Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

A SILAC-based accurate quantification of shrimp allergen tropomyosin in complex food matrices using UPLC-MS/MS

The carryover of trace allergens in complex food matrices poses challenges for detection techniques. Here, we demonstrate an accurate UPLC-MS/MS quantification assay for the shrimp allergen tropomyosin with a full-length isotope-labelled recombinant tropomyosin (TM-I) internal standard in complex food matrices. The TM-I, expressed based on the SILAC technique, exhibited a high isotope labelling ratio (>99%), purity, and alignment with the natural sequence. This method determined the tropomyosin ranging from 0.2 to 100 ng/mL. Mean recoveries ranged from 89 to 116%, with intra- and inter-day RSDs below 12%, for three signature peptides across three types of commercially processed food matrices. The limits of quantitation were 1 μg/g in pop food and sauce, and 10 μg/g in surimi product, respectively. This study supports the use of recombinant full-length isotope-labelled proteins rather than stable-isotope labelling peptides as internal standards to achieve more accurate quantitation of food allergens as the digestion error is corrected.

Mirror-image trypsin digestion and sequencing of D-proteins

The development of mirror-image biology systems and related applications is hindered by the lack of effective methods to sequence mirror-image (D-) proteins. Although natural-chirality (L-) proteins can be sequenced by bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS), the sequencing of long D-peptides and D-proteins with the same strategy requires digestion by a site-specific D-protease before mass analysis. Here we apply solid-phase peptide synthesis and native chemical ligation to chemically synthesize a mirror-image version of trypsin, a widely used protease for site-specific protein digestion. Using mirror-image trypsin digestion and LC-MS/MS, we sequence a mirror-image large subunit ribosomal protein (L25) and a mirror-image Sulfolobus solfataricus P2 DNA polymerase IV (Dpo4), and distinguish between different mutants of D-Dpo4. We also perform writing and reading of digital information in a long D-peptide of 50 amino acids. Thus, mirror-image trypsin digestion in conjunction with LC-MS/MS may facilitate practical applications of D-peptides and D-proteins as potential therapeutic and informational tools.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the "entrapment database" strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

The utility of proteases in proteomics, from sequence profiling to structure and function analysis

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Mirror proteases of Ac-Trypsin and Ac-LysargiNase precisely improve novel event identifications in Mycolicibacterium smegmatis MC2 155 by proteogenomic analysis

Accurate identification of novel peptides remains challenging because of the lack of evaluation criteria in large-scale proteogenomic studies. Mirror proteases of trypsin and lysargiNase can generate complementary b/y ion series, providing the opportunity to efficiently assess authentic novel peptides in experiments other than filter potential targets by different false discovery rates (FDRs) ranking. In this study, a pair of in-house developed acetylated mirror proteases, Ac-Trypsin and Ac-LysargiNase, were used in Mycolicibacterium smegmatis MC² 155 for proteogenomic analysis. The mirror proteases accurately identified 368 novel peptides, exhibiting 75–80% b and y ion coverages against 65–68% y or b ion coverages of Ac-Trypsin (38.9% b and 68.3% y) or Ac-LysargiNase (65.5% b and 39.6% y) as annotated peptides from M. smegmatis MC² 155. The complementary b and y ion series largely increased the reliability of overlapped sequences derived from novel peptides. Among these novel peptides, 311 peptides were annotated in other public M. smegmatis strains, and 57 novel peptides with more continuous b and y pairs were obtained for further analysis after spectral quality assessment. This enabled mirror proteases to successfully correct six annotated proteins' N-termini and detect 17 new coding open reading frames (ORFs). We believe that mirror proteases will be an effective strategy for novel peptide detection in both prokaryotic and eukaryotic proteogenomics.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Substrate preferences, phylogenetic and biochemical properties of proteolytic bacteria present in the digestive tract of Nile tilapia (Oreochromis niloticus)

