Peptide Ions Research Articles

Characterization of Amnion-Derived Membrane for Clinical Wound Applications.

Human amniotic membrane (hAM), the innermost placental layer, has unique properties that allow for a multitude of clinical applications. It is a common misconception that birth-derived tissue products, such as dual-layered dehydrated amnion-amnion graft (dHAAM), are similar regardless of the manufacturing steps. A commercial dHAAM product, Axolotl Biologix DualGraft™, was assessed for biological and mechanical characteristics. Testing of dHAAM included antimicrobial, cellular biocompatibility, proteomics analysis, suture strength, and tensile, shear, and compressive modulus testing. Results demonstrated that the membrane can be a scaffold for fibroblast growth (cellular biocompatibility), containing an average total of 7678 unique proteins, 82,296 peptides, and 96,808 peptide ion variants that may be antimicrobial. Suture strength results showed an average pull force of 0.2 N per dHAAM sample (equating to a pull strength of 8.5 MPa). Tensile modulus data revealed variation, with wet samples showing 5× lower stiffness than dry samples. The compressive modulus and shear modulus displayed differences between donors (lots). This study emphasizes the need for standardized processing protocols to ensure consistency across dHAAM products and future research to explore comparative analysis with other amniotic membrane products. These findings provide baseline data supporting the potential of amniotic membranes in clinical applications.

Electrophoresis-Correlative Data-Independent Acquisition (Eco-DIA) Improves the Sensitivity of Mass Spectrometry for Limited Proteome Amounts.

Capillary zone electrophoresis (CE) combines high separation power, scalability, and speed to limited proteome analyses by mass spectrometry (MS). However, compressed separation in CE challenges the duty cycle of tandem MS, even during data-independent acquisition (DIA). To help remedy this limitation, we introduce the concept of electrophoresis-correlative (Eco) data acquisition for CE-MS. We recognize CE electrospray ionization (ESI) to sort peptide ions into reproducible mass-to-charge (m/z) vs migration time (MT) trends in the solution phase, before subsequent ionization and m/z analysis. We proposed that such a correlation can be leveraged to improve the economy of data acquisition. We test this hypothesis using DIA frames that are tailored to the observed m/z-MT trends. The resulting Eco-DIA method substantially improves the bandwidth utilization of tandem MS during CE-MS. In proof-of-principle studies, Eco-DIA identified and quantified ∼38% more proteins from 1 ng of the HeLa proteome digest compared to the classical DIA, without the assistance of a project-specific tandem MS spectral library. Eco-DIA was able to quantify ∼51% more proteins with <10% coefficient of variation vs the control DIA approach. Based on label-free quantification, the proteins that were exclusively measured by Eco-MS occupied the lower dynamic range of the detected proteome concentration, revealing sensitivity enhancement. In addition to marking the inception of Eco-MS, this work lays the foundation for the development of next-generation data acquisition strategies that leverage electrophoretic ion sorting for high-sensitivity proteomics.

Integrating DIA Single-Cell Proteomics Data with the DiagnoMass Proteomic Hub for Biological Insights.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease's pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

Nanopore ion sources deliver individual ions of amino acids and peptides directly into high vacuum

Electrospray ionization is widely used to generate vapor phase ions for analysis by mass spectrometry in proteomics research. However, only a small fraction of the analyte enters the mass spectrometer due to losses that are fundamentally linked to the use of a background gas to stimulate the generation of ions from electrosprayed droplets. Here we report a nanopore ion source that delivers ions directly into high vacuum from aqueous solutions. The ion source comprises a pulled quartz pipette with a sub-100 nm opening. Ions escape an electrified meniscus by ion evaporation and travel along collisionless trajectories to the ion detector. We measure mass spectra of 16 different amino acid ions, post-translationally modified variants of glutathione, and the peptide angiotensin II, showing that these analytes can be emitted as desolvated ions. The emitted current is composed of ions rather than charged droplets, and more than 90% of the current can be recovered in a distant collector.

