A ranking-based scoring function for peptide-spectrum matches

Direct and Indirect Protein Interactions Link FUS Aggregation to Histone Post-Translational Modification Dysregulation and Growth Suppression in an ALS/FTD Yeast Model

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are incurable neurodegenerative disorders sharing pathological and genetic features, including mutations in the FUS gene. FUS is an RNA-binding protein that mislocalizes to the cytoplasm and aggregates in ALS/FTD. In a yeast model, FUS proteinopathy is connected to changes in the epigenome, including reductions in the levels of H3S10ph, H3K14ac, and H3K56ac. Exploiting the same model, we reveal novel connections between FUS aggregation and epigenetic dysregulation. We show that the histone-modifying enzymes Ipl1 and Rtt109-responsible for installing H3S10ph and H3K56ac-are excluded from the nucleus in the context of FUS proteinopathy. Furthermore, we found that Ipl1 colocalizes with FUS, but does not bind it directly. We identified Nop1 and Rrp5, a histone methyltransferase and rRNA biogenesis protein, respectively, as FUS binding partners involved in the growth suppression phenotype connected to FUS proteinopathy. We propose that the nuclear exclusion of Ipl1 through indirect interaction with FUS drives the dysregulation of H3S10ph as well as H3K14ac via crosstalk. We found that the knockdown of Nop1 interferes with these processes. In a parallel mechanism, Rtt109 mislocalization results in reduced levels of H3K56ac. Our results highlight the contribution of epigenetic mechanisms to ALS/FTD and identify novel targets for possible therapeutic intervention.

PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides

Short peptides have become the focus of recent research due to their variable bioactivities, good digestibility and wide existences in food-derived protein hydrolysates. However, due to the high complexity of the samples, identifying short peptides still remains a challenge. In this work, a tool, named PeposX-Exhaust, was developed for short peptide identification. Through validation with known peptides, PeposX-Exhaust identified all the submitted spectra and the accuracy rate reached 75.36%, and the adjusted accuracy rate further reached 98.55% when with top 5 candidates considered. Compared with other tools, the accuracy rate by PeposX-Exhaust was at least 70% higher than two database-search tools and 15% higher than the other two de novo-sequencing tools, respectively. For further application, the numbers of short peptides identified from soybean, walnut, collagen and bonito protein hydrolysates reached 1145, 628, 746 and 681, respectively. This fully demonstrated the superiority of the tool in short peptide identification.

A joint proteomic and genomic investigation provides insights into the mechanism of calcification in coccolithophores

Coccolithophores are globally abundant, calcifying microalgae that have profound effects on marine biogeochemical cycles, the climate, and life in the oceans. They are characterized by a cell wall of CaCO3 scales called coccoliths, which may contribute to their ecological success. The intricate morphologies of coccoliths are of interest for biomimetic materials synthesis. Despite the global impact of coccolithophore calcification, we know little about the molecular machinery underpinning coccolithophore biology. Working on the model Emiliania huxleyi, a globally distributed bloom-former, we deploy a range of proteomic strategies to identify coccolithogenesis-related proteins. These analyses are supported by a new genome, with gene models derived from long-read transcriptome sequencing, which revealed many novel proteins specific to the calcifying haptophytes. Our experiments provide insights into proteins involved in various aspects of coccolithogenesis. Our improved genome, complemented with transcriptomic and proteomic data, constitutes a new resource for investigating fundamental aspects of coccolithophore biology.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

PDZ Proteins SCRIB and DLG1 Regulate Myeloma Cell Surface CD86 Expression, Growth, and Survival

Despite advances in the treatment of multiple myeloma in the past decades, the disease remains incurable, and understanding signals and molecules that can control myeloma growth and survival are important for the development of novel therapeutic strategies. One such molecule, CD86, regulates multiple myeloma cell survival via its interaction with CD28 and signaling through its cytoplasmic tail. Although the CD86 cytoplasmic tail has been shown to be involved in drug resistance and can induce molecular changes in multiple myeloma cells, its function has been largely unexplored. Here, we show that CD86 cytoplasmic tail has a role in trafficking CD86 to the cell surface. This is due in part to a PDZ-binding motif at its C-terminus which is important for proper trafficking from the Golgi apparatus. BioID analysis revealed 10 PDZ domain-containing proteins proximal to CD86 cytoplasmic tail in myeloma cells. Among them, we found the planar cell polarity proteins, SCRIB and DLG1, are important for proper CD86 surface expression and the growth and survival of myeloma cells. These findings indicate a mechanism by which myeloma cells confer cellular survival and drug resistance and indicate a possible motif to target for therapeutic gain.

