Oxidative photocatalysis on membranes triggers non-canonical pyroptosis.

Intracellular membranes composing organelles of eukaryotes include membrane proteins playing crucial roles in physiological functions. However, a comprehensive understanding of the cellular responses triggered by intracellular membrane-focused oxidative stress remains elusive. Herein, we report an amphiphilic photocatalyst localised in intracellular membranes to damage membrane proteins oxidatively, resulting in non-canonical pyroptosis. Our developed photocatalysis generates hydroxyl radicals and hydrogen peroxides via water oxidation, which is accelerated under hypoxia. Single-molecule magnetic tweezers reveal that photocatalysis-induced oxidation markedly destabilised membrane protein folding. In cell environment, label-free quantification reveals that oxidative damage occurs primarily in membrane proteins related to protein quality control, thereby aggravating mitochondrial and endoplasmic reticulum stress and inducing lytic cell death. Notably, the photocatalysis activates non-canonical inflammasome caspases, resulting in gasdermin D cleavage to its pore-forming fragment and subsequent pyroptosis. These findings suggest that the oxidation of intracellular membrane proteins triggers non-canonical pyroptosis.

DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.

Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep.

Deephos: predicted spectral database search for TMT-labeled phosphopeptides and its false discovery rate estimation.

MOTIVATION: Tandem mass tag (TMT)-based tandem mass spectrometry (MS/MS) has become the method of choice for the quantification of post-translational modifications in complex mixtures. Many cancer proteogenomic studies have highlighted the importance of large-scale phosphopeptide quantification coupled with TMT labeling. Herein, we propose a predicted Spectral DataBase (pSDB) search strategy called Deephos that can improve both sensitivity and specificity in identifying MS/MS spectra of TMT-labeled phosphopeptides. RESULTS: With deep learning-based fragment ion prediction, we compiled a pSDB of TMT-labeled phosphopeptides generated from ∼8000 human phosphoproteins annotated in UniProt. Deep learning could successfully recognize the fragmentation patterns altered by both TMT labeling and phosphorylation. In addition, we discuss the decoy spectra for false discovery rate (FDR) estimation in the pSDB search. We show that FDR could be inaccurately estimated by the existing decoy spectra generation methods and propose an innovative method to generate decoy spectra for more accurate FDR estimation. The utilities of Deephos were demonstrated in multi-stage analyses (coupled with database searches) of glioblastoma, acute myeloid leukemia and breast cancer phosphoproteomes. AVAILABILITY AND IMPLEMENTATION: Deephos pSDB and the search software are available at https://github.com/seungjinna/deephos.

TIDD: tool-independent and data-dependent machine learning for peptide identification.

BACKGROUND: In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine. RESULTS: We propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23-38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience. CONCLUSIONS: TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

Cataract-Associated New Mutants S175G/H181Q of ßΒ2-Crystallin and P24S/S31G of γD-Crystallin Are Involved in Protein Aggregation by Structural Changes.

ß/γ-Crystallins, the main structural protein in human lenses, have highly stable structure for keeping the lens transparent. Their mutations have been linked to cataracts. In this study, we identified 10 new mutations of ß/γ-crystallins in lens proteomic dataset of cataract patients using bioinformatics tools. Of these, two double mutants, S175G/H181Q of ßΒ2-crystallin and P24S/S31G of γD-crystallin, were found mutations occurred in the largest loop linking the distant ß-sheets in the Greek key motif. We selected these double mutants for identifying the properties of these mutations, employing biochemical assay, the identification of protein modifications with nanoUPLC-ESI-TOF tandem MS and examining their structural dynamics with hydrogen/deuterium exchange-mass spectrometry (HDX-MS). We found that both double mutations decrease protein stability and induce the aggregation of ß/γ-crystallin, possibly causing cataracts. This finding suggests that both the double mutants can serve as biomarkers of cataracts.

Proteogenomic Approach to UTR Peptide Identification.

