Protein sequencing by tandem mass spectrometry

Analytical approaches for assessing protein structure in protein-rich food: A comprehensive review

This review focuses on changes in nutrition and functional properties of protein-rich foods, primarily attributed to alterations in protein structures. We provide a comprehensive overview and comparison of commonly used laboratory methods for protein structure identification, aiming to offer readers a convenient understanding of these techniques. The review covers a range of detection technologies employed in food protein analysis and conducts an extensive comparison to identify the most suitable method for various proteins. While these techniques offer distinct advantages for protein structure determination, the inherent complexity of food matrices presents ongoing challenges. Further research is necessary to develop and enhance more robust detection methods to improve accuracy in protein conformation and structure analysis.

Qualitative Monitoring of Proto-Peptide Condensation by Differential FTIR Spectroscopy

Condensation processes such as wet-dry cycling are thought to have played significant roles in the emergence of proto-peptides. Here, we describe a simple and low-cost method, differential Fourier transform infrared (FTIR) spectroscopy, for qualitative analysis of peptide condensation products in model primordial reactions. We optimize differential FTIR for depsipeptides and apply this method to investigate their polymerization in the presence of extraterrestrial dust simulants.

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics.

A critical evaluation of ultrasensitive single-cell proteomics strategies

Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.

Native mass spectrometry analysis of conjugated HSA and BSA complexes with various flavonoids

Mass spectrometry was used to study the binding interaction between serum albumin proteins (BSA and HSA) and flavone dyes, which is known to induce large fluorescence signals for protein detection. By electrospray ionization mass spectrometry (ESI-MS), multiple charged species/states could be produced in ammonium acetate buffer, while preserving the native structures of the proteins. Subsequent introduction of a flavone dye into the buffered solution resulted in an immediate interaction, forming the respective protein-dye conjugates associated by non-covalent interactions. Formation of protein-dye conjugates induced a notable response in the ESI-MS spectra, including changes in both the charge states and molecular mass of the protein species. The resulting data pointed out that the protein-flavone dye maintained a 1 : 1 ratio in the conjugate, although multiple binding sites for drug molecules are present in albumin proteins.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Recent advances in top-down proteome sample processing ahead of MS analysis

Top-down proteomics is emerging as a preferred approach to investigate biological systems, with objectives ranging from the detailed assessment of a single protein therapeutic, to the complete characterization of every possible protein including their modifications, which define the human proteoform. Given the controlling influence of protein modifications on their biological function, understanding how gene products manifest or respond to disease is most precisely achieved by characterization at the intact protein level. Top-down mass spectrometry (MS) analysis of proteins entails unique challenges associated with processing whole proteins while maintaining their integrity throughout the processes of extraction, enrichment, purification, and fractionation. Recent advances in each of these critical front-end preparation processes, including minimalistic workflows, have greatly expanded the capacity of MS for top-down proteome analysis. Acknowledging the many contributions in MS technology and sample processing, the present review aims to highlight the diverse strategies that have forged a pathway for top-down proteomics. We comprehensively discuss the evolution of front-end workflows that today facilitate optimal characterization of proteoform-driven biology, including a brief description of the clinical applications that have motivated these impactful contributions.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Analysis of Arginylated Peptides by Subtractive Edman Degradation

During the early studies of N-terminal arginylation, Edman degradation was widely used to identify N-terminally added Arg on protein substrates. This old method is reliable, but highly depends on the purity and abundance of samples and can become misleading unless a highly purified highly arginylated protein can be obtained. Here, we report a mass spectrometry-based method that utilizes Edman degradation chemistry to identify arginylation in more complex and less abundant protein samples. This method can also apply to the analysis of other posttranslational modifications.

From Peptide Nucleic Acids to Supramolecular Structures of Nucleic Acid Derivatives

Nucleic acids play a pivotal role in life processes. The endeavours to shed light on the essential properties of these intriguing building blocks led us to the synthesis of different analogues and the investigation of their properties. First various peptide nucleic acid monomers and oligomers have been synthesized, using an Fmoc/acyl protecting group strategy, and their properties studied. The serendipitous discovery of a side reaction of coupling agents led us to the elaboration of a peptide sequencing method. The capricious behaviour of guanine derivatives spurred the determination of their substitution pattern using ¹³ C, ¹⁵ N NMR, and mass spectrometric methods. The properties of guanines initiated the logical transition to the study of supramolecular systems composed of purine analogues. Thus, xanthine and uracil derivatives have been obtained and their supramolecular self-assembly properties scrutinized in gas, solid, and liquid states and at solid-liquid interfaces.