Combined release of LL37 peptide and zinc ion from a mussel-inspired coating on porous titanium for infected bone defect repairing

Implant-associated infections impose great burden on patient health and public healthcare. Antimicrobial peptides and metal ions are generally incorporated onto implant surface to deter bacteria colonization. However, it is still challenging to efficiently prevent postoperative infections at non-cytotoxic dosages. Herein, a scaffold based on porous titanium coated with a mussel-inspired dual-diameter TiO2 nanotubes is developed for loading dual drugs of LL37 peptide and Zn2+ with different sizes and characteristics. Benefiting from in-situ formed polydopamine layer and dual-diameter nanotubular structure, the scaffold provides an efficient platform for controllable drugs elution: accelerated release under acidic condition and sustained release for up to 28 days under neutral/alkalescent circumstances. Such combination of dual drugs simultaneously enhanced antibacterial efficacy and osteogenesis. In antibacterial test, LL37 peptide serving as bacteria membrane puncture agent, and Zn2+ acting as ROS generator, cooperatively destroyed bacterial membrane integrity and subsequently damaged bacterial DNA, endowing dual-drug loaded scaffold with remarkable bactericidal efficiency of > 92 % in vitro and > 99 % in vivo. Noteworthily, dual-drug loaded scaffold promoted bone-implant osteointegration under infectious microenvironment, overmatching single-drug load ones. It provides a promising strategy on surface modification of implant for infected bone defect repairing.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SignificanceMass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs.We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation.Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument.Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data.Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Alignment of lyotropic liquid crystals using magnetic nanoparticles improves ionic transport through built-in peptide ion channels

Hypothesis: We hypothesize that simultaneous incorporation of ion channel peptides (in this case, potassium channel as a model) and hydrophobic magnetite Fe3O4 nanoparticles (hFe3O4NPs) within lipidic hexagonal mesophases, and aligning them using an external magnetic field can significantly enhance ion transport through lipid membranes.Experiments: In this study, we successfully characterized the incorporation of gramicidin membrane ion channels and hFe3O4NPs in the lipidic hexagonal structure using SAXS and cryo-TEM methods. Additionally, we thoroughly investigated the conductive characteristics of freestanding films of lipidic hexagonal mesophases, both with and without gramicidin potassium channels, utilizing a range of electrochemical techniques, including impedance spectroscopy, normal pulse voltammetry, and chronoamperometry.Findings: Our research reveals a state-of-the-art breakthrough in enhancing ion transport in lyotropic liquid crystals as matrices for integral proteins and peptides. We demonstrate the remarkable efficacy of membranes composed of hexagonal lipid mesophases embedded with K+ transporting peptides. This enhancement is achieved through doping with hFe3O4NPs and exposure to a magnetic field. We investigate the intricate interplay between the conductive properties of the lipidic hexagonal structure, hFe3O4NPs, gramicidin incorporation, and the influence of Ca2+ on K+ channels. Furthermore, our study unveils a new direction in ion channel studies and biomimetic membrane investigations, presenting a versatile model for biomimetic membranes with unprecedented ion transport capabilities under an appropriately oriented magnetic field. These findings hold promise for advancing membrane technology and various biotechnological and biomedical applications of membrane proteins.

Unveiling Tst3, a Multi-Target Gating Modifier Scorpion α Toxin from Tityus stigmurus Venom of Northeast Brazil: Evaluation and Comparison with Well-Studied Ts3 Toxin of Tityus serrulatus.

Studies on the interaction sites of peptide toxins and ion channels typically involve site-directed mutations in toxins. However, natural mutant toxins exist among them, offering insights into how the evolutionary process has conserved crucial sequences for activities and molecular target selection. In this study, we present a comparative investigation using electrophysiological approaches and computational analysis between two alpha toxins from evolutionarily close scorpion species of the genus Tityus, namely, Tst3 and Ts3 from T. stigmurus and T. serrulatus, respectively. These toxins exhibit three natural substitutions near the C-terminal region, which is directly involved in the interaction between alpha toxins and Nav channels. Additionally, we characterized the activity of the Tst3 toxin on Nav1.1-Nav1.7 channels. The three natural changes between the toxins did not alter sensitivity to Nav1.4, maintaining similar intensities regarding their ability to alter opening probabilities, delay fast inactivation, and induce persistent currents. Computational analysis demonstrated a preference for the down conformation of VSD4 and a shift in the conformational equilibrium towards this state. This illustrates that the sequence of these toxins retained the necessary information, even with alterations in the interaction site region. Through electrophysiological and computational analyses, screening of the Tst3 toxin on sodium isoform revealed its classification as a classic α-NaTx with a broad spectrum of activity. It effectively delays fast inactivation across all tested isoforms. Structural analysis of molecular energetics at the interface of the VSD4-Tst3 complex further confirmed this effect.

Probing Metal Ion Adduction in the ESI Charged Residue Mechanism via Gas-Phase Ion/Ion Chemistry.

