Pursuing Impactful Quantitative Proteomics Using QC-Channels in Every Spectrum and Trend-Design in Experiment.

False changes discovered by quantitative proteomics reduce the trust of biologists in proteomics and limit the applications of proteomics to unlock biological mechanisms, which suppresses the application of proteomics techniques in the pharmaceutical industry more than it does in academic research. To remove false changes that arise during LC-MS/MS data acquisition, we evaluated the contributions of peptide abundance and number of unique peptides on reproducibility. Lower abundance and only one unique peptide have a higher risk of generating a higher coefficient of variation (CV), resulting in less accurate quantification. However, the abundance of peptides in samples is not adjustable and discarding proteins quantified by only one unique peptide is not a choice either. Indeed, a large percentage of proteins are accurately quantified by only one unique peptide. Therefore, to improve the calculations of the CV, we leverage a new function in PEAKS called QC-channels which enables technical replicates of each spectrum to be evaluated prior to calculation of the CV. While the QC-channels function in PEAKS significantly reduced the false quantification, random false changes still exist due to known or unknown reasons. To address this challenge, we present the idea of Trend-design to track trend changes rather than changes from two points to remove false quantifications and reveal consequential changes responding to a treatment or condition. The idea was confirmed by molecules with different affinity and dose in the current study. The combination of QC-channels and Trend-design enables a more impactful quantitative proteomics to allow unlocking biological mechanisms using proteomics.

Efficacy and safety of platelet-rich plasma in the treatment of venous ulcers: A systematic review and meta-analysis of randomized controlled trials.

Considering the substantial impact of venous ulcers on quality of life and healthcare systems, this study evaluated the efficacy and safety of platelet-rich plasma (PRP) in comparison to conventional therapy. A systematic review of four databases identified 16 randomized clinical trials, including 20 study groups. PRP significantly enhanced complete ulcer healing, exhibiting an odds ratio (OR) of 5.06 (95% confidence interval [CI]: 2.35-10.89), and increased the percentage of healed ulcer area by a mean difference of 47% (95% CI: 32%-62%). Additionally, PRP shortened the time required for complete healing by an average of 3.25 months (95% CI: -4.06 to -2.43). Although pain reduction was similar in both groups, PRP considerably decreased ulcer recurrence rates (OR = 0.16, 95% CI: 0.05-0.50) without increasing the risks of infection or irritative dermatitis. These results suggest PRP as a viable, safe alternative for venous ulcer treatment, providing significant improvements in healing outcomes.

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study.

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels.

Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics.

Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Deep neural network for detecting arbitrary precision peptide features through attention based segmentation.

A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.

ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition.

Accurate and sensitive identification of peptides from MS/MS spectra is a very challenging problem in computational shotgun proteomics. To tackle this problem, spectral library search has been one of the competitive solutions. However, most existing library search tools were developed on the basis of one peptide per spectrum, which prevents them from working properly on chimeric spectra where two or more peptides are co-fragmented. In this work, we present a new library search tool called ChimST, which is particularly capable of reliably identifying multiple peptides from a chimeric spectrum. It starts with associating each query MS/MS spectrum with MS precursor features. For each precursor feature, there is a list of peptide candidates extracted from an input spectral library. Then, it takes one peptide candidate from each associated feature and scores how well they could collectively interpret the query spectrum. The highest-scoring set of peptide candidates are finally reported as the identification of the query spectrum. Our experimental tests show that ChimST could significantly outperform the three state-of-the-art library search tools, SpectraST, reSpect, and MSPLIT, in terms of the numbers of both peptide-spectrum matches and unique peptides, especially when the acquisition isolation window is broad.

DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map.

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.

Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry.

We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.

DISC: DISulfide linkage Characterization from tandem mass spectra.

MOTIVATION: Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results. RESULTS: In this research, we present a new computational approach, DISC (DISulfide bond Characterization), for matching an input MS/MS spectrum against the putative disulfide linkage structures hypothetically constructed from the protein database. More specifically, we consider different ion types including a variety of internal ions that frequently observed in mass spectra resulted from disulfide linked peptides, and introduce an effective two-layer scoring scheme to evaluate the significance of the matching between spectrum and structure, based on which we have also developed a useful target-decoy strategy for providing quality control and reporting false discovery rate in the final results. Systematic experiments conducted on both low-complexity and high-complexity datasets demonstrated the efficiency of our proposed method for the identification of disulfide bonds from MS/MS spectra, and showed its potential in characterizing disulfide bonds at the proteome scale instead of just a single protein. AVAILABILITY AND IMPLEMENTATION: Software is available for downloading at http://www.csd.uwo.ca/yliu766/. CONTACT: yliu766@uwo.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

De novo peptide sequencing by deep learning.