Role of Protein Damage Inflicted by Dopamine Metabolites in Parkinson's Disease: Evidence, Tools, and Outlook

Dopamine is an important neurotransmitter that plays a critical role in motivational salience and motor coordination. However, dysregulated dopamine metabolism can result in the formation of reactive electrophilic metabolites which generate covalent adducts with proteins. Such protein damage can impair native protein function and lead to neurotoxicity, ultimately contributing to Parkinson's disease etiology. In this Review, the role of dopamine-induced protein damage in Parkinson's disease is discussed, highlighting the novel chemical tools utilized to drive this effort forward. Continued innovation of methodologies which enable detection, quantification, and functional response elucidation of dopamine-derived protein adducts is critical for advancing this field. Work in this area improves foundational knowledge of the molecular mechanisms that contribute to dopamine-mediated Parkinson's disease progression, potentially assisting with future development of therapeutic interventions.

Food and feed safety of the Bacillus thuringiensis derived protein Vpb4Da2, a novel protein for control of western corn rootworm

Western corn rootworm (WCR), Diabrotica virgifera virgifera, LeConte, is an insect pest that poses a significant threat to the productivity of modern agriculture, causing significant economic and crop losses. The development of genetically modified (GM) crops expressing one or more proteins that confer tolerance to specific insect pests, such as WCR, was a historic breakthrough in agricultural biotechnology and continues to serve as an invaluable tool in pest management. Despite this, evolving resistance to existing insect control proteins expressed in current generation GM crops requires continued identification of new proteins with distinct modes of action while retaining targeted insecticidal efficacy. GM crops expressing insecticidal proteins must undergo extensive safety assessments prior to commercialization to ensure that they pose no increased risk to the health of humans or other animals relative to their non-GM conventional counterparts. As part of these safety evaluations, a weight of evidence approach is utilized to assess the safety of the expressed insecticidal proteins to evaluate any potential risk in the context of dietary exposure. This study describes the food and feed safety assessment of Vpb4Da2, a new Bacillus thuringiensis insecticidal protein that confers in planta tolerance to WCR. Vpb4Da2 exhibits structural and functional similarities to other insect control proteins expressed in commercialized GM crops. In addition, the lack of homology to known toxins or allergens, a lack of acute toxicity in mice, inactivation by conditions commonly experienced in the human gut or during cooking/food processing, and the extremely low expected dietary exposure to Vpb4Da2 provide a substantial weight of evidence to demonstrate that the Vpb4Da2 protein poses no indication of a risk to the health of humans or other animals.

Towards understanding the formation of internal fragments generated by collisionally activated dissociation for top-down mass spectrometry

Top-down mass spectrometry (TD-MS) generates fragment ions that returns information on the polypeptide amino acid sequence. In addition to terminal fragments, internal fragments that result from multiple cleavage events can also be formed. Traditionally, internal fragments are largely ignored due to a lack of available software to reliably assign them, mainly caused by a poor understanding of their formation mechanism. To accurately assign internal fragments, their formation process needs to be better understood. Here, we applied a statistical method to compare fragmentation patterns of internal and terminal fragments of peptides and proteins generated by collisionally activated dissociation (CAD). Internal fragments share similar fragmentation propensities with terminal fragments (e.g., enhanced cleavages N-terminal to proline and C-terminal to acidic residues), suggesting that their formation follows conventional CAD pathways. Internal fragments should be generated by subsequent cleavages of terminal fragments and their formation can be explained by the well-known mobile proton model. In addition, internal fragments can be coupled with terminal fragments to form complementary product ions that span the entire protein sequence. These enhance our understanding of internal fragment formation and can help improve sequencing algorithms to accurately assign internal fragments, which will ultimately lead to more efficient and comprehensive TD-MS analysis of proteins and proteoforms.

Utility of Atmospheric-Pressure Chemical Ionization and Photoionization Mass Spectrometry in Bottom-Up Proteomics

In a typical bottom-up proteomics workflow, proteins are enzymatically cleaved, and the resulting peptides are analyzed by HPLC with electrospray ionization (ESI) tandem mass spectrometry. This approach is practical and widely applied. It has, however, limitations mostly related to less efficient or even inefficient ionization of some peptides in ESI sources. Gas-phase ionization methods like atmospheric-pressure chemical ionization (APCI) or atmospheric-pressure photoionization (APPI) offer alternative ways of detecting various analytes. This work is a systematic study of the ionization efficiencies of peptides in ESI, APCI, and APPI and the applicability of the mentioned ionizations in proteomics. A set of peptide standards and bovine serum albumin digests were examined using a high-resolution mass spectrometer coupled to an ultra HPLC system. Since the ionization efficiency in APCI and APPI depends strongly on experimental conditions, the ion source settings and mobile phase compositions were optimized for each ionization technique. As expected, tryptic peptides were best detected using ESI. The numbers of chymotrypsin peptides successfully detected by ESI, APPI, and APCI were comparable. In the case of Glu-C digest, APPI detected the highest number of peptides. The results suggest that gas-phase ionization techniques, particularly APPI, are an interesting alternative for detecting peptides and delivering complementary data in proteomics.