IMPLICATIONS

These findings demonstrate the importance of proper trafficking of CD86 to the cell surface in myeloma cell survival and may provide a new therapeutic target in this disease.

Validation of De Novo Peptide Sequences with Bottom-Up Tag Convolution

De novo sequencing is indispensable for the analysis of proteins from organisms with unknown genomes, novel splice variants, and antibodies. However, despite a variety of methods developed to this end, distinguishing between the correct interpretation of a mass spectrum and a number of incorrect alternatives often remains a challenge. Tag convolution is computed for a set of peptide sequence tags of a fixed length k generated from the input tandem mass spectra and can be viewed as a generalization of the well-known spectral convolution. We demonstrate its utility for validating de novo peptide sequences by using a set of those generated by the algorithm PepNovo+ from high-resolution bottom-up data sets for carbonic anhydrase 2 and the Fab region of alemtuzumab and indicate its further potential applications.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Proteomics of Multiple Sclerosis: Inherent Issues in Defining the Pathoetiology and Identifying (Early) Biomarkers

Multiple Sclerosis (MS) is a demyelinating disease of the human central nervous system having an unconfirmed pathoetiology. Although animal models are used to mimic the pathology and clinical symptoms, no single model successfully replicates the full complexity of MS from its initial clinical identification through disease progression. Most importantly, a lack of preclinical biomarkers is hampering the earliest possible diagnosis and treatment. Notably, the development of rationally targeted therapeutics enabling pre-emptive treatment to halt the disease is also delayed without such biomarkers. Using literature mining and bioinformatic analyses, this review assessed the available proteomic studies of MS patients and animal models to discern (1) whether the models effectively mimic MS; and (2) whether reasonable biomarker candidates have been identified. The implication and necessity of assessing proteoforms and the critical importance of this to identifying rational biomarkers are discussed. Moreover, the challenges of using different proteomic analytical approaches and biological samples are also addressed.

Y2O3 Nanoparticles and X-ray Radiation-Induced Effects in Melanoma Cells

The innovative strategy of using nanoparticles in radiotherapy has become an exciting topic due to the possibility of simultaneously improving local efficiency of radiation in tumors and real-time monitoring of the delivered doses. Yttrium oxide (Y₂O₃) nanoparticles (NPs) are used in material science to prepare phosphors for various applications including X-ray induced photodynamic therapy and in situ nano-dosimetry, but few available reports only addressed the effect induced in cells by combined exposure to different doses of superficial X-ray radiation and nanoparticles. Herein, we analyzed changes induced in melanoma cells by exposure to different doses of X-ray radiation and various concentrations of Y₂O₃ NPs. By evaluation of cell mitochondrial activity and production of intracellular reactive oxygen species (ROS), we estimated that 2, 4, and 6 Gy X-ray radiation doses are visibly altering the cells by inducing ROS production with increasing the dose while at 6 Gy the mitochondrial activity is also affected. Separately, high-concentrated solutions of 25, 50, and 100 µg/mL Y₂O₃ NPs were also found to affect the cells by inducing ROS production with the increase of concentration. Additionally, the colony-forming units assay evidenced a rather synergic effect of NPs and radiation. By adding the NPs to cells before irradiation, a decrease of the number of proliferating cell colonies was observed with increase of X-ray dose. DNA damage was evidenced by quantifying the γ-H2AX foci for cells treated with Y₂O₃ NPs and exposed to superficial X-ray radiation. Proteomic profile confirmed that a combined effect of 50 µg/mL Y₂O₃ NPs and 6 Gy X-ray dose induced mitochondria alterations and DNA changes in melanoma cells.