Recent sequencing technologies have highlighted translation of untranslated regions (UTRs) in genomes, although it remains unknown whether the translated products persist in a cell. Here, we propose a proteogenomic approach to UTR identification at the proteome level, which has been challenging due to the lack of corresponding sequences required for peptide spectrum matching. We address the challenge with constructing translated UTR (tUTR) database, consisting of all hypothetical sequences that can be translated from UTR by assuming non-AUG initiation at near-cognate start codons and stop codon readthrough. In the analysis of the H1299 cell line mass spectrometry (MS/MS) dataset, the tUTR DB-based proteogenomic approach enabled the detection of 52 5'-UTR and 9 3'-UTR peptides from 45 and 9 genes, respectively. The identified UTR peptides were validated via high spectral similarity with their synthetic peptides. The 5'-UTR peptides pointed out alternative initiation sites with non-AUG start codons, which exactly conformed to Kozak contexts of annotated initiation sites. It is also worth noting that our approach can detect translated amino acid sequences as well as provide evidence for UTR translation, while ribosome profiling provides only the translation evidence. For previously reported stop codon readthrough in MDH1 gene, we could confirm the amino acid inserted during the readthrough. Data are available via ProteomeXchange with identifier PXD016207.

The Antibody Repertoire of Colorectal Cancer.

Immunotherapy is becoming increasingly important in the fight against cancers, using and manipulating the body's immune response to treat tumors. Understanding the immune repertoire-the collection of immunological proteins-of treated and untreated cells is possible at the genomic, but technically difficult at the protein level. Standard protein databases do not include the highly divergent sequences of somatic rearranged immunoglobulin genes, and may lead to miss identifications in a mass spectrometry search. We introduce a novel proteogenomic approach, AbScan, to identify these highly variable antibody peptides, by developing a customized antibody database construction method using RNA-seq reads aligned to immunoglobulin (Ig) genes.AbScan starts by filtering transcript (RNA-seq) reads that match the template for Ig genes. The retained reads are used to construct a repertoire graph using the "split" de Bruijn graph: a graph structure that improves on the standard de Bruijn graph to capture the high diversity of Ig genes in a compact manner. AbScan corrects for sequencing errors, and converts the graph to a format suitable for searching with MS/MS search tools. We used AbScan to create an antibody database from 90 RNA-seq colorectal tumor samples. Next, we used proteogenomic analysis to search MS/MS spectra of matched colorectal samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) against the AbScan generated database. AbScan identified 1,940 distinct antibody peptides. Correlating with previously identified Single Amino-Acid Variants (SAAVs) in the tumor samples, we identified 163 pairs (antibody peptide, SAAV) with significant cooccurrence pattern in the 90 samples. The presence of coexpressed antibody and mutated peptides was correlated with survival time of the individuals. Our results suggest that AbScan (https://github.com/csw407/AbScan.git) is an effective tool for a proteomic exploration of the immune response in cancers.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks.

Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software.

Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer.

Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets. Here, we describe an integrative proteogenomic method that extends the limit of proteogenomic searches to identify multiple variant peptides as well as immunoglobulin gene variations/rearrangements using customized mining of RNA-seq data. Our results also provide the first extensive characterization of tumor immune response and demonstrate the potential of this method to improve the molecular characterization of tumor subtypes.

Characterization of disulfide bonds by planned digestion and tandem mass spectrometry.

The identification of disulfide bonds provides critical information regarding the structure and function of a protein and is a key aspect in understanding signaling cascades in biological systems. Recent proteomic approaches using digestion enzymes have facilitated the characterization of disulfide-bonds and/or oxidized products from cysteine residues, although these methods have limitations in the application of MS/MS. For example, protein digestion to obtain the native form of disulfide bonds results in short lengths of amino acids, which can cause ambiguous MS/MS analysis due to false positive identifications. In this study we propose a new approach, termed planned digestion, to obtain sufficient amino acid lengths after cleavage for proteomic approaches. Application of the DBond software to planned digestion of specific proteins accurately identified disulfide-linked peptides. RNase A was used as a model protein in this study because the disulfide bonds of this protein have been well characterized. Application of this approach to peptides digested with Asp-N/C (chemical digestion) and trypsin under acid hydrolysis conditions identified the four native disulfide bonds of RNase A. Missed cleavages introduced by trypsin treatment for only 3 hours generated sufficient lengths of amino acids for identification of the disulfide bonds. Analysis using MS/MS successfully showed additional fragmentation patterns that are cleavage products of S-S and C-S bonds of disulfide-linkage peptides. These fragmentation patterns generate thioaldehydes, persulfide, and dehydroalanine. This approach of planned digestion with missed cleavages using the DBond algorithm could be applied to other proteins to determine their disulfide linkage and the oxidation patterns of cysteine residues.

Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data.

Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular subtyping of cancers, understanding cancer progression, and the discovery of novel biomarkers. The advances of genomics technologies (whole-genome exome, and transcript sequencing, collectively referred to as NGS (next-generation sequencing)) have fueled recent studies on somatic mutation discovery. However, the vision is challenged by the complexity, redundancy, and errors in genomic data, and the difficulty of investigating the proteome translated portion of aberrant genes using only genomic approaches. Combination of proteomic and genomic technologies are increasingly being employed. Various strategies have been employed to allow the usage of large-scale NGS data for conventional MS/MS searches. This paper provides a discussion of applying different strategies relating to large database search, and FDR (false discovery rate) -based error control, and their implication to cancer proteogenomics. Moreover, it extends and develops the idea of a unified genomic variant database that can be searched by any MS sample. A total of 879 BAM files downloaded from TCGA repository were used to create a 4.34 GB unified FASTA database that contained 2787062 novel splice junctions, 38,464 deletions, 1,105 insertions, and 182,302 substitutions. Proteomic data from a single ovarian carcinoma sample (439,858 spectra) was searched against the database. By applying the most conservative FDR measure, we have identified 524 novel peptides and 65,578 known peptides at 1% FDR threshold. The novel peptides include interesting examples of doubly mutated peptides, frame-shifts, and nonsample-recruited mutations, which emphasize the strength of our approach.

Reinvestigation of aminoacyl-tRNA synthetase core complex by affinity purification-mass spectrometry reveals TARSL2 as a potential member of the complex.

Twenty different aminoacyl-tRNA synthetases (ARSs) link each amino acid to their cognate tRNAs. Individual ARSs are also associated with various non-canonical activities involved in neuronal diseases, cancer and autoimmune diseases. Among them, eight ARSs (D, EP, I, K, L, M, Q and RARS), together with three ARS-interacting multifunctional proteins (AIMPs), are currently known to assemble the multi-synthetase complex (MSC). However, the cellular function and global topology of MSC remain unclear. In order to understand the complex interaction within MSC, we conducted affinity purification-mass spectrometry (AP-MS) using each of AIMP1, AIMP2 and KARS as a bait protein. Mass spectrometric data were funneled into SAINT software to distinguish true interactions from background contaminants. A total of 40, 134, 101 proteins in each bait scored over 0.9 of SAINT probability in HEK 293T cells. Complex-forming ARSs, such as DARS, EPRS, IARS, Kars, LARS, MARS, QARS and RARS, were constantly found to interact with each bait. Variants such as, AIMP2-DX2 and AIMP1 isoform 2 were found with specific peptides in KARS precipitates. Relative enrichment analysis of the mass spectrometric data demonstrated that TARSL2 (threonyl-tRNA synthetase like-2) was highly enriched with the ARS-core complex. The interaction was further confirmed by coimmunoprecipitation of TARSL2 with other ARS core-complex components. We suggest TARSL2 as a new component of ARS core-complex.

Monoisotopic mass determination algorithm for selenocysteine-containing polypeptides from mass spectrometric data based on theoretical modeling of isotopic peak intensity ratios.