Identification of Antibacterial Peptide Candidates Encrypted in Stress-Related and Metabolic Saccharomyces cerevisiae Proteins

The protein-rich nature of Saccharomyces cerevisiae has led this yeast to the spotlight concerning the search for antimicrobial peptides. Herein, a <10 kDa peptide-rich extract displaying antibacterial activity was obtained through the autolysis of yeast biomass under mild thermal treatment with self-proteolysis by endogenous peptidases. Estimated IC₅₀ for the peptide pools obtained by FPLC gel filtration indicated improved antibacterial activities against foodborne bacteria and bacteria of clinical interest. Similarly, the estimated cytotoxicity concentrations against healthy human fibroblasts, alongside selective indices ≥10, indicates the fractions are safe, at least in a mixture format, for human tissues. Nano-LC-MS/MS analysis revealed that the peptides in FPLC fractions could be derived from both induced-proteolysis and proteasome activity in abundant proteins, up-regulated under stress conditions during S. cerevisiae biomass manufacturing, including those coded by TDH1/2/3, HSP12, SSA1/2, ADH1/2, CDC19, PGK1, PPI1, PDC1, and GMP1, as well as by other non-abundant proteins. Fifty-eight AMP candidate sequences were predicted following an in silico analysis using four independent algorithms, indicating their possible contribution to the bacterial inactivation observed in the peptides pool, which deserve special attention for further validation of individual functionality. S. cerevisiae-biomass peptides, an unconventional but abundant source of pharmaceuticals, may be promissory adjuvants to treat infectious diseases that are poorly sensitive to conventional antibiotics.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

Implementing Digital-Waveform Technology for Extended m/z Range Operation on a Native Dual-Quadrupole FT-IM-Orbitrap Mass Spectrometer

Here, we describe a digital-waveform dual-quadrupole mass spectrometer that enhances the performance of our drift tube FT-IMS high-resolution Orbitrap mass spectrometer (MS). The dual-quadrupole analyzer enhances the instrument capabilities for studies of large protein and protein complexes. The first quadrupole (q) provides a means for performing low-energy collisional activation of ions to reduce or eliminate noncovalent adducts, viz., salts, buffers, detergents, and/or endogenous ligands. The second quadrupole (Q) is used to mass-select ions of interest for further interrogation by ion mobility spectrometry and/or collision-induced dissociation (CID). Q is operated using digital-waveform technology (DWT) to improve the mass selection compared to that achieved using traditional sinusoidal waveforms at floated DC potentials (>500 V DC). DWT allows for increased precision of the waveform for a fraction of the cost of conventional RF drivers and with readily programmable operation and precision (Hoffman, N. M. . A comparison-based digital-waveform generator for high-resolution duty cycle. Review of Scientific Instruments 2018, 89, 084101).

Analysis of Fragmentation Pathways of Peptide Modified with Quaternary Ammonium and Phosphonium Group as Ionization Enhancers

Peptide modification by a quaternary ammonium group containing a permanent positive charge is a promising method of increasing the ionization efficiency of the analyzed compounds, making ultra-sensitive detection even at the attomolar level possible. Charge-derivatized peptides may undergo both charge remote (ChR) and charge-directed (ChD) fragmentation. A series of model peptide conjugates derivatized with N,N,N-triethyloammonium (TEA), 1-azoniabicyclo[2.2.2]octane (ABCO), 2,4,6-triphenylopyridinium (TPP) and tris(2,4,6-trimetoxyphenylo)phosphonium (TMPP) groups were analyzed by their fragmentation pathways both in collision-induced dissociation (CID) and electron-capture dissociation (ECD) mode. The effect of the fixed-charge tag type and peptide sequence on the fragmentation pathways was investigated. We found that the aspartic acid effect plays a crucial role in the CID fragmentation of TPP and TEA peptide conjugates whereas it was not resolved for the peptides derivatized with the phosphonium group. ECD spectra are mostly dominated by c_n ions. ECD fragmentation of TMPP-modified peptides results in the formation of intense fragments derived from this fixed-charge tag, which may serve as reporter ion.

Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

We present a protein database search engine for the automatic identification of peptide and protein sequences using the recently introduced method of two-dimensional partial covariance mass spectrometry (2D-PC-MS). Because the 2D-PC-MS measurement reveals correlations between fragments stemming from the same or consecutive decomposition processes, the first-of-its-kind 2D-PC-MS search engine is based entirely on the direct matching of the pairs of theoretical and the experimentally detected correlating fragments, rather than of individual fragment signals or their series. We demonstrate that the high structural specificity afforded by 2D-PC-MS fragment correlations enables our search engine to reliably identify the correct peptide sequence, even from a spectrum with a large proportion of contaminant signals. While for peptides, the 2D-PC-MS correlation-matching procedure is based on complementary and internal ion correlations, the identification of intact proteins is entirely based on the ability of 2D-PC-MS to spatially separate and resolve the experimental correlations between complementary fragment ions.

Subset of Fluorophores Is Responsible for Radiation Brightening in Viromimetic Particles

In certain conditions, dye-conjugated icosahedral virus shells exhibit suppression of concentration quenching. The recently observed radiation brightening at high fluorophore densities has been attributed to coherent emission, i.e., to a cooperative process occurring within a subset of the virus-supported fluorophores. Until now, the distribution of fluorophores among potential conjugation sites and the nature of the active subset remained unknown. With the help of mass spectrometry and molecular dynamics simulations, we found which conjugation sites in the brome mosaic virus capsid are accessible to fluorophores. Reactive external surface lysines but also those at the lumenal interface where the coat protein N-termini are located showed virtually unrestricted access to dyes. The third type of labeled lysines was situated at the intercapsomeric interfaces. Through limited proteolysis of flexible N-termini, it was determined that dyes bound to them are unlikely to be involved in the radiation brightening effect. At the same time, specific labeling of genetically inserted cysteines on the exterior capsid surface alone did not lead to radiation brightening. The results suggest that lysines situated within the more rigid structural part of the coat protein provide the chemical environments conducive to radiation brightening, and we discuss some of the characteristics of these environments.

Heterogeneity in proline hydroxylation of fibrillar collagens observed by mass spectrometry

Collagen is the major protein in the extracellular matrix and plays vital roles in tissue development and function. Collagen is also one of the most processed proteins in its biosynthesis. The most prominent post-translational modification (PTM) of collagen is the hydroxylation of Pro residues in the Y-position of the characteristic (Gly-Xaa-Yaa) repeating amino acid sequence of a collagen triple helix. Recent studies using mass spectrometry (MS) and tandem MS sequencing (MS/MS) have revealed unexpected hydroxylation of Pro residues in the X-positions (X-Hyp). The newly identified X-Hyp residues appear to be highly heterogeneous in location and percent occupancy. In order to understand the dynamic nature of the new X-Hyps and their potential impact on applications of MS and MS/MS for collagen research, we sampled four different collagen samples using standard MS and MS/MS techniques. We found considerable variations in the degree of PTMs of the same collagen from different organisms and/or tissues. The rat tail tendon type I collagen is particularly variable in terms of both over-hydroxylation of Pro in the X-position and under-hydroxylation of Pro in the Y-position. In contrast, only a few unexpected PTMs in collagens type I and type III from human placenta were observed. Some observations are not reproducible between different sequencing efforts of the same sample, presumably due to a low population and/or the unpredictable nature of the ionization process. Additionally, despite the heterogeneous preparation and sourcing, collagen samples from commercial sources do not show elevated variations in PTMs compared to samples prepared from a single tissue and/or organism. These findings will contribute to the growing body of information regarding the PTMs of collagen by MS technology, and culminate to a more comprehensive understanding of the extent and the functional roles of the PTMs of collagen.

MS Amanda 2.0: Advancements in the standalone implementation

RATIONALE

Database search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements.

METHODS

In 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high-resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast-growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged.

RESULTS

We present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3-5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types.

CONCLUSIONS

MS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.

Analysis of Antimicrobial Peptide Metabolome of Bacterial Endophyte Isolated From Traditionally Used Medicinal Plant Millettia pachycarpa Benth

