory pathway with drug-like molecules. Citation Format: Daphne R. Mattos, Jeffrey D. Serrill, Philip R. Gafken, Kerry L. McPhail, Walter K. Vogel, Jane E. Ishmael. Cyanobacterial natural product Sec61α inhibitors induce broad suppression of the glioma cell secretome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3916.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

In-Depth In Vivo Crosslinking in Minutes by a Compact, Membrane-Permeable, and Alkynyl-Enrichable Crosslinker.

Chemical crosslinking coupled with mass spectrometry (CXMS) has emerged as a powerful technique to obtain the dynamic conformations and interaction interfaces of protein complexes. Limited by the poor cell membrane permeability, chemical reactivity, and biocompatibility of crosslinkers, in vivo crosslinking to capture the dynamics of protein complexes with finer temporal resolution and higher coverage is attractive but challenging. In this work, a trifunctional crosslinker bis(succinimidyl) with propargyl tag (BSP), involving compact size, proper amphipathy, and enrichment capacity, was developed to enable better cell membrane permeability and efficient crosslinking in 5 min without obvious cellular interference. Followed by a two-step enrichment method based on click chemistry at the peptide level, 13,098 crosslinked peptides (5068 inter-crosslinked peptides and 8030 intra-crosslinked peptides) were identified under the data threshold of peptide-spectrum matches (PSMs) ≥2 on the basic of the FDR control of 1%, which was the most comprehensive dataset for homo species cells by a non-cleavable crosslinker. Besides, the interactome network comprising 1519 proteins connected by 2913 interaction edges in various intracellular compartments, as well as 80S ribosome structural dynamics, were characterized, showing the great potential of our in vivo crosslinking approach in minutes. All these results demonstrated that our developed BSP could provide a valuable toolkit for the in-depth in vivo analysis of protein-protein interactions (PPIs) and protein architectures with finer temporal resolution.

Protein sequence databases generated from metagenomics and public databases produced similar soil metaproteomic results of microbial taxonomic and functional changes

Soil metaproteomics has excellent potential as a tool to elucidate the structural and functional changes in soil microbial communities in response to environmental alterations. However, soil metaproteomics is hindered by several challenges and gaps. Soil microbial communities possess extremely complex microbial composition, including many uncultured microorganisms without whole genome sequencing. Thus, how to select a suitable protein sequence database remains challenging in soil metaproteomics. In this study, the Public database and Meta-database were constructed using protein sequences from public databases and metagenomics, respectively. We comprehensively analyzed and compared the soil metaproteomic results using these two kinds of protein sequence databases for protein identification based on published soil metaproteomic raw data. The results demonstrated that many more proteins, higher sequence coverage, and even more microbial species and functional annotations could be identified using the Meta-database compared with those identified using the Public database. These findings indicated that the Meta-database was more specific as a protein sequence database. However, the follow-up in-depth metaproteomic analyses exhibited similar main results regardless of the database used. The microbial community composition at the genus level was similar between the two databases, especially the species annotations with high peptide-spectrum match and high abundance. The functional analyses in response to stress, such as the gene ontology enrichment of biological progress and molecular function and the key functional microorganisms, were also similar regardless of the database. Our analysis revealed that the Public database could also meet the demand to explore the functional responses of microbial proteins to some extent. This study provides valuable insights into the choice of protein sequence databases and their impacts on subsequent bioinformatic analysis in soil metaproteomic research and will facilitate the optimization of experimental design for different purposes.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line.

