Strategies for dereplication of natural compounds using high-resolution tandem mass spectrometry

Complete structural elucidation of natural products is commonly performed by nuclear magnetic resonance spectroscopy (NMR), but annotating compounds to most likely structures using high-resolution tandem mass spectrometry is a faster and feasible first step. The CASMI contest 2016 (Critical Assessment of Small Molecule Identification) provided spectra of eighteen compounds for the best manual structure identification in the natural products category. High resolution precursor and tandem mass spectra (MS/MS) were available to characterize the compounds. We used the Seven Golden Rules, Sirius2 and MS-FINDER software for determination of molecular formulas, and then we queried the formulas in different natural product databases including DNP, UNPD, ChemSpider and REAXYS to obtain molecular structures. We used different in-silico fragmentation tools including CFM-ID, CSI:FingerID and MS-FINDER to rank these compounds. Additional neutral losses and product ion peaks were manually investigated. This manual and time consuming approach allowed for the correct dereplication of thirteen of the eighteen natural products.     

Separation and identification of mouse brain tissue microproteins using top‐down method with high resolution nanocapillary liquid chromatography mass spectrometry

Microproteins and endogenous peptides in the brain contain important substances that have critical roles in diverse biological processes, contributing to signal transduction and intercellular signaling. However, variability in their physical or chemical characteristics, such as molecule size, hydrophobicity, and charge states, complicate the simultaneous analysis of these compounds, although this would be highly beneficial for the field of neuroscience research. Here, we present a top‐down analytical method for simultaneous analysis of microproteins and endogenous peptides using high‐resolution nanocapillary LC‐MS/MS. This method is detergent‐free and digestion‐free, which allows for extracting and preserving intact microproteins and peptides for direct LC‐MS analysis. Both higher energy collision dissociation and electron‐transfer dissociation fragmentations were used in the LC‐MS analysis to increase the identification rate, and bioinformatics tools ProteinGoggle and PEAKS Studio software were utilized for database search. In total, we identified 471 microproteins containing 736 proteoforms, including brain‐derived neurotrophic factor and a number of fibroblast growth factors. In addition, we identified 599 peptides containing 151 known or potential neuropeptides such as somatostatin‐28 and neuropeptide Y. Our approach bridges the gap for the characterization of brain microproteins and peptides, which permits quantification of a diversity of signaling molecules for biomarker discovery or therapy diagnosis in the future.     

Definitive screening design enables optimization of LC–ESI–MS/MS parameters in proteomics

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC–ESI–MS/MS to comprehensively identify these peptides. However, there are many parameters for LC–ESI–MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC–ESI–MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC–ESI–MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC–ESI–MS/MS systems.     

A tool to evaluate correspondence between extraction ion chromatographic peaks and peptide‐spectrum matches in shotgun proteomics experiments

Chromatographed peptide signals form the basis of further data processing that eventually results in functional information derived from data‐dependent bottom‐up proteomics assays. We seek to rank LC/MS parent ions by the quality of their extracted ion chromatograms. Ranked extracted ion chromatograms act as an intuitive physical/chemical preselection filter to improve the quality of MS/MS fragment scans submitted for database search. We identify more than 4900 proteins when considering detector shifts of less than 7 ppm. High quality parent ions for which the database search yields no hits become candidates for subsequent unrestricted analysis for PTMs. Following this rational approach, we prioritize identification of more than 5000 spectrum matches from modified peptides and confirmed the presence of acetylaldehyde‐modified His/Lys. We present a logical workflow that scores data‐dependent selected ion chromatograms and leverage information about semianalytical LC/LC dimension prior to MS. Our method can be successfully used to identify unexpected modifications in peptides with excellent chromatography characteristics, independent of fragmentation pattern and activation methods. We illustrate analysis of ion chromatograms detected in two different modes by RF linear ion trap and electrostatic field orbitrap.     

Application of displacement chromatography for the analysis of a lipid raft proteome

Defining membrane proteomes is fundamental to understand the role of membrane proteins in biological processes and to find new targets for drug development. Usually multidimensional chromatography using step or gradient elution is applied for the separation of tryptic peptides of membrane proteins prior to their mass spectrometric analysis. Displacement chromatography (DC) offers several advantages that are helpful for proteome analysis. However, DC has so far been applied for proteomic investigations only in few cases. In this study we therefore applied DC in a multidimensional LC–MS approach for the separation and identification of membrane proteins located in cholesterol-enriched membrane microdomains (lipid rafts) obtained from rat kidney by density gradient centrifugation. The tryptic peptides were separated on a cation-exchange column in the displacement mode with spermine used as displacer. Fractions obtained from DC were analyzed using an HPLC-chip system coupled to an electrospray-ionization ion-trap mass spectrometer. This procedure yielded more than 400 highly significant peptide spectrum matches and led to the identification of more than 140 reliable protein hits within an established rat kidney lipid raft proteome. The majority of identified proteins were membrane proteins. In sum, our results demonstrate that DC is a suitable alternative to gradient elution separations for the identification of proteins via a multidimensional LC–MS approach.     