Increasing prevalence of antimicrobial resistance (AMR) has posed a major health concern worldwide, and the addition of new antimicrobial agents is diminishing due to overexploitation of plants and microbial resources. Inevitably, alternative sources and new strategies are needed to find novel biomolecules to counter AMR and pandemic circumstances. The association of plants with microorganisms is one basic natural interaction that involves the exchange of biomolecules. Such a symbiotic relationship might affect the respective bio-chemical properties and production of secondary metabolites in the host and microbes. Furthermore, the discovery of taxol and taxane from an endophytic fungus, Taxomyces andreanae from Taxus wallachiana, has stimulated much research on endophytes from medicinal plants. A gram-positive endophytic bacterium, Paenibacillus peoriae IBSD35, was isolated from the stem of Millettia pachycarpa Benth. It is a rod-shaped, motile, gram-positive, and endospore-forming bacteria. It is neutralophilic as per Joint Genome Institute’s (JGI) IMG system analysis. The plant was selected based on its ethnobotany history of traditional uses and highly insecticidal properties. Bioactive molecules were purified from P. peoriae IBSD35 culture broth using 70% ammonium sulfate and column chromatography techniques. The biomolecule was enriched to 151.72-fold and the yield percentage was 0.05. Peoriaerin II, a highly potent and broad-spectrum antimicrobial peptide against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans ATCC 10231 was isolated. LC-MS sequencing revealed that its N-terminal is methionine. It has four negatively charged residues (Asp + Glu) and a total number of two positively charged residues (Arg + Lys). Its molecular weight is 4,685.13 Da. It is linked to an LC-MS/MS inferred biosynthetic gene cluster with accession number A0A2S6P0H9, and blastp has shown it is 82.4% similar to fusaricidin synthetase of Paenibacillus polymyxa SC2. The 3D structure conformation of the BGC and AMP were predicted using SWISS MODEL homology modeling. Therefore, combining both genomic and proteomic results obtained from P. peoriae IBSD35, associated with M. pachycarpa Benth., will substantially increase the understanding of antimicrobial peptides and assist to uncover novel biological agents.

Forensic proteomics

Protein is a major component of all biological evidence, often the matrix that embeds other biomolecules such as polynucleotides, lipids, carbohydrates, and small molecules. The proteins in a sample reflect the transcriptional and translational program of the originating cell types. Because of this, proteins can be used to identify body fluids and tissues, as well as convey genetic information in the form of single amino acid polymorphisms, the result of non-synonymous SNPs. This review explores the application and potential of forensic proteomics. The historical role that protein analysis played in the development of forensic science is examined. This review details how innovations in proteomic mass spectrometry have addressed many of the historical limitations of forensic protein science, and how the application of forensic proteomics differs from proteomics in the life sciences. Two more developed applications of forensic proteomics are examined in detail: body fluid and tissue identification, and proteomic genotyping. The review then highlights developing areas of proteomics that have the potential to impact forensic science in the near future: fingermark analysis, species identification, peptide toxicology, proteomic sex estimation, and estimation of post-mortem intervals. Finally, the review highlights some of the newer innovations in proteomics that may drive further development of the field. In addition to potential impact, this review also attempts to evaluate the stage of each application in the development, validation and implementation process. This review is targeted at investigators who are interested in learning about proteomics in a forensic context and expanding the amount of information they can extract from biological evidence.

mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Protein phylogenetics with mass spectrometry. A comparison of methods

Advances in protein mass spectrometry have provided the ability to identify and sequence proteins with unprecedented speed, sensitivity and accuracy. These benefits now offer advantages for studies of protein evolution and phylogeny avoiding the need to generate and align DNA sequences which can prove time consuming, costly and difficult in the case of large genomes and for highly diverse organisms. The methods of phylogenetic analysis using protein mass spectrometry can be classified into three categories: (1) de novo protein sequencing followed by multiple sequence alignment for classical phylogenetic reconstruction, (2) direct phylogenetic reconstruction using expressed protein mass profiles exploited in microbial biotyping applications, and (3) the construction of trees using proteolytic peptide mass map or fingerprint data. This review describes the three approaches together with the relevant tools and algorithms required to implement them. It also compares each of these alternative protein based methods alongside conventional gene sequence based phylogenetics.

Tandem surface-induced dissociation of protein complexes on an ultrahigh resolution platform

We describe instrumentation for conducting tandem surface-induced dissociation (tSID) of native protein complexes on an ultrahigh resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. The two stages of SID are accomplished with split lenses replacing the entrance lenses of the quadrupole mass filter (stage 1, referred to herein as SID-Q) and the collision cell (stage 2, Q-SID). After SID-Q, the scattered projectile ions and subcomplexes formed in transit traverse the 20 mm pre-filter prior to the mass-selecting quadrupole, providing preliminary insights into the SID fragmentation kinetics of noncovalent protein complexes. The isolated SID fragments (subcomplexes) are then fragmented by SID in the collision cell entrance lens (Q-SID), generating subcomplexes of subcomplexes. We show that the ultrahigh resolution of the FT-ICR can be used for deconvolving species overlapping in m/z, which are particularly prominent in tandem SID spectra due to the combination of symmetric charge partitioning and narrow product ion charge state distributions. Various protein complex topologies are explored, including homotetramers, homopentamers, a homohexamer, and a heterohexamer.

An Algorithm to Improve the Speed of Semi and Non-Specific Enzyme Searches in Proteomics

Background

The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis.

Objective

We aimed to design an algorithm that would improve the speed of semi- and non-specific enzyme searches and could be applicable to existing search programs.