Vertebrate intestine appears to be an excellent source of proteolytic bacteria for industrial and probiotic use. We therefore aimed at obtaining the gut-associated proteolytic species of Nile tilapia (Oreochromis niloticus). We have isolated twenty six bacterial strains from its intestinal tract, seven of which showed exoprotease activity with the formation of clear halos on skim milk. Their depolymerization ability was further assessed on three distinct proteins including casein, gelatin, and albumin. All the isolates could successfully hydrolyze the three substrates indicating relatively broad specificity of their secreted proteases. Molecular taxonomy and phylogeny of the proteolytic isolates were determined based on their 16S rRNA gene barcoding, which suggested that the seven strains belong to three phyla viz. Firmicutes, Proteobacteria, and Actinobacteria, distributed across the genera Priestia, Citrobacter, Pseudomonas, Stenotrophomonas, Burkholderia, Providencia, and Micrococcus. The isolates were further characterized by a comprehensive study of their morphological, cultural, cellular and biochemical properties which were consistent with the phylogenetic annotations. To reveal their proteolytic capacity alongside substrate preferences, enzyme-production was determined by the diffusion assay. The Pseudomonas, Stenotrophomonas and Micrococcus isolates appeared to be most promising with maximum protease production on casein, gelatin, and albumin media respectively. Our findings present valuable insights into the phylogenetic and biochemical properties of gut-associated proteolytic strains of Nile tilapia.

Mirror-Cutting-Based Digestion Strategy Enables the In-Depth and Accuracy Characterization of N-Linked Protein Glycosylation

N-linked glycosylation plays important roles in multiple physiological and pathological processes, while the analysis coverage is still limited due to the insufficient digestion of glycoproteins, as well as incomplete ion fragments for intact glycopeptide determination. Herein, a mirror-cutting-based digestion strategy was proposed by combining two orthogonal proteases of LysargiNase and trypsin to characterize the macro- and micro-heterogeneity of protein glycosylation. Using the above two proteases, the b- or y-ion series of peptide sequences were, respectively, enhanced in MS/MS, generating the complementary spectra for peptide sequence identification. More than 27% (489/1778) of the site-specific glycoforms identified by LysargiNase digestion were not covered by trypsin digestion, suggesting the elevated coverage of protein sequences and site-specific glycoforms by the mirror-cutting method. Totally, 10,935 site-specific glycoforms were identified from mouse brain tissues in the 18 h MS analysis, which significantly enhanced the coverage of protein glycosylation. Intriguingly, 27 mannose-6-phosphate (M6P) glycoforms were determined with core fucosylation, and 23 of them were found with the "Y-HexNAc-Fuc" ions after manual checking. This is hitherto the first report of M6P and fucosylation co-modifications of glycopeptides, in which the mechanism and function still needs further exploration. The mirror-cutting digestion strategy also has great application potential in the exploration of missing glycoproteins from other complex samples to provide rich resources for glycobiology research.

Mapping Microproteins and ncRNA-Encoded Polypeptides in Different Mouse Tissues

Small open reading frame encoded peptides (SEPs), also called microproteins, play a vital role in biological processes. Plenty of their open reading frames are located within the non-coding RNA (ncRNA) range. Recent research has demonstrated that ncRNA-encoded polypeptides have essential functions and exist ubiquitously in various tissues. To better understand the role of microproteins, especially ncRNA-encoded proteins, expressed in different tissues, we profiled the proteomic characterization of five mouse tissues by mass spectrometry, including bottom-up, top-down, and de novo sequencing strategies. Bottom-up and top-down with database-dependent searches identified 811 microproteins in the OpenProt database. De novo sequencing identified 290 microproteins, including 12 ncRNA-encoded microproteins that were not found in current databases. In this study, we discovered 1,074 microproteins in total, including 270 ncRNA-encoded microproteins. From the annotation of these microproteins, we found that the brain contains the largest number of neuropeptides, while the spleen contains the most immunoassociated microproteins. This suggests that microproteins in different tissues have tissue-specific functions. These unannotated ncRNA-coded microproteins have predicted domains, such as the macrophage migration inhibitory factor domain and the Prefoldin domain. These results expand the mouse proteome and provide insight into the molecular biology of mouse tissues.