A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

A highly selective two-way purification method using liquid chromatography for isolating αS2-casein from goat milk of five different breeds

The main challenges in the purification of α_S2-casein are due to the low quantity in milk and high homology with other casein subunits, i.e., α_S1-casein, β-casein, and κ-casein. To overcome these challenges, the aim of this study was to develop a two-step purification to isolate native α_S2-casein in goat milk from five different breeds; British Alpine, Jamnapari, Saanen, Shami, and Toggenburg. The first step of the purification was executed by anion-exchange chromatography under optimal elution conditions followed by size exclusion chromatography. Tryptic peptides from in-gel digestion of purified α_S2-casein were sequenced and analyzed by LC-ESI-MS/MS. From 1.05 g of whole casein, the highest yield of α_S2-casein (6.7 mg/mL) was obtained from Jamnapari and the lowest yield (2.2 mg/mL) was from Saanen. A single band of pure α_S2-casein was observed on SDS-PAGE for all breeds. The α_S2-casein showed coverage percentage of amino acid sequence from 76.68 to 92.83%. The two-step purification process developed herein was successfully applied for isolating native α_S2-casein from goat milk with high purity, which will allow for future in vitro studies to be conducted on this protein.

DNMSO; an ontology for representing de novo sequencing results from Tandem-MS data

For the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.

GameTag: A New Sequence Tag Generation Algorithm Based on Cooperative Game Theory

Sequence tag-based peptide search is a critical technology in proteomics for the characterization of proteins from tandem mass spectrometry data. However, the main reason for hindering the full application of such an approach lies that accurately extracting sequence tags responsible for each experimental spectrum. Toward that end, GameTag, a novel cooperative game framework for sequence tag generation is proposed, which includes a tag generator and a tag discriminator to collaboratively generate sequence tags. Specifically, the tag generator works to extract as many correct tag candidates as possible and the tag discriminator serves to determine the correctness of tag candidates and reduce the total number of output tags simultaneously. Through the dynamic two-player game, the number of extracted tags is decreased while the number of correct tags gets boosted. The performance of the proposed method is also investigated under various hyperparameter and structure settings. Extensive experiments on a wide variety of data sets from different species demonstrate that GameTag outperforms previous state-of-the-art methods, InsPecT, PepNovo+, DirecTag, and the existing tag-extraction method in Open-pFind, increasing by at least 10% the number of spectra extracted more than one correct tag.

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics

Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

BACKGROUND

Cancer that arises from epithelial cells of the esophagus is called esophagus squamous cell carcinoma (ESCC) and is mostly observed in developing nations. Evaluation of cancer genomes and its regulation into proteins plays a predominant role in understanding the cancer progressions. Mass-spectrometry-based proteomics is a consequential tool to estimate proteomic variation and posttranslational modifications (PTMs) from standard protein databases. Post-translational modifications play a crucial role in protein folding and PTMs can be accounted for as a biological signal to interpret the structural changes and transition order of proteins. Functional validation of cancer-related mutations can explain the effects of mutations on genes and the identification of Oncogenes and tumor suppressor genes. Therefore, we present a study on protein variations to interpret the structural changes and transition order of proteins in ESCC carcinogenesis.

METHODOLOGY

We are using a bottom-up proteomics approach with Galaxy-P framework and RNA sequence data analysis to generate the sample-specific databases containing details of RNA splicing and variant peptides. Once the database generated with information on variable modification, only the curated PTMs at specific positions are considered to perform spectral matching. Proteogenomics mapping was performed to identify protein variations in ESCC.

RESULTS

RNA-sequence proteogenomics with G-PTM (Global Post-Translational Modification) searching strategy has revealed proteomic events including several peptides that contain single amino acid variations, novel splice junction peptides and posttranslationally modified peptides. Proteogenomic mapping exhibited the splice junction peptides mapped predominantly for Malic enzyme exon type (ME-3) and MCM7 protein-coding genes that promote cancer progression, found to be exhibited in ESCC samples. Approximately 25 ± types of PTM modifications were recorded, and Protein Phosphorylation was largely noted.

CONCLUSION

ESCC cancer prognosis at the molecular level enables a better understanding of cancer carcinogenesis and protein modifications can be used as potential biomarkers.