生物质谱技术及其应用

质谱是带电粒子按质荷比大小顺序排列的图谱,最初主要用来测定元素或同位素的原子量,随着科学的发展及高性能质谱仪器的出现,质谱被越来越多地应用生命科学研究的许多领域,以其质辅助激光解吸附飞行时间质谱和电喷雾质谱为代表的现代生物质谱技术,为蛋白质等生物大分子的研究提供了必要的技术手段。本文在简介近年来比较常用的几种生物质谱技术的基础上,概述了生物质谱技术在蛋白质,核酸研究及检测分析等几个方面的初步应用。     

Developing the wool proteome

The wool proteome has been largely uncharted due to a lack of database coverage, poor protein extractability and dynamic range issues. Yet, investigating correlations between wool physical properties and protein content, or characterising UV-, heat- or processing-induced protein damage requires the availability of an identifiable and identified proteome.In this study we have achieved unprecedented wool proteome identification through a strategy of comprehensive data acquisition, iterative protein identification/validation and concurrent augmentation of the sequence database. Data acquisition comprised a range of different hyphenated MS techniques including LC–MS/MS, LC–MALDI, 2D-LC–MS/MS and SDS-PAGE LC–MS. Using iterative searching of databases and search result combination using ProteinScape, a systematic expansion of identifiable proteins in the sequence database was achieved. This was followed by extensive validation and rationalisation of the protein identifications. In total, 72 complete and 30 partial ovine-specific protein sequences were added to the database, and 113 wool proteins were identified.Enhanced access to ovine-specific protein identification and characterisation will facilitate all wool fibre protein chemistry and proteomics research.     

Mass spectrometric measurement of protein amide hydrogen exchange rates of apo- and holo-myoglobin.

Measurement of backbone amide hydrogen exchange rates can provide detailed information concerning protein structure, dynamics, and interactions. Although nuclear magnetic resonance is typically used to provide these data, its use is restricted to lower molecular weight proteins that are soluble at millimolar concentrations. Not subject to these limitations is a mass spectrometric approach for measuring deuterium incorporation into proteins that are subsequently proteolyzed by pepsin; the resulting peptide masses are measured using a flowing-fast atom bombardment ionization source (Zhang Z, Smith DL, 1993, Protein Sci 2:522-531). In the current study, amide deuterium incorporation for intact apo- and holo-myoglobin was measured using liquid chromatography coupled directly to an electrospray ionization (LC/MS) source. Electrospray ionization provided a more complete coverage of the protein sequence and permitted the measurement of deuterium incorporation into intact proteins. Tandem mass spectrometry was used to rapidly identify the peptic peptides. It was found that within 30 s, the amides in apo-myoglobin were 47% deuterated, whereas holo-myoglobin was 12% deuterated. Peptic digestion and LC/MS demonstrated that regions represented by peptic peptides encompassing positions 1-7, 12-29, and 110-134 were not significantly altered by removal of the heme. Likewise, destabilized regions were identified within positions 33-106 and 138-153.     

Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs

MHC class I (MHC‐I)‐bound ligands play a pivotal role in CD8 T cell immunity and are hence of major interest in understanding and designing immunotherapies. One of the most commonly utilized approaches for detecting MHC ligands is LC‐MS/MS. Unfortunately, the effectiveness of current algorithms to identify MHC ligands from LC‐MS/MS data is limited because the search algorithms used were originally developed for proteomics approaches detecting tryptic peptides. Consequently, the analysis often results in inflated false discovery rate (FDR) statistics and an overall decrease in the number of peptides that pass FDR filters. Andreatta et al. describe a new scoring tool (MS‐rescue) for peptides from MHC‐I immunopeptidome datasets. MS‐rescue incorporates the existence of MHC‐I peptide motifs to rescore peptides from ligandome data. The tool is demonstrated here using peptides assigned from LC‐MS/MS data with PEAKs software but can be deployed on data from any search algorithm. This new approach increased the number of peptides identified by up to 20–30% and promises to aid the discovery of novel MHC‐I ligands with immunotherapeutic potential.     