The final stages of the charged residue mechanism/model (CRM) for ion generation via electrospray ionization (ESI) involves the binding of excess charge onto analyte species. Ions of both polarities can bind to the analyte with an excess of ions of the same polarity as the droplet. For large biomolecule/biocomplex ions, which are commonly the species of interest in native mass spectrometry (MS), the binding of acids and salts onto the analyte can lead to extensive broadening of ion signals due to adduction. Therefore, heating step(s) to facilitate desolvation and salt adduct removal are commonplace. In this work, we describe an approach to study the final stages of CRM using gas-phase ion/ion reactions to generate analyte ion/salt clusters of well-defined composition, followed by gas-phase collision-induced dissociation (CID). While there are many variables that can be studied systematically via this approach, the work described herein is focused on salt clusters of the form [Na10X11]-, where X = acetate (Ac-), chloride (Cl-), or nitrate (NO3-), in reaction with a common charge state of ubiquitin as well as several model peptides. Experiments in which equimolar quantities of each salt (i.e., NaAc, NaCl, and NaNO3) are subjected to ESI with ubiquitin (Ubi) and gas-phase ion/ion reaction studies involving [Na10X11]- and [Ubi + 6H]6+ show similar trends, in terms of the extent of sodium ion incorporation into the protein ions. Ion/ion reaction studies using model peptides show that the acetate-containing salt transfers significantly more Na+ ions into the peptide ions. Exchange of Na+ for H+ is shown to occur at the C-terminus and at up to all of the amide linkages using [Na10X11]-, whereas only the C-terminus engages in Na+/H+ exchange with [Na10Cl11]- and [Na10(NO3)11]-. In the latter cases, an additional Na+ is taken up as the excess positive charge, presumably due to solvation of the charge by multiple sites (e.g., carbonyl oxygens and basic sites).

Tripeptide-Assisted Gold Nanocluster Formation for Fe3+ and Cu2+ Sensing.

Fluorescent gold nanoclusters (AuNCs) have shown promise as metal ion sensors. Further research into surface ligands is crucial for developing sensors that are both selective and sensitive. Here, we designed simple tripeptides to form fluorescent AuNCs, capitalizing on tyrosine's reduction capability under alkaline conditions. We investigated tyrosine's role in both forming AuNCs and sensing metal ions. Two tripeptides, tyrosine-cysteine-tyrosine (YCY) and serine-cysteine-tyrosine (SCY), were used to form AuNCs. YCY peptides produced AuNCs with blue and red fluorescence, while SCY peptides produced blue-emitting AuNCs. The blue fluorescence of YCY- and SCY-AuNCs was selectively quenched by Fe3+ and Cu2+, whereas red-emitting YCY-AuNC fluorescence remained stable with 13 different metal ions. The number of tyrosine residues influenced the sensor response. DLS measurements revealed different aggregation propensities in the presence of various metal ions, indicating that chelation between the peptide and target ions led to aggregation and fluorescence quenching. Highlighting the innovation of our approach, our study demonstrates the feasibility of the rational design of peptides for the formation of fluorescent AuNCs that serve as highly selective and sensitive surface ligands for metal ion sensing. This method marks an advancement over existing methods due to its dual capability in both synthesizing gold nanoclusters and detecting analytes, specifically Fe3+ and Cu2+.

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Genome-scale annotation of protein binding sites via language model and geometric deep learning

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Identification of calcium chelating peptides from peanut protein hydrolysate and absorption activity of peptide-calcium complex.

Peanut peptides have good chelating ability with metal ions. However, there are few studies on the chelation mechanism of peanut peptides with calcium and absorption properties of peptide-calcium complex. Peptides with high calcium chelating rate were isolated and purified from peanut protein hydrolysate (PPH), and the chelation rate of component F21 was higher (81.4 ± 0.8%). Six peptides were identified from component F21 by liquid chromatography-tandem mass spectrometry, and the frequency of acidic amino acids and arginine in the amino acid sequence was higher in all six peptides. Peanut peptide-calcium complex (PPH21-Ca) was prepared by selecting component F21 (PPH21). Ultraviolet analysis indicated that the chelate reaction occurred between peanut peptide and calcium ions. Fourier transform infrared analysis showed that the chelating sites were carboxyl and amino groups on the amino acid residues of peptides. Scanning electron microscopy revealed that the surface of peanut peptide had a smooth block structure, but the surface of the complex had a granular morphology. Caco-2 cell model tests revealed that the bioavailability of PPH21-Ca was 58.4 ± 0.5%, which was significantly higher than that of inorganic calcium at 37.0 ± 0.4%. Peanut peptides can chelate calcium ions by carboxyl and amino groups, and the peptide-calcium complex had higher bioavailability. This study provides a theoretical basis for the development of new calcium supplement products that are absorbed easily. © 2024 Society of Chemical Industry.