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide–spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Identification of alveolar proteins associated with claudin‐18 using BioID

Tight junctions are protein complexes that form at sites of cell contact that function to regulate paracellular flow of small molecules, water, and ions. Distinct protein components contribute to the barrier function of tight junctions including the claudin family of transmembrane proteins and scaffolding proteins such as Zonula Occludens‐1(ZO‐1). Changes in tight junction morphology from the established smooth cobblestone patterning to a spiked junction are associated with an increase in barrier permeability (leak). Tight junction spike formation is associated with changes in claudin‐18 interacting partners, including ZO‐1. This suggests that protein‐claudin‐18 interactions are essential for tight junction morphology and therefore function. Using rat alveolar epithelial cells (rAECs), we used BioID to investigate the proximal claudin‐18 proteome. BioID uses a promiscuous biotinylating enzyme, BirA, which we fused to claudin‐18 to biotinylate proximal and interacting proteins. To determine that BirA‐claudin‐18 localized to tight junctions, rAECs were transduced with adenovirus to express BirA‐claudin‐18 and stained with claudin‐18 and streptavidin‐Cy3. This showed that the assay was functional and our chimera trafficked to tight junctions and colocalized to proximal proteins. To identify biotinylated proteins, we used tandem mass spectrometry (MS/MS) and identified 3771 protein matches within the BirA‐claudin‐18 sample. Proteins of interest were vetted, and a list of protein candidates were compiled based on peptide spectrum matches (PSMs) and known protein localization. Proteins of interest include retinoic acid and palladin, which are of specific interest because they are associated with a spike like structure found in the testis, the tubulobulbar complex, which we hypothesize behaves as a signaling platform in the testis and is comparable to tight junction spikes found in the lung. These findings show that BioID is a valid method for identification of proteins to claudin‐18, providing a discovery‐based approach to identify novel proteins that play a role in barrier function and tight junction regulation.

Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation

Glycopeptides with unusual glycans or poor peptide backbone fragmentation in tandem mass spectrometry are unaccounted for in typical site-specific glycoproteomics analysis and thus remain unidentified. Here, we develop a glycoproteomics tool, Glyco-Decipher, to address these issues. Glyco-Decipher conducts glycan database-independent peptide matching and exploits the fragmentation pattern of shared peptide backbones in glycopeptides to improve the spectrum interpretation. We benchmark Glyco-Decipher on several large-scale datasets, demonstrating that it identifies more peptide-spectrum matches than Byonic, MSFragger-Glyco, StrucGP and pGlyco 3.0, with a 33.5%-178.5% increase in the number of identified glycopeptide spectra. The database-independent and unbiased profiling of attached glycans enables the discovery of 164 modified glycans in mouse tissues, including glycans with chemical or biological modifications. By enabling in-depth characterization of site-specific protein glycosylation, Glyco-Decipher is a promising tool for advancing glycoproteomics analysis in biological research.

TIDD: tool-independent and data-dependent machine learning for peptide identification

BackgroundIn shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.ResultsWe propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience.ConclusionsTIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

Fritted tip capillary column with negligible dead volume facilitated ultrasensitive and deep proteomics

Insufficient chromatographic performance results in reduced utilization of MS/MS scan capacity of advanced MS instruments. Improvement in peptide separation in liquid chromatography is critical for increasing the sensitivity and quantification performance of LC-MS-based proteomics. However, existing column fabrication methods suffer from slow packing, large dead volume, and band broadening. Herein, we reported that directly pulling emitter tips within short frits after fast packing (termed “filled tip”) can minimize the dead volume, improving ionization efficiency and reducing band broadening. Within 10 min, our method can pack over 10 cm for 50 μm I.D. capillary columns under 6–8 MPa and over 50 cm for 75 μm I.D. long capillary columns under 70 MPa. We can identify an average of 3043 protein groups and 33 309 peptide-spectrum matches (PSMs) from 1 ng of HeLa digest using a 50 μm I.D. x 20 cm “filled tip” column, with good reproducibility. The number of protein groups increased by 50% and 96% when compared with a 50 μm I.D. “void tip” column and a 100 μm I.D. column with a manually pulled tip, respectively. We identified an average of 5534 protein groups and 71 769 PSMs from 10 ng of HeLa digest. In addition, using 75 μm I.D. x 50 cm “filled tip” columns, we can identify on average 8829 protein groups and 170 751 PSMs in single-shot data-dependent acquisition analysis from 500 ng of 293T digested peptides. Importantly, good repeatability and reproducibility of “filled tip” method were verified by results from columns fabricated in three batches and by different persons. When compared with conventional columns with “void tips”, “filled tip” columns reduced median full peak widths by 19% and alleviated sampling redundancy by 10%. Collectively, we developed an easy-to-use, versatile and robust column fabrication method for both narrow-bore and long capillary columns, which achieved great sensitivity and depth in proteomic analysis.