Leveraging proteomics in orphan disease research: pitfalls and potential

Introduction: The term 'orphan diseases' includes conditions meeting prevalence-based or commercial viability criteria: they affect a small number of individuals and are considered an unviable market for drug development. Proteomics is an important technology to study them, providing information on mechanisms and evolution, biomarkers, and effects of therapeutic interventions.Areas covered: Herein, we review how proteomics and bioinformatic tools could be applied to the study of rare diseases and discuss pitfalls and potential.Expert opinion: Research in the field of rare diseases has to face many challenges, and implementation plans should foresee highly specialized collaborative consortia to create multidisciplinary frameworks for data sharing, advancing research, supporting clinical studies, and accelerating drug development. The integration of different technologies will allow better knowledge of disease pathophysiology, and the inclusion of proteomics and other omics technologies in this context will be pivotal to this aim.Several aspects of rare diseases, often perceived as limiting factors, might actually be advantages for a precision medicine approach: the limited number of patients, the collaboration with patient societies, and the availability of curated clinical registries could allow the development of homogeneous clinical databases and ultimately a better control over the data to be analyzed.

Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

Deadly Proteomes: A Practical Guide to Proteotranscriptomics of Animal Venoms

Animal venoms are renowned for their toxicity, biochemical complexity, and as a source of compounds with potential applications in medicine, agriculture, and industry. Polypeptides underlie much of the pharmacology of animal venoms, and elucidating these arsenals of polypeptide toxins-known as the venom proteome or venome-is an important step in venom research. Proteomics is used for the identification of venom toxins, determination of their primary structure including post-translational modifications, as well as investigations into the physiology underlying their production and delivery. Advances in proteomics and adjacent technologies has led to a recent upsurge in publications reporting venom proteomes. Improved mass spectrometers, better proteomic workflows, and the integration of next-generation sequencing of venom-gland transcriptomes and venomous animal genomes allow quicker and more accurate profiling of venom proteomes with greatly reduced starting material. Technologies such as imaging mass spectrometry are revealing additional insights into the mechanism, location, and kinetics of venom toxin production. However, these numerous new developments may be overwhelming for researchers designing venom proteome studies. Here, the field of venom proteomics is reviewed and some practical solutions for simplifying mass spectrometry workflows to study animal venoms are offered.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Tryp-N: A Thermostable Protease for the Production of N-terminal Argininyl and Lysinyl Peptides

Bottom-up proteomics is a mainstay in protein identification and analysis. These studies typically employ proteolytic treatment of biological samples to generate suitably sized peptides for tandem mass spectrometric (MS) analysis. In MS, fragmentation of peptides is largely driven by charge localization. Consequently, peptides with basic centers exclusively on their N-termini produce mainly b-ions. Thus, it was long ago realized that proteases that yield such peptides would be valuable proteomic tools for achieving simplified peptide fragmentation patterns and peptide assignment. Work by several groups has identified such proteases, however, structural analysis of these suggested that enzymatic optimization was possible. We therefore endeavored to find enzymes that could provide enhanced activity and versatility while maintaining specificity. Using these previously described proteases as informatic search templates, we discovered and then characterized a thermophilic metalloprotease with N-terminal specificity for arginine and lysine. This enzyme, dubbed Tryp-N, affords many advantages including improved thermostability, solvent and detergent tolerance, and rapid digestion time.

Kinetic Studies of the Effect of pH on the Trypsin-Catalyzed Hydrolysis of N-α-benzyloxycarbonyl-l-lysine-p-nitroanilide: Mechanism of Trypsin Catalysis

The pH dependence of the trypsin-catalyzed hydrolysis of N-α-benzyloxycarbonyl-l-lysine p-nitroanilide has been studied at 25 °C. k _cat/K _M was maximal at alkaline pH values but decreased with decreasing pH. k _cat/K _M was dependent on free enzyme pK _a values of 6.75 ± 0.09 and 4.10 ± 0.13, which were assigned to the ionization of the active site histidine-57 and aspartate-189, respectively. Protonation of either group abolished catalytic activity. k _cat is shown to equal the acylation rate constant k ₂ over the pH range studied. k ₂ decreased on the protonation of two groups with pK _a values of 4.81 ± 0.15 and 4.23 ± 0.19. We assign the pK _a of 4.23 to the ionization of the aspartate-189 residue and the pK _a of 4.81 to the oxyanion of the tetrahedral intermediate formed during acylation. We conclude that during acylation, breakdown of the catalytic tetrahedral intermediate is rate-limiting and that there is a strong interaction between the imidazolium ion of histidine-57 and the oxyanion of the catalytic tetrahedral intermediate, which perturbs their pK _a values. From the pH dependence of k ₃, we conclude that deacylation depends on a pK _a of 6.41 ± 0.22 and that the ionization of the carboxylate group of aspartate-189 does not have a significant effect on the rate of deacylation (k ₃). A catalytic mechanism is proposed to explain the pH dependence of catalysis.