Optimization of protein isolation by proteomic qualification from Cutaneotrichosporon oleaginosus

In the last decades, microbial oils have been extensively investigated as a renewable platform for biofuel and oleochemical production. Offering a potent alternative to plant-based oils, oleaginous microorganisms have been the target of ongoing metabolic engineering aimed at increasing growth and lipid yields, in addition to specialty fatty acids. Discovery proteomics is an attractive tool for elucidating lipogenesis and identifying metabolic bottlenecks, feedback regulation, and competing biosynthetic pathways. One prominent microbial oil producer is Cutaneotrichosporon oleaginosus, due to its broad feedstock catabolism and high lipid yield. However, this yeast has a recalcitrant cell wall and high cell lipid content, which complicates efficient and unbiased protein extraction for downstream proteomic analysis. Optimization efforts of protein sample preparation from C. oleaginosus in the present study encompasses the comparison of 8 lysis methods, 13 extraction buffers, and 17 purification methods with respect to protein abundance, proteome coverage, applicability, and physiochemical properties (pI, MW, hydrophobicity in addition to COG, and GO analysis). The optimized protocol presented in this work entails a one-step extraction method utilizing an optimal lysis method (liquid homogenization), which is augmented with a superior extraction buffer (50 mM Tris, 8/2 M Urea/Thiourea, and 1% C7BzO), followed by either of 2 advantageous purification methods (hexane/ethanol or TCA/acetone), depending on subsequent applications and target studies. This work presents a significant step forward towards implementation of efficient C. oleaginosus proteome mining for the identification of potential targets for genetic optimization of this yeast to improve lipogenesis and production of specialty lipids. Graphical abstract.

SWPepNovo: An Efficient De Novo Peptide Sequencing Tool for Large-scale MS/MS Spectra Analysis

Tandem mass spectrometry (MS/MS)-based de novo peptide sequencing is a powerful method for high-throughput protein analysis. However, the explosively increasing size of MS/MS spectra dataset inevitably and exponentially raises the computational demand of existing de novo peptide sequencing methods, which is an issue urgently to be solved in computational biology. This paper introduces an efficient tool based on SW26010 many-core processor, namely SWPepNovo, to process the large-scale peptide MS/MS spectra using a parallel peptide spectrum matches (PSMs) algorithm. Our design employs a two-level parallelization mechanism: (1) the task-level parallelism between MPEs using MPI based on a data transformation method and a dynamic feedback task scheduling algorithm, (2) the thread-level parallelism across CPEs using asynchronous task transfer and multithreading. Moreover, three optimization strategies, including vectorization, double buffering and memory access optimizations, have been employed to overcome both the compute-bound and the memory-bound bottlenecks in the parallel PSMs algorithm. The results of experiments conducted on multiple spectra datasets demonstrate the performance of SWPepNovo against three state-of-the-art tools for peptide sequencing, including PepNovo+, PEAKS and DeepNovo-DIA. The SWPepNovo also shows high scalability in experiments on extremely large datasets sized up to 11.22 GB. The software and the parameter settings are available at https://github.com/ChuangLi99/SWPepNovo.

Optimized Cross-Linking Mass Spectrometry for in Situ Interaction Proteomics

Recent development of mass spectrometer cleavable protein cross-linkers and algorithms for their spectral identification now permits large-scale cross-linking mass spectrometry (XL-MS). Here, we optimized the use of cleavable disuccinimidyl sulfoxide (DSSO) cross-linker for labeling native protein complexes in live human cells. We applied a generalized linear mixture model to calibrate cross-link peptide-spectra matching (CSM) scores to control the sensitivity and specificity of large-scale XL-MS. Using specific CSM score thresholds to control the false discovery rate, we found that higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD) can both be effective for large-scale XL-MS protein interaction mapping. We found that the coverage of protein-protein interaction maps is significantly improved through the use of multiple proteases. In addition, the use of focused sample-specific search databases can be used to improve the specificity of cross-linked peptide spectral matching. Application of this approach to human chromatin labeled in live cells recapitulated known and revealed new protein interactions of nucleosomes and other chromatin-associated complexes in situ. This optimized approach for mapping native protein interactions should be useful for a wide range of biological problems.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions

Peptide⁻protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer's disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide⁻protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide⁻protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide⁻protein interactions.

Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.