Elucidation of the protein composition of mouse seminal vesicle fluid.

The seminal vesicles are male accessory sex glands that contribute the major portion of the seminal plasma in which mammalian spermatozoa are bathed during ejaculation. In addition to conveying sperm through the ejaculatory duct, seminal vesicle secretions support sperm survival after ejaculation, and influence the female reproductive tract to promote receptivity to pregnancy. Analysis of seminal vesicle fluid (SVF) composition by proteomics has proven challenging, due to its highly biased protein signature with a small subset of dominant proteins and the difficulty of solubilizing this viscous fluid. As such, publicly available proteomic datasets identify only 85 SVF proteins in total. To address this limitation, we report a new preparative methodology involving sequential solubilization of mouse SVF in guanidine hydrochloride, acetone precipitation, and analysis by label-free mass spectrometry. Using this strategy, we identified 126 SVF proteins, including 83 previously undetected in SVF. Members of the seminal vesicle secretory protein family were the most abundant, accounting for 79% of all peptide spectrum matches. Functional analysis identified inflammation and formation of the vaginal plug as the two most prominent biological processes. Other notable processes included modulation of sperm function and regulation of the female reproductive tract immune environment. Together, these findings provide a robust methodological framework for future SVF studies and identify novel proteins with potential to influence both male and female reproductive physiology.

Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.

In shotgun proteomics, false discovery rate (FDR) estimation is a necessary step to ensure the quality of accepted peptide-spectrum matches (PSMs) from a database search. Popular statistical validation tools for FDR control tend to rely on target-decoy searching to build empirical, dataset-specific models, which often leads to inaccurate FDR estimates. In this paper, we propose a new approach named common decoy distribution (CDD) to FDR estimation using the idea of a fixed empirical null score distribution derived from millions of peptide tandem mass spectra. To demonstrate the viability of CDD, its stability with respect to noise and the presence of unexpected peptide modifications was evaluated. PeptideProphet-based implementation of CDD was benchmarked against decoy-based PeptideProphet, and both methods exhibited similar accuracy of FDR estimates and retrieval of correct PSMs. The finding of this study calls for a re-evaluation of the necessity of dataset-specific target-decoy searches and illustrates the potential of Big Data approaches for statistical analysis in proteomics.

Aβ42 Expressing Drosophila melanogaster Model for Alzheimer's Disease: Quantitative Proteomics Identifies Altered Protein Dynamics of Relevance to Neurodegeneration.

Production and deposition of β-amyloid peptides (Aβ) are among the major hallmarks of the pathogenesis of Alzheimer's disease (AD). Mapping the altered protein dynamics associated with Aβ accumulation and neuronal damage may open up new avenues to innovation for drug target discovery in AD. Using quantitative proteomics, we report new findings from the amyloid beta-peptide with 42 amino acids (Aβ42) expressing Drosophila melanogaster model for AD compared to that of the wild-type flies. We identified 302,241 peptide-spectrum matches with 25,641 nonredundant peptides corresponding to 7959 D. melanogaster proteins. Furthermore, we unraveled 538 significantly altered proteins in Aβ42 expressing flies. These differentially expressed proteins were enriched for biological processes associated with neuronal damage leading to AD progression. We also identified 463 unique post-translational modification events mapping to 202 proteins from the same dataset. Among these, 303 modified peptides corresponding to 246 proteins were also altered in the AD model. These modified proteins are known to be involved in the disruption of molecular functions maintaining neuronal plasticity. This study provides new molecular leads on altered protein dynamics relevant to neurodegeneration, neuroplasticity, and AD progression induced by Aβ42 toxicity. These proteins may prove useful to discover new drugs in an AD model of D. melanogaster and evaluate their efficacy and mode of molecular action in the future.

