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Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.
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Espectrometria de Massas , Proteômica , Proteômica/métodos , Humanos , Proteoma/análise , Proteoma/metabolismoRESUMO
Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.
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Proteômica , Espectrometria de Massas em Tandem , Proteômica/métodos , Humanos , Espectrometria de Massas em Tandem/métodos , Atrofia de Múltiplos Sistemas/metabolismo , Vimentina/análise , Vimentina/metabolismo , Proteoma/análise , Encéfalo/metabolismoRESUMO
Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.
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Descoberta de Drogas , Proteoma , Proteômica , Espectrometria de Massas em Tandem , Proteômica/métodos , Humanos , Proteoma/análise , Descoberta de Drogas/métodos , Espectrometria de Massas em Tandem/métodos , Linhagem Celular TumoralRESUMO
Rapamycin is a natural antifungal, immunosuppressive, and antiproliferative compound that allosterically inhibits mTOR complex 1. The ubiquitin-proteasome system (UPS) responsible for protein turnover is usually not listed among the pathways affected by mTOR signaling. However, some previous studies have indicated the interplay between the UPS and mTOR. It has also been reported that rapamycin and its analogs can allosterically inhibit the proteasome itself. In this work, we studied the molecular effect of rapamycin and its analogs (rapalogs), everolimus and temsirolimus, on the A549 cell line by expression proteomics. The analysis of differentially expressed proteins showed that the cellular response to everolimus treatment is strikingly different from that to rapamycin and temsirolimus. In the cluster analysis, the effect of everolimus was similar to that of bortezomib, a well-established proteasome inhibitor. UPS-related pathways were enriched in the cluster of proteins specifically upregulated upon everolimus and bortezomib treatments, suggesting that both compounds have similar proteasome inhibition effects. In particular, the total amount of ubiquitin was significantly elevated in the samples treated with everolimus and bortezomib, and analysis of the polyubiquitination patterns revealed elevated intensities of the ubiquitin peptide with a GG modification at the K48 residue, consistent with a bottleneck in proteasomal protein degradation. Moreover, the everolimus treatment resulted in both ubiquitin phosphorylation and generation of a significant amount of semitryptic peptides, illustrating the increase in the protease activity. These observations suggest that everolimus affects the UPS in a unique way, and its mechanism of action is different from that of its close chemical analogs, rapamycin and temsirolimus.
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The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.
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Escherichia coli , Proteômica , Humanos , Espectrometria de Massas/métodos , Proteômica/métodos , Software , Saccharomyces cerevisiaeRESUMO
The Tibellus oblongus spider is an active hunter that does not spin webs and remains highly underinvestigated in terms of the venom composition. Here, we describe venom glands transcriptome and venom proteome analysis for unveiling the polypeptide composition of Tibellus oblongus spider venom. The resulting EST database includes 1733 records, including 1263 nucleotide sequences with ORFs, of these 942 have been identified as toxin-coding. The database of peptide sequences was built based on of the transcriptomics results. It contains 217 new toxins, 212 of them were detected in the T. oblongus venom by the proteomics.
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Venenos de Aranha , Transcriptoma , Sequência de Aminoácidos , Perfilação da Expressão Gênica/métodos , Proteômica/métodos , Venenos de Aranha/genética , Venenos de Aranha/química , Peçonhas , AnimaisRESUMO
One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.
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Proteômica , Espectrometria de Massas em Tandem , Humanos , Escherichia coli , Algoritmos , Peptídeos/química , SoftwareRESUMO
Current proteomics approaches rely almost exclusively on using the positive ionization mode, resulting in inefficient ionization of many acidic peptides. This study investigates protein identification efficiency in the negative ionization mode using the DirectMS1 method. DirectMS1 is an ultrafast data acquisition method based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identification in the negative ion mode to date, identifying over 1000 proteins in a human cell line at a 1% false discovery rate. This is accomplished using a single-shot 10 min separation gradient, comparable to lengthy MS/MS-based analyses. Optimizing separation and experimental conditions was achieved by utilizing mobile buffers containing 2.5 mM imidazole and 3% isopropanol. The study emphasized the complementary nature of data obtained in positive and negative ion modes. Combining the results from all replicates in both polarities increased the number of identified proteins to 1774. Additionally, we analyzed the method's efficiency using different proteases for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), trypsin and LysC demonstrated the highest protein identification yield. This suggests that digestion procedures utilized in positive-mode proteomics can be effectively applied in the negative ion mode. Data are deposited to ProteomeXchange: PXD040583.
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Proteômica , Espectrometria de Massas em Tandem , Humanos , Espectrometria de Massas em Tandem/métodos , Tripsina , Proteômica/métodos , Peptídeos/análise , Proteínas , Peptídeo Hidrolases/metabolismoRESUMO
The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.
