On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method.

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.

Chemical Proteomics Reveals that the Anticancer Drug Everolimus Affects the Ubiquitin-Proteasome System.

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.

Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing.

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.

Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data.

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.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics.

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.

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads.

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.

Accelerating photofragmentation UV Spectroscopy-Mass spectrometry fingerprinting for quantification of isomeric peptides.

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.

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes.

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty cycle. It was always tempting to implement approaches that do not require MS/MS, yet they were constantly failing to achieve a meaningful depth of quantitative proteome coverage within short experimental times, which is particularly important for clinical or biomarker-discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free method, DirectMS1, for bottom-up proteomics. The method is compared with the standard MS/MS-based data-dependent acquisition approach for proteome-wide analysis using 5 min LC gradients. Specifically, we demonstrate identification of 1 000 protein groups for a standard HeLa cell line digest. The amount of loaded sample was varied in a range from 1 to 500 ng, and the method demonstrated 10-fold higher sensitivity. Combined with the recently introduced Diffacto approach for relative protein quantification, DirectMS1 outperforms most popular MS/MS-based label-free quantitation approaches because of significantly higher protein sequence coverage.

Method for Identification of Threonine Isoforms in Peptides by Ultraviolet Photofragmentation of Cold Ions.

Identification of isomeric amino acid residues in peptides and proteins is challenging but often highly desired in proteomics. One of the practically important cases that require isomeric assignments is that associated with single-nucleotide polymorphism substitutions of Met residues by Thr in cancer-related proteins. These genetically encoded substitutions can yet be confused with the chemical modifications, arising from protein alkylation by iodoacetamide, which is commonly used in the standard procedure of sample preparation for proteomic analysis. Similar to the genetically encoded mutations, the alkylation also induces a conversion of methionine residues, but to the iso-threonine form. Recognition of the mutations therefore requires isoform-sensitive detection techniques. Herein, we demonstrate an analytical method for reliable identification of isoforms of threonine residues in tryptic peptides. It is based on ultraviolet photodissociation mass spectrometry of cryogenically cooled ions and a machine-learning algorithm. The measured photodissociation mass spectra exhibit isoform-specific patterns, which are independent of the residues adjacent to threonine or iso-threonine in a peptide sequence. A comprehensive metric-based evaluation demonstrates that, being calibrated with a set of model peptides, the method allows for isomeric identification of threonine residues in peptides of arbitrary sequence.

Multiplexed Middle-Down Mass Spectrometry as a Method for Revealing Light and Heavy Chain Connectivity in a Monoclonal Antibody.

Pairing light and heavy chains in monoclonal antibodies (mAbs) using top-down (TD) or middle-down (MD) mass spectrometry (MS) may complement the sequence information on single chains provided by high-throughput genomic sequencing and bottom-up proteomics, favoring the rational selection of drug candidates. The 50 kDa F(ab) subunits of mAbs are the smallest structural units that contain the required information on chain pairing. These subunits can be enzymatically produced from whole mAbs and interrogated in their intact form by TD/MD MS approaches. However, the high structural complexity of F(ab) subunits requires increased sensitivity of the modern TD/MD MS for a comprehensive structural analysis. To address this and similar challenges, we developed and applied a multiplexed TD/MD MS workflow based on spectral averaging of tandem mass spectra (MS/MS) across multiple liquid chromatography (LC)-MS/MS runs acquired in reduced or full profile mode using an Orbitrap Fourier transform mass spectrometer (FTMS). We first benchmark the workflow using myoglobin as a reference protein, and then validate it for the analysis of the 50 kDa F(ab) subunit of a therapeutic mAb, trastuzumab. Obtained results confirm the envisioned benefits in terms of increased signal-to-noise ratio of product ions from utilizing multiple LC-MS/MS runs for TD/MD protein analysis using mass spectral averaging. The workflow performance is compared with the earlier introduced multiplexed TD/MD MS workflow based on transient averaging in Orbitrap FTMS. For the latter, we also report on enabling absorption mode FT processing and demonstrate its comparable performance to the enhanced FT (eFT) spectral representation.