基于精氨酸酶切的蛋白质C端肽段富集方法的优化及评估

基于聚合物的蛋白质C端反向富集策略是用于研究蛋白质C端最为广泛的策略之一。目前,基于胰蛋白酶(trypsin)切割精氨酸残基C端(ArgC型酶切)的蛋白C端组学方法对蛋白质C端的鉴定深度仍有待提高。为解决这一问题,该研究对此方法进行了优化和评估:建立了基于“V型”过滤装置的“一锅法”富集流程,避免了副反应的干扰,缩短了样本的制备时间;优化了蛋白水平乙酰化反应条件,最大限度地降低了丝氨酸、苏氨酸、酪氨酸残基上的副反应,提高了肽段鉴定的可信性;优化了基于固相萃取枪头膜片过滤柱(StageTip柱)的样品分离过程,使C端肽段的鉴定深度增加至原来的4倍。通过以上优化,按照肽段水平错误发现率(FDR)<0.01、离子分数(ion score)≥20,且C端带有乙醇胺修饰的数据筛选标准,从人HEK 293T细胞中共鉴定出696个蛋白质C端。若仅要求肽段水平FDR<0.01,鉴定数目进一步增加到933个,这是基于聚合物富集策略的蛋白质C端组学方法所得的最大数据集之一。探索了胰蛋白酶镜像酶(LysargiNase)切割精氨酸残基N端(ArgN型酶切)与不同肽段N端衍生化修饰组合对蛋白质C端鉴定数目和种类的影响,“LysargiNase酶切+肽段N端乙酰化”新策略在原有“胰蛋白酶酶切+肽段N端二甲基化”策略的基础上将鉴定蛋白质C端的种类提升了47%。综上,该研究通过对基于Arg型酶切的蛋白C端组学方法的优化,提升了C端肽段的鉴定深度,扩大了C端肽段鉴定的覆盖范围。该方法将有望成为系统性表征蛋白质C端的有力工具。

Biochemical Characterization of Cellulase From Bacillus subtilis Strain and its Effect on Digestibility and Structural Modifications of Lignocellulose Rich Biomass.

Microbial cellulases have become the mainstream biocatalysts due to their complex nature and widespread industrial applications. The present study reports the partial purification and characterization of cellulase from Bacillus subtilis CD001 and its application in biomass saccharification. Out of four different substrates, carboxymethyl cellulose, when amended as fermentation substrate, induced the highest cellulase production from B. subtilis CD001. The optimum activity of CMCase, FPase, and amylase was 2.4 U/ml, 1.5 U/ml, and 1.45 U/ml, respectively. The enzyme was partially purified by (NH4)2SO4 precipitation and sequenced through LC-MS/MS. The cellulase was found to be approximately 55 kDa by SDS-PAGE and capable of hydrolyzing cellulose, as confirmed by zymogram analysis. The enzyme was assigned an accession number AOR98335.1 and displayed 46% sequence homology with 14 peptide-spectrum matches having 12 unique peptide sequences. Characterization of the enzyme revealed it to be an acidothermophilic cellulase, having an optimum activity at pH 5 and a temperature of 60°C. Kinetic analysis of partially purified enzyme showed the Km and Vmax values of 0.996 mM and 1.647 U/ml, respectively. The enzyme activity was accelerated by ZnSO4, MnSO4, and MgSO4, whereas inhibited significantly by EDTA and moderately by β-mercaptoethanol and urea. Further, characterization of the enzyme saccharified sugarcane bagasse, wheat straw, and filter paper by SEM, ATR-FTIR, and XRD revealed efficient hydrolysis and structural modifications of cellulosic materials, indicating the potential industrial application of the B. subtilis CD001 cellulase. The findings demonstrated the potential suitability of cellulase from B. subtilis CD001 for use in current mainstream biomass conversion into fuels and other industrial processes.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library.

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.