Selenoproteins, containing selenocysteine (Sec, U) as the 21st amino acid in the genetic code, are well conserved from bacteria to human, except yeast and higher plants that miss the Sec insertion machinery. Determination of Sec association is important to find substrates and to understand redox action of selenoproteins. While mass spectrometry (MS) has become a common and powerful tool to determine an amino acid sequence of a protein, identification of a protein sequence containing Sec was not easy using MS because of the limited stability of Sec in selenoproteins. Se has six naturally occurring isotopes, 74Se, 76Se, 77Se, 78Se, 8°Se, and 8²Se, and 8°Se is the most abundant isotope. These characteristics provide a good indicator for selenopeptides but make it difficult to detect selenopeptides using software analysis tools developed for common peptides. Thus, previous reports verified MS scans of selenopeptides by manual inspection. None of the fully automated algorithms have taken into account the isotopes of Se, leading to the wrong interpretation for selenopeptides. In this paper, we present an algorithm to determine monoisotopic masses of selenocysteine-containing polypeptides. Our algorithm is based on a theoretical model for an isotopic distribution of a selenopeptide, which regards peak intensities in an isotopic distribution as the natural abundances of C, H, N, O, S, and Se. Our algorithm uses two kinds of isotopic peak intensity ratios: one for two adjacent peaks and another for two distant peaks. It is shown that our algorithm for selenopeptides performs accurately, which was demonstrated with two LC-MS/MS data sets. Using this algorithm, we have successfully identified the Sec-Cys and Sec-Sec cross-linking of glutaredoxin 1 (GRX1) from mass spectra obtained by UPLC-ESI-q-TOF instrument.

High-throughput peptide quantification using mTRAQ reagent triplex.

BACKGROUND: Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents. METHODS AND RESULTS: Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments. CONCLUSIONS: We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

Novel oxidative modifications in redox-active cysteine residues.

Redox-active cysteine, a highly reactive sulfhydryl, is one of the major targets of ROS. Formation of disulfide bonds and other oxidative derivatives of cysteine including sulfenic, sulfinic, and sulfonic acids, regulates the biological function of various proteins. We identified novel low-abundant cysteine modifications in cellular GAPDH purified on 2-dimensional gel electrophoresis (2D-PAGE) by employing selectively excluded mass screening analysis for nano ultraperformance liquid chromatography-electrospray-quadrupole-time of flight tandem mass spectrometry, in conjunction with MODi and MODmap algorithm. We observed unexpected mass shifts (Δm=-16, -34, +64, +87, and +103 Da) at redox-active cysteine residue in cellular GAPDH purified on 2D-PAGE, in oxidized NDP kinase A, peroxiredoxin 6, and in various mitochondrial proteins. Mass differences of -16, -34, and +64 Da are presumed to reflect the conversion of cysteine to serine, dehydroalanine (DHA), and Cys-SO2-SH respectively. To determine the plausible pathways to the formation of these products, we prepared model compounds and examined the hydrolysis and hydration of thiosulfonate (Cys-S-SO2-Cys) either to DHA (Δm=-34 Da) or serine along with Cys-SO2-SH (Δm=+64 Da). We also detected acrylamide adducts of sulfenic and sulfinic acids (+87 and +103 Da). These findings suggest that oxidations take place at redox-active cysteine residues in cellular proteins, with the formation of thiosulfonate, Cys-SO2-SH, and DHA, and conversion of cysteine to serine, in addition to sulfenic, sulfinic and sulfonic acids of reactive cysteine.

New algorithm for the identification of intact disulfide linkages based on fragmentation characteristics in tandem mass spectra.

Identifying the sites of disulfide bonds in a protein is essential for thorough understanding of a protein's tertiary and quaternary structures and its biological functions. Disulfide linked peptides are usually identified indirectly by labeling free sulfhydryl groups with alkylating agents, followed by chemical reduction and mass spectral comparison or by detecting the expected masses of disulfide linked peptides on mass scan level. However, these approaches for determination of disulfide bonds become ambiguous when the protein is highly bridged and modified. For accurate identification of disulfide linked peptides, we present here an algorithmic solution for the analysis of tandem mass (MS/MS) spectra of disulfide bonded peptides under nonreducing condition. A new algorithm called "DBond" analyzes disulfide linked peptides based on specific features of disulfide bonds. To determine disulfide linked sites, DBond takes into account fragmentation patterns of disulfide linked peptides in nucleoside diphosphate kinase (NDPK) as a model protein, considering fragment ions including cysteine, cysteine thioaldehyde (-2 Da, C(T)), cysteine persulfide (+32 Da, C(S)) and dehydroalanine (-34 Da, C(Delta)). Using this algorithm, we successfully identified about a dozen novel disulfide bonds in a hexa EF-hand calcium binding protein secretagogin and in a methionine sulfoxide reductase. We believe that DBond, taking into account the disulfide bond fragmentation characteristics and post-translational modifications, offers a novel approach for automatic identification of unknown disulfide bonds and their sites in proteins from MS/MS spectra.