Improved Detection of Tryptic Peptides from Tissue Sections Using Desorption Electrospray Ionization Mass Spectrometry Imaging.

DESI-MSI is an ambient ionization technique used frequently for the detection of lipids, small molecules, and drug targets. Until recently, DESI had only limited use for the detection of proteins and peptides due to the setup and needs around deconvolution of data resulting in a small number of species being detected at lower spatial resolution. There are known differences in the ion species detected using DESI and MALDI for nonpeptide molecules, and here, we identify that this extends to proteomic species. DESI MS images were obtained for tissue sections of mouse and rat brain using a precommercial heated inlet (approximately 450 °C) to the mass spectrometer. Ion mobility separation resolved spectral overlap of peptide ions and significantly improved the detection of multiply charged species. The images acquired were of pixel size 100 μm (rat brain) and 50 μm (mouse brain), respectively. Observed tryptic peptides were filtered against proteomic target lists, generated by LC-MS, enabling tentative protein assignment for each peptide ion image. Precise localizations of peptide ions identified by DESI and MALDI were found to be comparable. Some spatially localized peptides ions were observed in DESI that were not found in the MALDI replicates, typically, multiply charged species with a low mass to charge ratio. This method demonstrates the potential of DESI-MSI to detect large numbers of tryptic peptides from tissue sections with enhanced spatial resolution when compared to previous DESI-MSI studies.

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications.

Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful tool to probe protein dynamics. As a bottom-up technique, HDX-MS provides information at peptide-level resolution, allowing structural localization of dynamic changes. Consequently, the HDX-MS data quality is largely determined by the number of peptides that are identified and monitored after deuteration. Integration of ion mobility (IM) into HDX-MS workflows has been shown to increase the data quality by providing an orthogonal mode of peptide ion separation in the gas phase. This is of critical importance for challenging targets such as integral membrane proteins (IMPs), which often suffer from low sequence coverage or redundancy in HDX-MS analyses. The increasing complexity of samples being investigated by HDX-MS, such as membrane mimetic reconstituted and in vivo IMPs, has generated need for instrumentation with greater resolving power. Recently, Giles et al. developed cyclic ion mobility (cIM), an IM device with racetrack geometry that enables scalable, multipass IM separations. Using one-pass and multipass cIM routines, we use the recently commercialized SELECT SERIES Cyclic IM spectrometer for HDX-MS analyses of four detergent solubilized IMP samples and report its enhanced performance. Furthermore, we develop a novel processing strategy capable of better handling multipass cIM data. Interestingly, use of one-pass and multipass cIM routines produced unique peptide populations, with their combined peptide output being 31 to 222% higher than previous generation SYNAPT G2-Si instrumentation. Thus, we propose a novel HDX-MS workflow with integrated cIM that has the potential to enable the analysis of more complex systems with greater accuracy and speed.

Effects of Nano-Electrospray Ionization Emitter Position on Unintentional In-Source Activation of Peptide and Protein Ions.

Native ion mobility-mass spectrometry (IM-MS) typically introduces protein ions into the gas phase through nano-electrospray ionization (nESI). Many nESI setups have mobile stages for tuning the ion signal and extent of co-solute and salt adduction. However, tuning the position of the emitter capillary in nESI can have unintended downstream consequences for collision-induced unfolding or collision-induced dissociation (CIU/D) experiments. Here, we show that relatively small variations in the nESI emitter position can shift the midpoint (commonly called the "CID50" or "CIU50") potential of CID breakdown curves and CIU transitions by as much as 8 V on commercial instruments. A spatial "map" of the shift in CID50 for the loss of heme from holomyoglobin onto the emitter position on a Waters Synapt G2-Si mass spectrometer shows that emitter positions closer to the instrument inlet can result in significantly greater in-source activation, whereas different effects are found on an Agilent 6545XT instrument for the ions studied. A similar effect is observed for CID of the singly protonated leucine enkephalin peptide and Shiga toxin 1 subunit B homopentamer on the Waters Synapt G2-Si instrument. In-source activation effects on a Waters Synapt G2-Si are also investigated by examining the RMSD between CIU fingerprints acquired at different emitter positions and the shifts in CIU50 for structural transitions of bovine serum albumin and NIST monoclonal antibody.

UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow.

We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).

Two-Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis.

Biopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in-source collision-induced dissociation (IS-CID) with two-dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS-CID is employed as a gas-phase digestion method, i.e., to break down intact full-length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D-MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.

Two‐Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis

AbstractBiopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in‐source collision‐induced dissociation (IS‐CID) with two‐dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS‐CID is employed as a gas‐phase digestion method, i.e., to break down intact full‐length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D‐MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.