Chemical-Mediated Digestion: An Alternative Realm for Middle-down Proteomics?

Protein digestion in mass spectrometry (MS)-based bottom-up proteomics targets mainly lysine and arginine residues, yielding primarily 0.6-3 kDa peptides for the proteomes of organisms of all major kingdoms. Recent advances in MS technology enable analysis of complex mixtures of increasingly longer (>3 kDa) peptides in a high-throughput manner supporting the development of a middle-down proteomics (MDP) approach. Generating longer peptides is a paramount step in launching an MDP pipeline, but the quest for the selection of a cleaving agent that would provide the desired 3-15 kDa peptides remains open. Recent bioinformatics studies have shown that cleavage at the rarely occurring amino acid residues such as methionine (Met), tryptophan (Trp), or cysteine (Cys) would be suitable for MDP approach. Interestingly, chemical-mediated proteolytic cleavages uniquely allow targeting these rare amino acids, for which no specific proteolytic enzymes are known. Herein, as potential candidates for MDP-grade proteolysis, we have investigated the performance of chemical agents previously reported to target primarily Met, Trp, and Cys residues: CNBr, BNPS-Skatole (3-bromo-3-methyl-2-(2-nitrophenyl)sulfanylindole), and NTCB (2-nitro-5-thiobenzoic acid), respectively. Figures of merit such as digestion reproducibility, peptide size distribution, and occurrence of side reactions are discussed. The NTCB-based MDP workflow has demonstrated particularly attractive performance, and NTCB is put forward here as a potential cleaving agent for further MDP development.

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics.

Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The "real world" sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer . The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstract The analysis workflow employing FractionOptimizer software.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics.

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics.

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

Comparative proteomics as a tool for identifying specific alterations within interferon response pathways in human glioblastoma multiforme cells.

An acquisition of increased sensitivity of cancer cells to viruses is a common outcome of malignant progression that justifies the development of oncolytic viruses as anticancer therapeutics. Studying molecular changes that underlie the sensitivity to viruses would help to identify cases where oncolytic virus therapy would be most effective. We quantified changes in protein abundances in two glioblastoma multiforme (GBM) cell lines that differ in the ability to induce resistance to vesicular stomatitis virus (VSV) infection in response to type I interferon (IFN) treatment. In IFN-treated samples we observed an up-regulation of protein products of some IFN-regulated genes (IRGs). In total, the proteome analysis revealed up to 20% more proteins encoded by IRGs in the glioblastoma cell line, which develops resistance to VSV infection after pre-treatment with IFN. In both cell lines protein-protein interaction and signaling pathway analyses have revealed a significant stimulation of processes related to type I IFN signaling and defense responses to viruses. However, we observed a deficiency in STAT2 protein in the VSV-sensitive cell line that suggests a de-regulation of the JAK/STAT/IRF9 signaling. The study has shown that the up-regulation of IRG proteins induced by the IFNα treatment of GBM cells can be detected at the proteome level. Similar analyses could be applied for revealing functional alterations within the antiviral mechanisms in glioblastoma samples, accompanying by acquisition of sensitivity to oncolytic viruses. The approach can be useful for discovering the biomarkers that predict a potential sensitivity of individual glioblastoma tumors to oncolytic virus therapy.

Brute-Force Approach for Mass Spectrometry-Based Variant Peptide Identification in Proteogenomics without Personalized Genomic Data.