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Proteogenômica , Proteômica , Humanos , Animais , Camundongos , RNA/metabolismo , Edição de RNA , Cromatografia Líquida , Espectrometria de Massas em Tandem , Proteoma/genética , Proteoma/metabolismo , Adenosina/metabolismo , Inosina/genética , Inosina/metabolismoRESUMO
The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing.
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Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.
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Processamento Alternativo , Melanoma , Humanos , Processamento Alternativo/genética , Proteoma/genética , Proteoma/metabolismo , Proteômica/métodos , RNA/metabolismo , Isoformas de Proteínas/genética , Isoformas de Proteínas/metabolismo , Análise de Sequência de RNA , Splicing de RNA , Linhagem Celular , Melanoma/genéticaRESUMO
Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.
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Proteômica , Transdução de Sinais , Proteômica/métodos , BiomarcadoresRESUMO
Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.
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Neoplasias Ovarianas , Proteoma , Humanos , Feminino , Proteoma/metabolismo , Proteômica/métodos , Linhagem Celular Tumoral , Neoplasias Ovarianas/tratamento farmacológico , Espectrometria de Massas em TandemRESUMO
Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.
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Proteoma , Proteômica , Sistemas de Liberação de Medicamentos , Descoberta de Drogas/métodos , Espectrometria de Massas/métodos , Proteoma/análise , Proteômica/métodosRESUMO
Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.
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Proteoma , Proteômica , Cromatografia Líquida/métodos , Humanos , Proteoma/análise , Proteômica/métodos , Espectrometria de Massas em Tandem/métodos , Fluxo de TrabalhoRESUMO
Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.
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Proteogenômica , Aminoácidos , Células HEK293 , Humanos , Peptídeo Hidrolases , Peptídeos/análise , Proteogenômica/métodos , Proteoma/análise , Reprodutibilidade dos TestesRESUMO
Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.
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Interferon Tipo I , Terapia Viral Oncolítica , Vírus Oncolíticos , Estomatite Vesicular , Animais , Linhagem Celular Tumoral , Receptores ErbB/genética , Interferon Tipo I/metabolismo , Vírus Oncolíticos/fisiologia , Vírus da Estomatite Vesicular Indiana/genética , Vesiculovirus/fisiologiaRESUMO
Oncolytic viruses have gained momentum in the last decades as a promising tool for cancer treatment. Despite the progress, only a fraction of patients show a positive response to viral therapy. One of the key variable factors contributing to therapy outcomes is interferon-dependent antiviral mechanisms in tumor cells. Here, we evaluated this factor using patient-derived glioblastoma multiforme (GBM) cultures. Cell response to the type I interferons' (IFNs) stimulation was characterized at mRNA and protein levels. Omics analysis revealed that GBM cells overexpress interferon-stimulated genes (ISGs) and upregulate their proteins, similar to the normal cells. A conserved molecular pattern unambiguously differentiates between the preserved and defective responses. Comparing ISGs' portraits with titration-based measurements of cell sensitivity to a panel of viruses, the "strength" of IFN-induced resistance acquired by GBM cells was ranked. The study demonstrates that suppressing a single ISG and encoding an essential antiviral protein, does not necessarily increase sensitivity to viruses. Conversely, silencing IFIT3 and PLSCR1 genes in tumor cells can negatively affect the internalization of vesicular stomatitis and Newcastle disease viruses. We present evidence of a complex relationship between the interferon response genes and other factors affecting the sensitivity of tumor cells to viruses.
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Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.
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Proteômica , Espectrometria de Massas em Tandem , Algoritmos , Bases de Dados de Proteínas , Processamento de Proteína Pós-Traducional , SoftwareRESUMO
Identification of isomeric biomolecules remains a challenging analytical problem. A recently developed spectroscopic method that combines UV photofragmentation and mass spectrometry for fingerprinting of cold ions (2D UV-MS), has already demonstrated its high performance in the library-based identification and quantification of different types of biomolecular isomers. The practical use of the method has been hindered by a slow rate of data acquisition, which makes the fingerprinting incompatible with high-throughput analysis and online liquid chromatography (LC) separation. Herein we demonstrate how the use of a few pre-selected wavelengths can accelerate the method by two orders of magnitude without a significant loss of accuracy. As a proof of principle, 2D UV-MS fingerprinting was coupled to online LC separation and tested for quantification of isomeric peptides containing either Asp or isoAsp residues. The relative concentrations of the peptides mixed in solution have been determined, on average, with better than 4% and 6% accuracy for resolving and non-resolving gradients of LC separation, respectively.