Real-time laser induced chemical derivatizations of peptide N-Terminus for in-situ mass spectrometric sequencing at sub-picomole and nanosecond scale

Distinguishing b- and y-ions is essential to compute amino acid sequences from either N- or C-terminus in mass spectrometry. We described herein a solvent free and real time on-plate derivatization approach that can tag N-terminus of peptides at microliter level with p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde for matrix assisted laser desorption ionization mass spectrometry (MALDI MS). Less than 1 μL of sample solutions can be directly mixed with equal volumes of p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde and α-cyano-4-hydroxycinnamic acid (CHCA), a matrix compound to co-crystalize with analytes for efficient absorption of laser energy and peptide ionization. When the mixture spotted on the sample plate is irradiated with the 3rd harmonic (355 nm) of Nd³⁺:YAG laser pulses (3 ns width), N-terminal amine groups of peptides instantly react with carbonyl groups of chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde. Resultant peptides carrying with on-plate formed azomethine group (-CN-) are simultaneously protonated and isolated as precursor ions for subsequent collision-activated dissociation. The mass shift with unique Cl isotopic signature unambiguously distinguishes b ions from y ions and other ions. This method does not need extensive sample preparation and is useful for those samples with limited quantities down to sub-picomole level in sub-microliter volumes. The efficiency was demonstrated with synthetic peptides and tryptic peptides of model proteins. It was found that 2-hydroxy-5-methylisophthalaldehyde provides improved yield for peptides containing lysine residues. Unknown proteins of human saliva and bovine milk as well as phosphopeptides have been identified.

MS-based strategies for identification of protein SUMOylation modification

Protein SUMOylation modification conjugated with small ubiquitin-like modifiers (SUMOs) is one kind of PTMs, which exerts comprehensive roles in cellular functions, including gene expression regulation, DNA repair, intracellular transport, stress responses, and tumorigenesis. With the development of the peptide enrichment approaches and MS technology, more than 6000 SUMOylated proteins and about 40 000 SUMO acceptor sites have been identified. In this review, we summarize several popular approaches that have been developed for the identification of SUMOylated proteins in human cells, and further compare their technical advantages and disadvantages. And we also introduce identification approaches of target proteins which are co-modified by both SUMOylation and ubiquitylation. We highlight the emerging trends in the SUMOylation field as well. Especially, the advent of the clustered regularly interspaced short palindromic repeats/ Cas9 technique will facilitate the development of MS for SUMOylation identification.

Mass Spectrometry-Based Quantification of Tau in Human Cerebrospinal Fluid Using a Complementary Tryptic Peptide Standard

Here, we report a method for the generation of complementary tryptic (CompTryp) isotope-labeled peptide standards for the relative and absolute quantification of proteins by mass spectrometry (MS). These standards can be digested in parallel with either trypsin (Tryp-C) or trypsin-N (Tryp-N), to generate peptides that significantly overlap in primary sequence having C- and N-terminal arginine and lysine residues, respectively. As a proof of concept, an isotope-labeled CompTryp standard was synthesized for Tau, a well-established biomarker in Alzheimer's disease (AD), which included both N- and C-terminal heavy isotope-labeled (¹⁵N and ¹³C) arginine residues and flanking amino acid sequences to monitor proteolytic digestion. Despite having the exact same mass, the N- and C-terminal heavy Tau peptides are distinguishable by retention time and MS/MS fragmentation profiles. The isotope-labeled Tau CompTryp standard was added to human cerebrospinal fluid (CSF) followed by parallel digestion with Tryp-N and Tryp-C. The native and isotope-labeled peptide pairs were quantified by parallel reaction monitoring (PRM) in a single assay. Notably, both tryptic peptides were effective at quantifying Tau in human CSF, and both showed a significant difference in CSF Tau levels between AD and controls. Treating these CompTryp Tau peptide measurements as independent replicates also improved the coefficient of variation and correlation with Tau immunoassays. More broadly, we propose that CompTryp standards can be generated for any protein of interest, providing an efficient method to improve the robustness and reproducibility for MS analysis of clinical and research samples.