In a proteogenomic approach based on tandem mass spectrometry analysis of proteolytic peptide mixtures, customized exome or RNA-seq databases are employed for identifying protein sequence variants. However, the problem of variant peptide identification without personalized genomic data is important for a variety of applications. Following the recent proposal by Chick et al. (Nat. Biotechnol. 33, 743-749, 2015) on the feasibility of such variant peptide search, we evaluated two available approaches based on the previously suggested "open" search and the "brute-force" strategy. To improve the efficiency of these approaches, we propose an algorithm for exclusion of false variant identifications from the search results involving analysis of modifications mimicking single amino acid substitutions. Also, we propose a de novo based scoring scheme for assessment of identified point mutations. In the scheme, the search engine analyzes y-type fragment ions in MS/MS spectra to confirm the location of the mutation in the variant peptide sequence. Graphical abstract ᅟ.

MS/MS-Free Protein Identification in Complex Mixtures Using Multiple Enzymes with Complementary Specificity.

In this work, we present the results of evaluation of a workflow that employs a multienzyme digestion strategy for MS1-based protein identification in "shotgun" proteomic applications. In the proposed strategy, several cleavage reagents of different specificity were used for parallel digestion of the protein sample followed by MS1 and retention time (RT) based search. Proof of principle for the proposed strategy was performed using experimental data obtained for the annotated 48-protein standard. By using the developed approach, up to 90% of proteins from the standard were unambiguously identified. The approach was further applied to HeLa proteome data. For the sample of this complexity, the proposed MS1-only strategy determined correctly up to 34% of all proteins identified using standard MS/MS-based database search. It was also found that the results of MS1-only search were independent of the chromatographic gradient time in a wide range of gradients from 15-120 min. Potentially, rapid MS1-only proteome characterization can be an alternative or complementary to the MS/MS-based "shotgun" analyses in the studies, in which the experimental time is more important than the depth of the proteome coverage.

Influence of the Number of Axial Bexarotene Ligands on the Cytotoxicity of Pt(IV) Analogs of Oxaliplatin.

We present the synthesis and cytotoxic potencies of new Pt(IV) complexes with bexarotene, an anticancer drug that induces cell differentiation and apoptosis via selective activation of retinoid X receptors. In these complexes bexarotene is positioned as an axial ligand. The complex of one bexarotene ligand attached to Pt(IV) oxaliplatin moiety was potent whereas its counterpart carrying two bexarotene ligands was inactive.

Comparison of False Discovery Rate Control Strategies for Variant Peptide Identifications in Shotgun Proteogenomics.

Proteogenomic studies aiming at identification of variant peptides using customized database searches of mass spectrometry data are facing a dilemma of selecting the most efficient database search strategy: A choice has to be made between using combined or sequential searches against reference (wild-type) and mutant protein databases or directly against the mutant database without the wild-type one. Here we called these approaches "all-together", "one-by-one", and "direct", respectively. We share the results of the comparison of these search strategies obtained for large data sets of publicly available proteogenomic data. On the basis of the results of this evaluation, we found that the "all-together" strategy provided, in general, more variant peptide identifications compared with the "one-by-one" approach, while showing similar performance for some specific cases. To validate further the results of this study, we performed a control comparison of the strategies in question using publicly available data for a mixture of the annotated human protein standard UPS1 and E. coli. For these data, both "all-together" and "one-by-one" approaches showed similar sensitivity and specificity of the searches, while the "direct" approach resulted in an increased number of false identifications.

Unbiased False Discovery Rate Estimation for Shotgun Proteomics Based on the Target-Decoy Approach.

Target-decoy approach (TDA) is the dominant strategy for false discovery rate (FDR) estimation in mass-spectrometry-based proteomics. One of its main applications is direct FDR estimation based on counting of decoy matches above a certain score threshold. The corresponding equations are widely employed for filtering of peptide or protein identifications. In this work we consider a probability model describing the filtering process and find that, when decoy counting is used for q value estimation and subsequent filtering, a correction has to be introduced into these common equations for TDA-based FDR estimation. We also discuss the scale of variance of false discovery proportion (FDP) and propose using confidence intervals for more conservative FDP estimation in shotgun proteomics. The necessity of both the correction and the use of confidence intervals is especially pronounced when filtering small sets (such as in proteogenomics experiments) and when using very low FDR thresholds.