Unrestrictive identification of multiple post-translational modifications from tandem mass spectrometry using an error-tolerant algorithm based on an extended sequence tag approach.

Identification of post-translational modifications (PTMs) is important to understanding the biological functions of proteins. MS/MS is a useful tool to identify PTMs. Most existing search tools are restricted to take only a few types of PTMs as input. Here we describe a new algorithm, called MOD(i) (pronounced "mod eye"), that rapidly searches for all known types of PTMs at once without limiting a multitude of modified sites in a peptide. MOD(i) introduces the notion of a tag chain, a combination structure made from multiple sequence tags, that effectively localizes modified regions within a spectrum and overcomes de novo sequencing errors common in tag-based approaches. MOD(i) showed its performance competence by identifying various types of PTMs in analysis of PTM-rich proteins such as glyceraldehyde-3-phosphate dehydrogenase and lens protein. We demonstrated that MOD(i) innovatively manages the computational complexity of identifying multiple PTMs in a peptide, which may exist in a greater variety than usually expected. In addition, it is suggested that MOD(i) has great potential to discover novel modifications.

CIFTER: automated charge-state determination for peptide tandem mass spectra.

Tandem mass spectrometry (MS/MS) has become a common and useful tool for analyzing complex protein mixtures. Database search programs are the most popular means for peptide identification from MS/MS spectra. However, estimations of charge states of peptide MS/MS spectra obtained from low-resolution mass spectrometers have not been reliable. They require repetitive database searches and additional analyses of the search results. We propose here an algorithm designed to reliably differentiate doubly charged spectra from triply charged ones. We conducted a rigorous analysis of various spectral features and their effects. We employed the distinguishing features found in our analysis and developed a classifier for multiply charged spectra using a machine learning approach. The test on various data sets showed that our method could be successfully applied independent of experimental setup and mass instrument. This algorithm can be used to prefilter spectra so that only reasonably good spectra are submitted to database search programs, thereby saving considerable time. The software for MS/MS charge-state determination, which we named "CIFTER", is available at a website http://prix.uos.ac.kr/sifter/cifter.

Quality assessment of tandem mass spectra based on cumulative intensity normalization.

A large proportion of MS/MS spectral analyses do not result in significant matches because their spectral quality is too poor to produce meaningful identification. Throughput of peptide identification can be greatly improved, if one can filter out, in advance, spectra that would lead to wrong identification. We introduce here an innovative approach to assess spectral quality utilizing a new spectral feature called Xrea, based on cumulative intensity normalization.

MODi: a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra.

MOD(i) (http://modi.uos.ac.kr/modi/) is a powerful and convenient web service that facilitates the interpretation of tandem mass spectra for identifying post-translational modifications (PTMs) in a peptide. It is powerful in that it can interpret a tandem mass spectrum even when hundreds of modification types are considered and the number of potential PTMs in a peptide is large, in contrast to most of the methods currently available for spectra interpretation that limit the number of PTM sites and types being used for PTM analysis. For example, using MOD(i), one can consider for analysis both the entire PTM list published on the unimod webpage (http://www.unimod.org) and user-defined PTMs simultaneously, and one can also identify multiple PTM sites in a spectrum. MOD(i) is convenient in that it can take various input file formats such as .mzXML, .dta, .pkl and .mgf files, and it is equipped with a graphical tool called MassPective developed to display MOD(i)'s output in a user-friendly manner and helps users understand MOD(i)'s output quickly. In addition, one can perform manual de novo sequencing using MassPective.