RNA editing derived epitopes function as cancer antigens to elicit immune responses

In addition to genomic mutations, RNA editing is another major mechanism creating sequence variations in proteins by introducing nucleotide changes in mRNA sequences. Deregulated RNA editing contributes to different types of human diseases, including cancers. Here we report that peptides generated as a consequence of RNA editing are indeed naturally presented by human leukocyte antigen (HLA) molecules. We provide evidence that effector CD8⁺ T cells specific for edited peptides derived from cyclin I are present in human tumours and attack tumour cells that are presenting these epitopes. We show that subpopulations of cancer patients have increased peptide levels and that levels of edited RNA correlate with peptide copy numbers. These findings demonstrate that RNA editing extends the classes of HLA presented self-antigens and that these antigens can be recognised by the immune system.

RNA editing is a biological process that creates sequence variation. Here the authors show that peptides generated as a consequence of RNA editing are naturally presented by human leukocyte antigen (HLA) and serve as antigens to elicit anti-tumour immune responses.

Structure of the human PKD1-PKD2 complex

Mutations in two genes, PKD1 and PKD2, account for most cases of autosomal dominant polycystic kidney disease, one of the most common monogenetic disorders. Here we report the 3.6-angstrom cryo–electron microscopy structure of truncated human PKD1-PKD2 complex assembled in a 1:3 ratio. PKD1 contains a voltage-gated ion channel (VGIC) fold that interacts with PKD2 to form the domain-swapped, yet noncanonical, transient receptor potential (TRP) channel architecture. The S6 helix in PKD1 is broken in the middle, with the extracellular half, S6a, resembling pore helix 1 in a typical TRP channel. Three positively charged, cavity-facing residues on S6b may block cation permeation. In addition to the VGIC, a five–transmembrane helix domain and a cytosolic PLAT domain were resolved in PKD1. The PKD1-PKD2 complex structure establishes a framework for dissecting the function and disease mechanisms of the PKD proteins.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

Tandem mass spectral libraries of peptides and their roles in proteomics research

Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Comprehensive de Novo Peptide Sequencing from MS/MS Pairs Generated through Complementary Collision Induced Dissociation and 351 nm Ultraviolet Photodissociation

We describe a strategy for de novo peptide sequencing based on matched pairs of tandem mass spectra (MS/MS) obtained by collision induced dissociation (CID) and 351 nm ultraviolet photodissociation (UVPD). Each precursor ion is isolated twice with the mass spectrometer switching between CID and UVPD activation modes to obtain a complementary MS/MS pair. To interpret these paired spectra, we modified the UVnovo de novo sequencing software to automatically learn from and interpret fragmentation spectra, provided a representative set of training data. This machine learning procedure, using random forests, synthesizes information from one or multiple complementary spectra, such as the CID/UVPD pairs, into peptide fragmentation site predictions. In doing so, the burden of fragmentation model definition shifts from programmer to machine and opens up the model parameter space for inclusion of nonobvious features and interactions. This spectral synthesis also serves to transform distinct types of spectra into a common representation for subsequent activation-independent processing steps. Then, independent from precursor activation constraints, UVnovo's de novo sequencing procedure generates and scores sequence candidates for each precursor. We demonstrate the combined experimental and computational approach for de novo sequencing using whole cell E. coli lysate. In benchmarks on the CID/UVPD data, UVnovo assigned correct full-length sequences to 83% of the spectral pairs of doubly charged ions with high-confidence database identifications. Considering only top-ranked de novo predictions, 70% of the pairs were deciphered correctly. This de novo sequencing performance exceeds that of PEAKS and PepNovo on the CID spectra and that of UVnovo on CID or UVPD spectra alone. As presented here, the methods for paired CID/UVPD spectral acquisition and interpretation constitute a powerful workflow for high-throughput and accurate de novo peptide sequencing.

Dereplication of peptidic natural products through database search of mass spectra

Peptidic Natural Products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. While recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers utilize dereplication strategies that identify known PNPs and lead to the discovery of new ones even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enabled high-throughput PNP identification and that is compatible with large-scale mass spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts.

Peptide de novo sequencing of mixture tandem mass spectra

The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co‐isolation and thus prone to false identifications. The deconvolution approach matched complementary b‐, y‐ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co‐isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20–35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.

