SpotLight Proteomics Identifies Variable Sequences of Blood Antibodies Specific Against Deamidated Human Serum Albumin.

Spontaneous deamidation of asparaginyl residues in proteins, if not repaired or cleared, can set in motion a cascade that leads to deteriorated health. Previously, we have discovered that deamidated human serum albumin (HSA) is elevated in the blood of patients with Alzheimer's disease and other neurodegenerative diseases, while the level of endogenous antibodies against deamidated HSA is significantly diminished, creating an imbalance between the risk factor and the defense against it. Endogenous antibodies against deamidated proteins are still unexplored. In the current study, we employed the SpotLight proteomics approach to identify novel amino acid sequences in antibodies specific to deamidated HSA. The results provide new insights into the clearance mechanism of deamidated proteins, a possible avenue for prevention of neurodegeneration.

A Handle on Mass Coincidence Errors in De Novo Sequencing of Antibodies by Bottom-up Proteomics.

Antibody sequences can be determined at 99% accuracy directly from the polypeptide product by using bottom-up proteomics techniques. Sequencing accuracy at the peptide level is limited by the isobaric residues leucine and isoleucine, incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions, and isobaric combinations of amino acids, of potentially different lengths, for example, GG = N and GA = Q. Here, we present several updates to Stitch (v1.5), which performs template-based assembly of de novo peptides to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions, i.e., w-ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. Lastly, input data from a wider selection of de novo peptide sequencing algorithms are allowed, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.

Enrichment and fragmentation approaches for enhanced detection and characterization of endogenous glycosylated neuropeptides.

Glycosylated neuropeptides were recently discovered in crustaceans, a model organism with a well-characterized neuroendocrine system. Several workflows exist to characterize enzymatically digested peptides; however, the unique properties of endogenous neuropeptides require methods to be re-evaluated. We investigate the use of hydrophilic interaction liquid chromatography (HILIC) enrichment and different fragmentation methods to further probe the expression of glycosylated neuropeptides in Callinectes sapidus. During the evaluation of HILIC, we observed the necessity of a less aqueous solvent for endogenous peptide samples. This modification enabled the number of detected neuropeptide glycoforms to increase almost two-fold, from 18 to 36. Product ion-triggered electron-transfer/higher-energy collision dissociation enabled the site-specific detection of 55 intact N- and O-linked glycoforms, while the faster stepped collision energy higher-energy collisional dissociation resulted in detection of 25. Additionally, applying this workflow to five neuronal tissues enabled the characterization of 36 more glycoforms of known neuropeptides and 11 more glycoforms of nine putative novel neuropeptides. Overall, the database of glycosylated neuropeptides in crustaceans was largely expanded from 18 to 136 glycoforms of 40 neuropeptides from 10 neuropeptide families. Both macro- and micro-heterogeneity were observed, demonstrating the chemical diversity of this simple invertebrate, establishing a framework to use crustacean to probe modulatory effects of glycosylation on neuropeptides.

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search.

Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

Direct Mass Spectrometry-Based Detection and Antibody Sequencing of Monoclonal Gammopathy of Undetermined Significance from Patient Serum: A Case Study.

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell disorder characterized by the presence of a predominant monoclonal antibody (i.e., M-protein) in serum, without clinical symptoms. Here we present a case study in which we detect MGUS by liquid-chromatography coupled with mass spectrometry (LC-MS) profiling of IgG1 in human serum. We detected a Fab-glycosylated M-protein and determined the full heavy and light chain sequences by bottom-up proteomics techniques using multiple proteases, further validated by top-down LC-MS. Moreover, the composition and location of the Fab-glycan could be determined in CDR1 of the heavy chain. The outlined approach adds to an expanding mass spectrometry-based toolkit to characterize monoclonal gammopathies such as MGUS and multiple myeloma, with fine molecular detail. The ability to detect monoclonal gammopathies and determine M-protein sequences straight from blood samples by mass spectrometry provides new opportunities to understand the molecular mechanisms of such diseases.

FT-MS in the de novo top-down sequencing of natural nontryptic peptides.

The present review covers available results on the application of FT-MS for the de novo sequencing of natural peptides of various animals: cones, bees, snakes, amphibians, scorpions, and so forth. As these peptides are usually bioactive, the animals efficiently use them as a weapon against microorganisms or higher animals including predators. These peptides represent definite interest as drugs of future generations since the mechanism of their activity is completely different in comparison with that of the modern antibiotics. Utilization of those peptides as antibiotics can eliminate the problem of the bacterial resistance development. Sequence elucidation of these bioactive peptides becomes even more challenging when the species genome is not available and little is known about the protein origin and other properties of those peptides in the study. De novo sequencing may be the only option to obtain sequence information. The benefits of FT-MS for the top-down peptide sequencing, the general approaches of the de novxxo sequencing, the difficult cases involving sequence coverage, isobaric and isomeric amino acids, cyclization of short peptides, the presence of posttranslational modifications will be discussed in the review.