Method

We developed a novel search algorithm that leverages fragment-ion redundancy to simultaneously search multiple non-specifically cleaved peptides at once. Briefly, a theoretical peptide tandem mass spectrum is generated using only the fragment-ion series from a single terminus. This spectrum serves as a proxy for several shorter theoretical peptides sharing the same terminus. After database searching, amino acids are removed from the opposing terminus until the observed and theoretical precursor masses match within a given mass tolerance.

Results

The algorithm was implemented in the search program MetaMorpheus and found to perform an order of magnitude faster than the traditional MetaMorpheus search and produce superior results.

Conclusion

We report a speedy non-specific enzyme search algorithm which is open-source and enables search programs to utilize fragment-ion redundancy to achieve a notable increase in search speed.

Accurate protein structure prediction with hydroxyl radical protein footprinting data

Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.

Advances in venomics: Modern separation techniques and mass spectrometry

Snake venoms are complex chemical mixtures of biologically active proteins and non-protein components. Toxins have a wide range of targets and effects to include ion channels and membrane receptors, and platelet aggregation and platelet plug formation. Toxins target these effectors and effects at high affinity and selectivity. From a pharmacological perspective, snake venom compounds are a valuable resource for drug discovery and development. However, a major challenge to drug discovery using snake venoms is isolating and analyzing the bioactive proteins and peptides in these complex mixtures. Getting molecular information from complex mixtures such as snake venoms requires proteomic analyses, generally combined with transcriptomic analyses of venom glands. The present review summarizes current knowledge and highlights important recent advances in venomics with special emphasis on contemporary separation techniques and bioinformatics that have begun to elaborate the complexity of snake venoms. Several analytical techniques such as two-dimensional gel electrophoresis, RP-HPLC, size exclusion chromatography, ion exchange chromatography, MALDI-TOF-MS, and LC-ESI-QTOF-MS have been employed in this regard. The improvement of separation approaches such as multidimensional-HPLC, 2D-electrophoresis coupled to soft-ionization (MALDI and ESI) mass spectrometry has been critical to obtain an accurate picture of the startling complexity of venoms. In the case of bioinformatics, a variety of software tools such as PEAKS also has been used successfully. Such information gleaned from venomics is important to both predicting and resolving the biological activity of the active components of venoms, which in turn is key for the development of new drugs based on these venom components.

Figainin 1, a Novel Amphibian Skin Peptide with Antimicrobial and Antiproliferative Properties

Amphibian skin secretions are abundant in bioactive compounds, especially antimicrobial peptides. These molecules are generally cationic and rich in hydrophobic amino acids, have an amphipathic structure and adopt an α-helical conformation when in contact with microorganisms membranes. In this work, we purified and characterized Figainin 1, a novel antimicrobial and antiproliferative peptide from the cutaneous secretion of the frog Boana raniceps. Figainin 1 is a cationic peptide with eighteen amino acid residues—rich in leucine and isoleucine, with an amidated C-terminus—and adopts an α-helical conformation in the presence of trifluoroethanol (TFE). It displayed activity against Gram-negative and especially Gram-positive bacteria, with MIC values ranging from 2 to 16 µM, and showed an IC₅₀ value of 15.9 µM against epimastigote forms of T. cruzi; however, Figanin 1 did not show activity against Candida species. This peptide also showed cytolytic effects against human erythrocytes with an HC₅₀ of 10 µM, in addition to antiproliferative activity against cancer cells and murine fibroblasts, with IC₅₀ values ranging from 10.5 to 13.7 µM. Despite its adverse effects on noncancerous cells, Figainin 1 exhibits interesting properties for the development of new anticancer agents and anti-infective drugs against pathogenic microorganisms.

Optical Fiber-Enabled Photoactivation of Peptides and Proteins

Photoactivation and photodissociation have long proven to be useful tools in tandem mass spectrometry, but implementation often involves cumbersome and potentially dangerous configurations. Here, we redress this problem by using a fiber-optic cable to couple an infrared (IR) laser to a mass spectrometer for robust, efficient, and safe photoactivation experiments. Transmitting 10.6 μm IR photons through a hollow-core fiber, we show that such fiber-assisted activated ion-electron transfer dissociation (AI-ETD) and IR multiphoton dissociation (IRMPD) experiments can be carried out as effectively as traditional mirror-based implementations. We report on the transmission efficiency of the hollow-core fiber for conducting photoactivation experiments and perform various intact protein and peptide analyses to illustrate the benefits of fiber-assisted AI-ETD, namely, a simplified system for irradiating the two-dimensional linear ion trap volume concurrent with ETD reactions to limit uninformative nondissociative events and thereby amplify sequence coverage. We also describe a calibration scheme for the routine analysis of IR laser alignment and power through the fiber and into the dual cell quadrupolar linear ion trap. In all, these advances allow for a more robust, straightforward, and safe instrumentation platform, permitting implementation of AI-ETD and IRMPD on commercial mass spectrometers and broadening the accessibility of these techniques.