A Side by Side Comparison of Bruker Biotyper and VITEK MS: Utility of MALDI-TOF MS Technology for Microorganism Identification in a Public Health Reference Laboratory

Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid, highly accurate, and cost-effective method for routine identification of a wide range of microorganisms. We carried out a side by side comparative evaluation of the performance of Bruker Biotyper versus VITEK MS for identification of a large and diverse collection of microorganisms. Most difficult and/or unusual microorganisms, as well as commonly encountered microorganisms were selected, including Gram-positive and negative bacteria, mycobacteria, actinomycetes, yeasts and filamentous fungi. Six hundred forty two strains representing 159 genera and 441 species from clinical specimens previously identified at the Laboratoire de santé publique du Québec (LSPQ) by reference methods were retrospectively chosen for the study. They included 254 Gram-positive bacteria, 167 Gram-negative bacteria, 109 mycobacteria and aerobic actinomycetes and 112 yeasts and moulds. MALDI-TOF MS analyses were performed on both systems according to the manufacturer’s instructions. Of the 642 strains tested, the name of the genus and / or species of 572 strains were referenced in the Bruker database while 406 were present in the VITEK MS IVD database. The Biotyper correctly identified 494 (86.4%) of the strains, while the VITEK MS correctly identified 362 (92.3%) of the strains (excluding 14 mycobacteria that were not tested). Of the 70 strains not present in the Bruker database at the species level, the Biotyper correctly identified 10 (14.3%) to the genus level and 2 (2.9%) to the complex/group level. For 52 (74.2%) strains, we obtained no identification, and an incorrect identification was given for 6 (8.6%) strains. Of the 178 strains not present in the VITEK MS IVD database at the species level (excluding 71 untested mycobacteria and actinomycetes), the VITEK MS correctly identified 12 (6.8%) of the strains each to the genus and to the complex/group level. For 97 (54.5%) strains, no identification was given and for 69 (38.7%) strains, an incorrect identification was obtained. Our study demonstrates that both systems gave a high level (above 85%) of correct identification for a wide range of microorganisms. However, VITEK MS gave more misidentification when the microorganism analysed was not present in the database, compared to Bruker Biotyper. This should be taken into account when this technology is used alone for microorganism identification in a public health laboratory, where isolates received are often difficult to identify and/or unusual microorganisms.     

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – that is, Mascot, Mascot+Percolator, and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.     

Synthesis,identification, and biological activity of metabolites of two novel selective S1P1 agonists

SYL927 and SYL930 are selective S1P₁ agonists under preclinical development. However, during their pharmacokinetic studies we detected two metabolites in rat blood that were tentatively identified as monohydroxylated metabolites of SYL927 and SYL930 based on LC–MS/MS data. In this study, we designed and synthesized possible monohydroxylated products 6a–e and used them as references to confirm the structures of the two metabolites detected by LC–MS/MS. We also evaluated the in vitro and in vivo biological activities of these two metabolites.     

Identification of Indian butterflies using Deep Convolutional Neural Network

The conventional butterfly identification method is based on their different morphological characters namely wing-venation, color, shape, patterns and through the dissection studies and molecular techniques which are tedious, expensive and highly time-consuming. To overcome the above aforesaid challenges, a new butterfly identification system using butterfly images has been designed to instantly identify the butterfly with high accuracy. In this study, we construct a new butterfly dataset with 34,024 butterfly images belonging to 315 species from India. We propose and prove the effectiveness of new data augmentation techniques on our dataset. To identify butterflies using photographic images, we built eleven new Deep Convolutional Neural Network (DCNN) butterfly classifier models using eleven pre-trained architectures namely ResNet-18, ResNet-34, ResNet-50, ResNet-121, ResNet-152, Alex-Net, DenseNet-121, DenseNet-161, VGG-16, VGG-19 and SqueezeNet-v1.1. The different model's classification results were compared and the proposed technique achieved a maximum top-1 accuracy(94.44%), top-3 accuracy(98.46%) and top-5 accuracy(99.09%) using ResNet-152 model, followed by DenseNet-161 model achieved the top-1 accuracy(94.31%), top-3 accuracy (98.07%) and top-5 accuracy (98.66%). The results suggest that models can be assertively used to identify butterflies in India.     

Development of a reverse-phase liquid chromatography electrospray ionization mass spectrometry method for lipidomics, improving detection of phosphatidic acid and phosphatidylserine

In the studies of lipid metabolomics, liquid chromatography electrospray ionization mass spectrometry (LC/MS) is a robust and popular technique. Although effective reverse-phase LC methods enabling the separation of phospholipid molecular species have been developed, there are still problems with the separation of phosphatidic acid (PA) and phosphatidylserine (PS). These acidic phospholipids often elute as extensively broad peaks, causing inferior separation, detection, and quantification-a severe limitation of the method. In this study, we have developed reverse-phase LC conditions that reduce the undesired peak tailings in the elution profiles of both PA and PS, by using a starting mobile phase containing a low concentration of phosphoric acid (5 microM) and a high percentage of water (40%). Our method sensitively analyzes PA, PS, and their lysoforms, as well as the other phospholipids within a biological sample, in a single chromatographic step by an LC/MS method and, thus, is suitable for lipidomics.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.     

Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry

Human saliva contains a large number of proteins and peptides (salivary proteome) that help maintain homeostasis in the oral cavity. Global analysis of human salivary proteome is important for understanding oral health and disease pathogenesis. In this study, large-scale identification of salivary proteins was demonstrated by using shotgun proteomics and two-dimensinal gel electrophoresis-mass spectrometry (2-DE-MS). For the shotgun approach, whole saliva proteins were prefractionated according to molecular weight. The smallest fraction, presumably containing salivary peptides, was directly separated by capillary liquid chromatography (LC). However, the large protein fractions were digested into peptides for subsequent LC separation. Separated peptides were analyzed by on-line electrospray tandem mass spectrometry (MS/MS) using a quadrupole-time of flight mass spectrometer, and the obtained spectra were automatically processed to search human protein sequence database for protein identification. Additionally, 2-DE was used to map out the proteins in whole saliva. Protein spots 105 in number were excised and in-gel digested; and the resulting peptide fragments were measured by matrix-assisted laser desorption/ionization-mass spectrometry and sequenced by LC-MS/MS for protein identification. In total, we cataloged 309 proteins from human whole saliva by using these two proteomic approaches.     

High‐throughput identification of conditional MHCI ligands and scaled‐up production of conditional MHCI complexes

Despite the need to monitor the impact of Cancer Immunotherapy (CI)/Immuno‐Oncology (IO) therapeutics on neoantigen‐specific T‐cell responses, very few clinical programs incorporate this aspect of immune monitoring due to the challenges in high‐throughput (HTP) generation of Major Histocompatibility Complex Class I (MHCI) tetramers across a wide range of HLA alleles. This limitation was recently addressed through the development of MHCI complexes with peptides containing a nonnatural UV cleavable amino acid (conditional MHCI ligands) that enabled HTP peptide exchange upon UV exposure. Despite this advancement, the number of alleles with known conditional MHCI ligands is limited. We developed a novel workflow to enable identification and validation of conditional MHCI ligands across a range of HLA alleles. First, known peptide binders were screened via an enzyme‐linked immunosorbent assay (ELISA) assay. Conditional MHCI ligands were designed using the highest‐performing peptides and evaluated in the same ELISA assay. The top performers were then selected for scale‐up production. Next‐generation analytical techniques (LC/MS, SEC‐MALS, and 2D LC/MS) were used to characterize the complex after refolding with the conditional MHCI ligands. Finally, we used 2D LC/MS to evaluate peptide exchange with these scaled‐up conditional MHCI complexes after UV exposure with validated peptide binders. Successful peptide exchange was observed for all conditional MHCI ligands upon UV exposure, validating our screening approach. This approach has the potential to be broadly applied and enable HTP generation of MHCI monomers and tetramers across a wider range of HLA alleles, which could be critical to enabling the use of MHCI tetramers to monitor neoantigen‐specific T‐cells in the clinic.     

Distribution of cuticular proteins in different structures of adult Anopheles gambiae

Anopheles gambiae devotes over 2% (295) of its protein coding genes to structural cuticular proteins (CPs) that have been classified into 13 different families plus ten low complexity proteins not assigned to families. Small groups of genes code for identical proteins reducing the total number of unique cuticular proteins to 282. Is the large number because different structures utilize different CPs, or are all of the genes widely expressed? We used LC-MS/MS to learn how many products of these genes were found in five adult structures: Johnston's organs, the remainder of the male antennae, eye lenses, legs, and wings. Data were analyzed against both the entire proteome and a smaller database of just CPs. We recovered unique peptides for 97 CPs and shared peptides for another 35. Members of 11 of the 13 families were recovered as well as some unclassified. Only 11 CPs were present exclusively in only one structure while 43 CPs were recovered from all five structures. A quantitative analysis, using normalized spectral counts, revealed that only a few CPs were abundant in each structure. When the MS/MS data were run against the entire proteome, the majority of the top hits were to CPs, but peptides were recovered from an additional 467 proteins. CP peptides were frequently recovered from chitin-binding domains, confirming that protein-chitin interactions are not mediated by covalent bonds. Comparison with three other MS/MS analyses of cuticles or cuticle-rich structures augmented the current analysis. Our findings provide new insights into the composition of different mosquito structures and reveal the complexity of selection and utilization of genes coding for structural cuticular proteins.