Mass spectrometry characterization of antibodies at the intact and subunit levels: from targeted to large-scale analysis.

Antibodies are one of the most formidable molecular weapons available to our immune system. Their high specificity against a target (antigen) and capability of triggering different immune responses (e.g., complement system activation and antibody-dependent cell-mediated cytotoxicity) make them ideal drugs to fight many different human diseases. Currently, both monoclonal antibodies and more complex molecules based on the antibody scaffold are used as biologics. Naturally, such highly heterogeneous molecules require dedicated analytical methodologies for their accurate characterization. Mass spectrometry (MS) can define the presence and relative abundance of multiple features of antibodies, including critical quality attributes. The combination of small and large variations within a single molecule can only be determined by analyzing intact antibodies or their large (25 to 100 kDa) subunits. Hence, top-down (TD) and middle-down (MD) MS approaches have gained popularity over the last decade. In this Young Scientist Feature we discuss the evolution of TD and MD MS analysis of antibodies, including the new frontiers that go beyond biopharma applications. We will show how this field is now moving from the "quality control" analysis of a known, single antibody to the high-throughput investigation of complex antibody repertoires isolated from clinical samples, where the ultimate goal is represented by the complete gas-phase sequencing of antibody molecules without the need of any a priori knowledge.

The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires.

Standardization of immunopeptidomics experiments across laboratories is a pressing issue within the field, and currently a variety of different methods for sample preparation and data analysis tools are applied. Here, we compared different software packages to interrogate immunopeptidomics datasets and found that Peaks reproducibly reports substantially more peptide sequences (~30-70%) compared with Maxquant, Comet, and MS-GF+ at a global false discovery rate (FDR) of <1%. We noted that these differences are driven by search space and spectral ranking. Furthermore, we observed differences in the proportion of peptides binding the human leukocyte antigen (HLA) alleles present in the samples, indicating that sequence-related differences affected the performance of each tested engine. Utilizing data from single HLA allele expressing cell lines, we observed significant differences in amino acid frequency among the peptides reported, with a broadly higher representation of hydrophobic amino acids L, I, P, and V reported by Peaks. We validated these results using data generated with a synthetic library of 2000 HLA-associated peptides from four common HLA alleles with distinct anchor residues. Our investigation highlights that search engines create a bias in peptide sequence depth and peptide amino acid composition, and resulting data should be interpreted with caution.

Comparative transcriptomic analysis of the larval and adult stages of Dibothriocephalus dendriticus (Cestoda: Diphyllobothriidea).

Tapeworms of the genus Dibothriocephalus are widely distributed throughout the world, some of which are agents of human diphyllobothriasis, one of the most important fish-borne zoonoses caused by a cestode parasite. Genomic and transcriptomic data can be used to develop future diagnostic tools and epidemiological studies. The present work focuses on a comparative analysis of the transcriptomes of adult and plerocercoid D. dendriticus and the identification of their differentially expressed genes (DEGs). Transcriptome assembly and analysis yielded and annotated 35,129 unigenes, noting that 16,568 (47%) unigenes were not annotated in known databases, which may indicate a unique set of expressed transcripts for D. dendriticus. A total of 8022 differentially expressed transcripts were identified, including 3225 upregulated and 4797 downregulated differentially expressed transcripts from the plerocercoid and adult animals. The analysis of DEGs has shown that among the most differentially expressed genes, there are important genes characteristic of each stage. Thus, several genes are characteristic of D. dendriticus plerocercoids, including fatty acid-binding protein and ferritin. Among the most highly expressed DEGs of the adult stage of D. dendriticus is the Kunitz-type serine protease inhibitor, in two putative isoforms. The analyses of GO and KEGG metabolic pathways revealed that a large number of the DEGs of D. dendriticus are associated with the biosynthesis of various substances such as arginine and folate, as well as with various metabolic pathways such as galactose metabolism, selenocompound metabolism, and phosphonate and phosphinate metabolism. This will contribute to further research aimed at identifying targets for new generation drugs and the development of specific vaccines.

Tandem Mass Spectrometry de novo Sequencing of the Skin Defense Peptides of the Central Slovenian Agile Frog Rana dalmatina.