A nonenzymatic method for cleaving polysaccharides to yield oligosaccharides for structural analysis

Polysaccharides are the most abundant biomolecules in nature, but are the least understood in terms of their chemical structures and biological functions. Polysaccharides cannot be simply sequenced because they are often highly branched and lack a uniform structure. Furthermore, large polymeric structures cannot be directly analyzed by mass spectrometry techniques, a problem that has been solved for polynucleotides and proteins. While restriction enzymes have advanced genomic analysis, and trypsin has advanced proteomic analysis, there has been no equivalent enzyme for universal polysaccharide digestion. We describe the development and application of a chemical method for producing oligosaccharides from polysaccharides. The released oligosaccharides are characterized by advanced liquid chromatography-mass spectrometry (LC-MS) methods with high sensitivity, accuracy and throughput. The technique is first used to identify polysaccharides by oligosaccharide fingerprinting. Next, the polysaccharide compositions of food and feces are determined, further illustrating the utility of technique in food and clinical studies.

The Journey Is the Reward, a Taoist Proverb: John B. Fenn Award for Distinguished Contribution in Mass Spectrometry Lecture

This account provided by John Yates describes his incredible path as a scientist, emphasizing key decisions along the way that shaped his career and led to his landmark contributions to the field of mass spectrometry. Although perhaps best known is the development of the SEQUEST algorithm for automated interpretation of tandem mass spectra of peptides, John's achievements have spanned the field of proteomics and had major impact on the ability to address and solve significant biological problems.

High-Resolution Ion Mobility Separations of Isomeric Glycoforms with Variations on the Peptide and Glycan Levels

Glycosylation is a ubiquitous post-translational modification (PTM) that strongly affects the protein folding and function. Glycosylation patterns are impacted by many diseases, making promising biomarkers. Glycans are also the most complex PTMs, exhibiting isomers (linkage, anomers, and those with isomeric moieties). Permuted with localization variants that occur for all PTMs, these produce numerous isomeric glycoforms. Characterizing them by mass spectrometry and ion mobility spectrometry (IMS) has been a challenge. High-definition differential IMS (FAIMS) had robustly disentangled isomeric peptides involving other PTMs but was not evaluated for glycopeptides that featured multilevel isomerism. Here, we apply it to representative mucin glycopeptides with O-linked glycans: three GalNAc localization variants, a pair with α/β GalNAc anomers, and another with GalNAc/GlcNAc isomers. The first two classes were separated baseline with the resolution exceeding previous benchmarks by 10-fold, and the last pair was partly resolved. The recently demonstrated straightforward coupling to ultrahigh-resolution MS and electron-transfer dissociation makes high-definition FAIMS an attractive tool for glycoproteomics.

Ultraviolet Photodissociation of Tryptic Peptide Backbones at 213 nm

We analyzed the backbone fragmentation behavior of tryptic peptides of a four-protein mixture and of E. coli lysate subjected to ultraviolet photodissociation (UVPD) at 213 nm on a commercially available UVPD-equipped tribrid mass spectrometer. We obtained 15 178 unique high-confidence peptide UVPD spectrum matches by recording a reference beam-type collision-induced dissociation (HCD) spectrum of each precursor, ensuring that our investigation includes a broad selection of peptides, including those that fragmented poorly by UVPD. Type a, b, and y ions were most prominent in UVPD spectra, and median sequence coverage ranged from 5.8% (at 5 ms laser excitation time) to 45.0% (at 100 ms). Overall, the sequence fragment intensity remained relatively low (median: 0.4% (5 ms) to 16.8% (100 ms) of total intensity), and the remaining precursor intensity, high. The sequence coverage and sequence fragment intensity ratio correlated with the precursor charge density, suggesting that UVPD at 213 nm may suffer from newly formed fragments sticking together due to noncovalent interactions. The UVPD fragmentation efficiency therefore might benefit from supplemental activation, as was shown for ETD. Aromatic amino acids, most prominently tryptophan, facilitated UVPD. This points to aromatic tags as possible enhancers of UVPD. Data are available via ProteomeXchange with identifier PXD018176 and on spectrumviewer.org/db/UVPD-213nm-trypPep.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Metabolomics in Central Sensitivity Syndromes

Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties in central sensitization disorders may contribute to the identification of novel therapeutic targets and more precise treatment options. The analysis of small molecules in biological samples through metabolomics has improved greatly and may be the technology needed to identify key moieties in difficult to diagnose diseases. In this review, we discuss the current state of metabolomics as it relates to central sensitization disorders. From initial literature review until Feb 2020, PubMed, Embase, and Scopus were searched for applicable studies. We included cohort studies, case series, and interventional studies of both adults and children affected by central sensitivity syndromes. The majority of metabolomic studies addressing a CSS found significantly altered metabolites that allowed for differentiation of CSS patients from healthy controls. Therefore, the published literature overwhelmingly supports the use of metabolomics in CSS. Further research into these altered metabolites and their respective metabolic pathways may provide more reliable and effective therapeutics for these syndromes.