A high-resolution LC-MS-based secondary metabolite fingerprint database of marine bacteria

Marine bacteria are the most widely distributed organisms in the ocean environment and produce a wide variety of secondary metabolites. However, traditional screening for bioactive natural compounds is greatly hindered by the lack of a systematic way of cataloguing the chemical profiles of bacterial strains found in nature. Here we present a chemical fingerprint database of marine bacteria based on their secondary metabolite profiles, acquired by high-resolution LC-MS. Till now, 1,430 bacterial strains spanning 168 known species collected from different marine environments were cultured and profiled. Using this database, we demonstrated that secondary metabolite profile similarity is approximately, but not always, correlated with taxonomical similarity. We also validated the ability of this database to find species-specific metabolites, as well as to discover known bioactive compounds from previously unknown sources. An online interface to this database, as well as the accompanying software, is provided freely for the community to use.

Combinatorial approach for large-scale identification of linked peptides from tandem mass spectrometry spectra

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein-protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides.

Neutron-encoded signatures enable product ion annotation from tandem mass spectra

We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, (13)C6(15)N2 and (2)H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ~50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo(+) resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.

UniNovo: a universal tool for de novo peptide sequencing

Motivation: Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function.

Results: The performance of UniNovo is compared with PepNovo+, PEAKS and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable with others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD and HCD/ETD spectra of trypsin, LysC or AspN digested peptides).

Availability: UniNovo is implemented in JAVA and tested on Windows, Ubuntu and OS X machines. UniNovo is available at http://proteomics.ucsd.edu/Software/UniNovo.html along with the manual.

Contact:kwj@ucsd.edu or ppevzner@ucsd.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Sequencing-grade de novo analysis of MS/MS triplets (CID/HCD/ETD) from overlapping peptides

Full-length de novo sequencing of unknown proteins remains a challenging open problem. Traditional methods that sequence spectra individually are limited by short peptide length, incomplete peptide fragmentation, and ambiguous de novo interpretations. We address these issues by determining consensus sequences for assembled tandem mass (MS/MS) spectra from overlapping peptides (e.g., by using multiple enzymatic digests). We have combined electron-transfer dissociation (ETD) with collision-induced dissociation (CID) and higher-energy collision-induced dissociation (HCD) fragmentation methods to boost interpretation of long, highly charged peptides and take advantage of corroborating b/y/c/z ions in CID/HCD/ETD. Using these strategies, we show that triplet CID/HCD/ETD MS/MS spectra from overlapping peptides yield de novo sequences of average length 70 AA and as long as 200 AA at up to 99% sequencing accuracy.

How many signal peptides are there in bacteria?

Over the last 5 years proteogenomics (using mass spectroscopy to identify proteins predicted from genomic sequences) has emerged as a promising approach to the high-throughput identification of protein N-termini, which remains a problem in genome annotation. Comparison of the experimentally determined N-termini with those predicted by sequence analysis tools allows identification of the signal peptides and therefore conclusions on the cytoplasmic or extracytoplasmic (periplasmic or extracellular) localization of the respective proteins. We present here the results of a proteogenomic study of the signal peptides in Escherichia coli K-12 and compare its results with the available experimental data and predictions by such software tools as SignalP and Phobius. A single proteogenomics experiment recovered more than a third of all signal peptides that had been experimentally determined during the past three decades and confirmed at least 31 additional signal peptides, mostly in the known exported proteins, which had been previously predicted but not validated. The filtering of putative signal peptides for the peptide length and the presence of an eight-residue hydrophobic patch and a typical signal peptidase cleavage site proved sufficient to eliminate the false-positive hits. Surprisingly, the results of this proteogenomics study, as well as a re-analysis of the E. coli genome with the latest version of SignalP program, show that the fraction of proteins containing signal peptides is only about 10%, or half of previous estimates.