Peptides released on frogs' skin in a stress situation represent their only weapon against micro-organisms and predators. Every species and even population of frog possesses its own peptidome being appropriate for their habitat. Skin peptides are considered potential pharmaceuticals, while the whole peptidome may be treated as a taxonomic characteristic of each particular population. Continuing the studies on frog peptides, here we report the peptidome composition of the Central Slovenian agile frog Rana dalmatina population. The detection and top-down de novo sequencing of the corresponding peptides was conducted exclusively by tandem mass spectrometry without using any chemical derivatization procedures. Collision-induced dissociation (CID), higher energy collision-induced dissociation (HCD), electron transfer dissociation (ETD) and combined MS3 method EThcD with stepwise increase of HCD energy were used for that purpose. MS/MS revealed the whole sequence of the detected peptides including differentiation between isomeric Leu/Ile, and the sequence portion hidden in the disulfide cycle. The array of the discovered peptide families (brevinins 1 and 2, melittin-related peptides (MRPs), temporins and bradykinin-related peptides (BRPs)) is quite similar to that of R. temporaria. Since the genome of this frog remains unknown, the obtained results were compared with the recently published transcriptome of R. dalmatina.

Template-Assisted De Novo Sequencing of SARS-CoV-2 and Influenza Monoclonal Antibodies by Mass Spectrometry.

In this study, we used multiple enzyme digestions, coupled with higher-energy collisional dissociation (HCD) and electron-transfer/higher-energy collision dissociation (EThcD) fragmentation to develop a mass-spectrometric (MS) method for determining the complete protein sequence of monoclonal antibodies (mAbs). The method was refined on an mAb of a known sequence, a SARS-CoV-1 antireceptor binding domain (RBD) spike monoclonal antibody. The data were searched using Supernovo to generate a complete template-assisted de novo sequence for this and two SARS-CoV-2 mAbs of known sequences resulting in correct sequences for the variable regions and correct distinction of Ile and Leu residues. We then used the method on a set of 25 antihemagglutinin (HA) influenza antibodies of unknown sequences and determined high confidence sequences for >99% of the complementarity determining regions (CDRs). The heavy-chain and light-chain genes were cloned and transfected into cells for recombinant expression followed by affinity purification. The recombinant mAbs displayed binding curves matching the original mAbs with specificity to the HA influenza antigen. Our findings indicate that this methodology results in almost complete antibody sequence coverage with high confidence results for CDR regions on diverse mAb sequences.

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.

Systematic Identification of Microproteins during the Development of Drosophila melanogaster.

Short open reading frame-encoded peptides (SEPs) are microproteins with less than 100 amino acids that play an essential role in the growth and development of organisms. There are plenty of short open reading frames in Drosophila melanogaster that potentially code polypeptides. We chose 11 time points during the life cycle of Drosophila to investigate microproteins, particularly those related to development. Finally, we identified a total of 410 microproteins, of which 27 were noncoding RNA-encoded proteins. Of the 410 microproteins, 74 were expressed in all stages from embryo to adults, whereas 300 microproteins were only found in one or two time points. Approximately, one-third of the microproteins were not reported previously and 44 were obtained from de novo sequencing, validated by synthetic peptides. These microproteins are related to the main bioprocesses of growth and development, such as multicellular organism reproduction, postmating behavior, and oviposition. Over half of the microproteins have predicted functional domains and are conserved across species, suggesting that these microproteins have critical functions in fly development. This work enriches the D. melanogaster proteome and provides a significant data resource for growth and development research.

ProAlanase is an Effective Alternative to Trypsin for Proteomics Applications and Disulfide Bond Mapping.

Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus Aspergillus niger that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and de novo sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by de novo sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb.

Assessing Protein Sequence Database Suitability Using De Novo Sequencing.

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

Whole Transcriptome Analyses of Apricots and Japanese Plum Fruits after 1-MCP (Ethylene-Inhibitor) and Ethrel (Ethylene-Precursor) Treatments Reveal New Insights into the Physiology of the Ripening Process.