Graph theory-based reaction pathway searches and DFT calculations for the mechanism studies of free radical-initiated peptide sequencing mass spectrometry (FRIPS MS): a model gas-phase reaction of GGR tri-peptide

Graph theory-based reaction pathway searches (ACE-Reaction program) and density functional theory calculations were performed to shed light on the mechanisms for the production of [a_n + H]⁺, x_n⁺, y_n⁺, z_n⁺, and [y_n + 2H]⁺ fragments formed in free radical-initiated peptide sequencing (FRIPS) mass spectrometry measurements of a small model system of glycine-glycine-arginine (GGR). In particular, the graph theory-based searches, which are rarely applied to gas-phase reaction studies, allowed us to investigate reaction mechanisms in an exhaustive manner without resorting to chemical intuition. As expected, radical-driven reaction pathways were favorable over charge-driven reaction pathways in terms of kinetics and thermodynamics. Charge- and radical-driven pathways for the formation of [y_n + 2H]⁺ fragments were carefully compared, and it was revealed that the [y_n + 2H]⁺ fragments observed in our FRIPS MS spectra originated from the radical-driven pathway, which is in contrast to the general expectation. The acquired understanding of the FRIPS fragmentation mechanism is expected to aid in the interpretation of FRIPS MS spectra. It should be emphasized that graph theory-based searches are powerful and effective methods for studying reaction mechanisms, including gas-phase reactions in mass spectrometry.

Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments

Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results was reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large dataset and use the learned model to evaluate the small-scale experiment. We call this a “static modeling” approach, in contrast to Percolator’s usual “dynamic model” that is trained anew for each dataset. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semi-supervised algorithms to small-scale experiments.

The use of hydrolytic enzymes and multi-stage tandem mass spectrometry to analyze pyridoxal phosphate-modified peptides

A previous approach was established that allowed direct identification of pyridoxal-5'-phosphate (PLP) bonding sites in proteins using mass spectrometry after tryptic proteolysis. The approach required peptide mass fingerprinting owing to suppressed amide backbone fragmentation in favor of side-chain elimination of diagnostic product ions from PLP-derivatized lysyl residues. While sufficient for purified proteins, unambiguous sequence determination is needed to assign PLP bonding sites in unknown proteins in complex mixtures. Here, we describe the use of hydrolytic enzymes and multi-stage tandem mass spectrometry to elucidate the amino acid sequence and PLP bonding site in PLP-modified peptides.

Mass spectra alignment using virtual lock-masses

Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization technologies such as Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART) has rendered high-throughput mass spectrometry possible. It is used for large-scale comparative analysis of populations of samples. In practice, many factors resulting from the environment, the protocol, and even the instrument itself, can lead to minor discrepancies between spectra, rendering automated comparative analysis difficult. In this work, a sequence/pipeline of algorithms to correct variations between spectra is proposed. The algorithms correct multiple spectra by identifying peaks that are common to all and, from those, computes a spectrum-specific correction. We show that these algorithms increase comparability within large datasets of spectra, facilitating comparative analysis, such as machine learning.

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

A Review on Quantitative Multiplexed Proteomics

Over the last few decades, mass spectrometry-based proteomics has become an increasingly powerful tool that is now able to routinely detect and quantify thousands of proteins. A major advance for global protein quantification was the introduction of isobaric tags, which, in a single experiment, enabled the global quantification of proteins across multiple samples. Herein, these methods are referred to as multiplexed proteomics. The principles, advantages, and drawbacks of various multiplexed proteomics techniques are discussed and compared with alternative approaches. We also discuss how the emerging combination of multiplexing with targeted proteomics might enable the reliable and high-quality quantification of very low abundance proteins across multiple conditions. Lastly, we suggest that fusing multiplexed proteomics with data-independent acquisition approaches might enable the comparison of hundreds of different samples without missing values, while maintaining the superb measurement precision and accuracy obtainable with isobaric tag quantification.

Proteomics Analysis of Myelin Composition

The identification and functional characterization of the repertoire of myelin proteins provides a valuable foundation for understanding molecular mechanisms of myelination and the pathogenesis of human myelin disease. Here we provide a procedure for the purification of myelin from rodent or human brains and a large-scale analysis of the myelin proteome, using the shotgun approach of one-dimensional PAGE and liquid chromatography (LC)/tandem mass spectrometry (MS).