Unexpected diversity of signal peptides in prokaryotes

Signal peptides are a cornerstone mechanism for cellular protein localization, yet until now experimental determination of signal peptides has come from only a narrow taxonomic sampling. As a result, the dominant view is that Sec-cleaved signal peptides in prokaryotes are defined by a canonical AxA motif. Although other residues are permitted in the motif, alanine is by far the most common. Here we broadly examine proteomics data to reveal the signal peptide sequences for 32 bacterial and archaeal organisms from nine phyla and demonstrate that this alanine preference is not universal. Discoveries include fundamentally distinct signal peptide motifs from Alphaproteobacteria, Spirochaetes, Thermotogae and Euryarchaeota. In these novel motifs, alanine is no longer the dominant residue but has been replaced in a different way for each taxon. Surprisingly, divergent motifs correlate with a proteome-wide reduction in alanine. Computational analyses of ~1,500 genomes reveal numerous major evolutionary clades which have replaced the canonical signal peptide sequence with novel motifs.

This article replaces a widely held general model with a more detailed model describing phylogenetically correlated variation in motifs for Sec secretion.

Key issues in the acquisition and analysis of qualitative and quantitative mass spectrometry data for peptide-centric proteomic experiments

Proteomic technologies have matured to a level enabling accurate and reproducible quantitation of peptides and proteins from complex biological matrices. Analysis of samples as diverse as assembled protein complexes, whole cell lysates or sub-cellular proteomes from cell cultures, and direct analysis of animal and human tissues and fluids demonstrate the incredible versatility of the fundamental nature of the technique that forms the basis of most proteomic applications today (mass spectrometry). Determining the mass of biomolecules and their fragments or related products with high accuracy can convey a highly specific assay for detection and identification. Importantly, ion currents representative of these specifically identified analytes can be accurately quantified with the correct application of smart isobaric tagging chemistries, heavy and light isotopically derivatised samples or standards, or by careful application of workflows to compare unlabelled samples in so-called 'label-free' and targeted selected reaction monitoring experiments. In terms of exploring biology, a myriad of protein changes and modifications are being increasingly probed and quantified, including diverse chemical changes from relatively decisive modifications such as protein splicing and truncation, to more transient dynamic modifications such as phosphorylation, acetylation and ubiquitination. Proteomic workflows can be complex beasts and several key considerations to ensure effective applications have been outlined in the recent literature. The past year has witnessed the publication of several excellent reviews that thoroughly describe the fundamental principles underlying the state of the art. This review further elaborates on specific critical issues introduced by these publications and raises other important unaddressed considerations and new developments that directly impact on the effectiveness of proteomic technologies, in particular for, but not necessarily exclusive to peptide-centric experiments. These factors are discussed both in terms of qualitative analyses, including dynamic range and sampling issues, and developments to improve the translation of peptide fragmentation data into peptide and protein identities, as well as quantitative analyses, including data normalisation and the utility of ontology or functional annotation, the effects of modified peptides, and considered experimental design to facilitate the use of robust statistical methods.

Peptide reranking with protein-peptide correspondence and precursor peak intensity information

Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification. Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information directly from database search scores or by training machine learning models. Information in the protein database and MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a score regularization method SRPI. The source codes of PPMRanker, PPIRanker, and MIRanker, and all supplementary documents are available at our website: http://bioinformatics.ust.hk/pepreranking/. Alternatively, these documents can also be downloaded from: http://sourceforge.net/projects/pepreranking/.

Cycloquest: identification of cyclopeptides via database search of their mass spectra against genome databases

Hundreds of ribosomally synthesized cyclopeptides have been isolated from all domains of life, the vast majority having been reported in the last 15 years. Studies of cyclic peptides have highlighted their exceptional potential both as stable drug scaffolds and as biomedicines in their own right. Despite this, computational techniques for cyclopeptide identification are still in their infancy, with many such peptides remaining uncharacterized. Tandem mass spectrometry has occupied a niche role in cyclopeptide identification, taking over from traditional techniques such as nuclear magnetic resonance spectroscopy (NMR). MS/MS studies require only picogram quantities of peptide (compared to milligrams for NMR studies) and are applicable to complex samples, abolishing the requirement for time-consuming chromatographic purification. While database search tools such as Sequest and Mascot have become standard tools for the MS/MS identification of linear peptides, they are not applicable to cyclopeptides, due to the parent mass shift resulting from cyclization and different fragmentation patterns of cyclic peptides. In this paper, we describe the development of a novel database search methodology to aid in the identification of cyclopeptides by mass spectrometry and evaluate its utility in identifying two peptide rings from Helianthus annuus, a bacterial cannibalism factor from Bacillus subtilis, and a θ-defensin from Rhesus macaque.