The physiology of Prunus fruit ripening is a complex and not completely understood process. To improve this knowledge, postharvest behavior during the shelf-life period at the transcriptomic level has been studied using high-throughput sequencing analysis (RNA-Seq). Monitoring of fruits has been analyzed after different ethylene regulator treatments, including 1-MCP (ethylene-inhibitor) and Ethrel (ethylene-precursor) in two contrasting selected apricot (Prunus armeniaca L.) and Japanese plum (P. salicina L.) cultivars, 'Goldrich' and 'Santa Rosa'. KEEG and protein-protein interaction network analysis unveiled that the most significant metabolic pathways involved in the ripening process were photosynthesis and plant hormone signal transduction. In addition, previously discovered genes linked to fruit ripening, such as pectinesterase or auxin-responsive protein, have been confirmed as the main genes involved in this process. Genes encoding pectinesterase in the pentose and glucuronate interconversions pathway were the most overexpressed in both species, being upregulated by Ethrel. On the other hand, auxin-responsive protein IAA and aquaporin PIP were both upregulated by 1-MCP in 'Goldrich' and 'Santa Rosa', respectively. Results also showed the upregulation of chitinase and glutaredoxin 3 after Ethrel treatment in 'Goldrich' and 'Santa Rosa', respectively, while photosystem I subunit V psaG (photosynthesis) was upregulated after 1-MCP in both species. Furthermore, the overexpression of genes encoding GDP-L-galactose and ferredoxin in the ascorbate and aldarate metabolism and photosynthesis pathways caused by 1-MCP favored antioxidant activity and therefore slowed down the fruit senescence process.

Mass Spectrometric Proof of Predicted Peptides: Novel Adipokinetic Hormones in Insects.

The importance of insects in our ecosystems is undeniable. The indiscriminate use of broad-spectrum insecticides is a factor in the decline in insect biomass. We identify and sequence a prominent neuropeptide hormone in insects with an overarching goal to elucidate relatedness and create a database of bioactive peptides that could inform possible cross-activity in biological assays for the identification of a biorational lead compound. The major task of an adipokinetic hormone (AKH) in an insect is the regulation of metabolic events, such as carbohydrate and lipid breakdown in storage tissue during intense muscular work. From genomic and/or transcriptomic information one may predict the genes encoding neuropeptides such as the AKHs of insects. Definite elucidation of the primary structure of the mature peptide with putative post-translational modifications needs analytical chemical methods. Here we use high-resolution mass spectrometry coupled with liquid chromatography to identify unequivocally the AKHs of five insect species (one cockroach, two moths, and two flies) of which either genomic/transcriptomic information was available or sequences from related species. We confirm predicted sequences and discover novel AKH sequences, including one with a post-translational hydroxyproline modification. The additional sequences affirm an evolutionary pattern of dipteran AKHs and a conserved pattern in crambid moths.

Qualitative and Quantitative Analysis of Ejiao-Related Animal Gelatins through Peptide Markers Using LC-QTOF-MS/MS and Scheduled Multiple Reaction Monitoring (MRM) by LC-QQQ-MS/MS.

Donkey-hide gelatin, also called Ejiao (colla corii asini), is commonly used as a food health supplement and valuable Chinese medicine. Its growing popular demand and short supply make it a target for fraud, and many other animal gelatins can be found as adulterants. Authentication remains a quality concern. Peptide markers were developed by searching the protein database. However, donkeys and horses share the same database, and there is no specific marker for donkeys. Here, solutions are sought following a database-independent strategy. The peptide profiles of authentic samples of different animal gelatins were compared using LC-QTOF-MS/MS. Fourteen specific markers, including four donkey-specific, one horse-specific, three cattle-specific, and six pig-specific peptides, were successfully found. As these donkey-specific peptides are not included in the current proteomics database, their sequences were determined by de novo sequencing. A quantitative LC-QQQ multiple reaction monitoring (MRM) method was further developed to achieve highly sensitive and selective analysis. The specificity and applicability of these markers were confirmed by testing multiple authentic samples and 110 batches of commercial Ejiao products, 57 of which were found to be unqualified. These results suggest that these markers are specific and accurate for authentication purposes.

Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme.

Antibody sequence information is crucial to understanding the structural basis for antigen binding and enables the use of antibodies as therapeutics and research tools. Here, we demonstrate a method for direct de novo sequencing of monoclonal IgG from the purified antibody products. The method uses a panel of multiple complementary proteases to generate suitable peptides for de novo sequencing by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a bottom-up fashion. Furthermore, we apply a dual fragmentation scheme, using both stepped high-energy collision dissociation (stepped HCD) and electron-transfer high-energy collision dissociation (EThcD), on all peptide precursors. The method achieves full sequence coverage of the monoclonal antibody herceptin, with an accuracy of 99% in the variable regions. We applied the method to sequence the widely used anti-FLAG-M2 mouse monoclonal antibody, which we successfully validated by remodeling a high-resolution crystal structure of the Fab and demonstrating binding to a FLAG-tagged target protein in Western blot analysis. The method thus offers robust and reliable sequences of monoclonal antibodies.

Deep learning approaches for data-independent acquisition proteomics.

INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field. AREAS COVERED: This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases. EXPERT OPINION: While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.