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.

Complete De Novo Assembly of Monoclonal Antibody Sequences.

De novo protein sequencing is one of the key problems in mass spectrometry-based proteomics, especially for novel proteins such as monoclonal antibodies for which genome information is often limited or not available. However, due to limitations in peptides fragmentation and coverage, as well as ambiguities in spectra interpretation, complete de novo assembly of unknown protein sequences still remains challenging. To address this problem, we propose an integrated system, ALPS, which for the first time can automatically assemble full-length monoclonal antibody sequences. Our system integrates de novo sequencing peptides, their quality scores and error-correction information from databases into a weighted de Bruijn graph to assemble protein sequences. We evaluated ALPS performance on two antibody data sets, each including a heavy chain and a light chain. The results show that ALPS was able to assemble three complete monoclonal antibody sequences of length 216-441 AA, at 100% coverage, and 96.64-100% accuracy.

GlycoMaster DB: software to assist the automated identification of N-linked glycopeptides by tandem mass spectrometry.

Glycosylation is one of the most commonly observed post-translational modifications (PTMs) in eukaryotes. It is believed that more than 50% eukaryotic proteins are glycosylated. To reveal the biological functions of protein-linked glycans involved in numerous biological processes, the high-throughput identification of both glycoproteins and the attached glycan structures becomes fundamentally important. Tandem mass spectrometry (MS/MS) is an effective method for glycoproteomic analysis because of its high sensitivity and selectivity. Two experimental approaches exist to obtain MS/MS spectral data of glycopeptides. One consists of isolating glycans from glycopeptides and generating MS/MS spectra of the glycans and peptides separately. The other approach produces spectra directly from intact glycopeptides. The latter approach has the advantage of retaining the glycosylation site information. However, the spectral data cannot be readily analyzed because of the lack of software specifically designed for the identification of intact glycopeptides. To address this need, we developed a novel software tool, GlycoMaster DB, to assist the automated and high-throughput identification of intact N-linked glycopeptides from MS/MS spectra. The software simultaneously searches a protein sequence database and a glycan structure database to find the best pair of peptide and glycan for each input spectrum. GlycoMaster DB can analyze mass spectral data produced with HCD/ETD mixed fragmentation, where HCD spectra are used to identify glycans and ETD spectra are used to determine peptide sequences. When only HCD spectra are available, GlycoMaster DB can still help to identify the glycans, and a list of possible peptide sequences are reported according to the accurate precursor mass and the N-linked glycopeptide sequon. GlycoMaster DB is freely accessible at http://www-novo.cs.uwaterloo.ca:8080/GlycoMasterDB .

Through the eye of an electrospray needle: mass spectrometric identification of the major peptides and proteins in the milk of the one-humped camel (Camelus dromedarius).

The milk of the one-humped camel (Camelus dromedarius) reportedly offers medicinal benefits, perhaps because of its unique bioactive components. Milk proteins were determined by (1) two-dimensional gel electrophoresis and peptide mass mapping and (2) liquid chromatography-tandem mass spectrometry (LC-MS/MS) following one-dimensional polyacrylamide gel electrophoresis. Over 200 proteins were identified: some known camel proteins including heavy-chain immunoglobulins and others exhibiting regions of exact homology with proteins from other species. Indigenous peptides were also identified following isolation and concentration by two strategies: (1) gel-eluted liquid fraction entrapment electrophoresis and (2) small-scale electrophoretic separation. Extracts were analyzed by LC-MS/MS and peptides identified by matching strategies, by de novo sequencing and by applying a sequence tag tool requiring similarity to the proposed sequence, but not an exact match. A plethora of protein cleavage products including some novel peptides were characterized. These studies demonstrate that camel milk is a rich source of peptides, some of which may serve as nutraceuticals.

Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report.

The workshop "Bioinformatics for Biotechnology Applications (HavanaBioinfo 2012)", held December 8-11, 2012 in Havana, aimed at exploring new bioinformatics tools and approaches for large-scale proteomics, genomics and chemoinformatics. Major conclusions of the workshop include the following: (i) development of new applications and bioinformatics tools for proteomic repository analysis is crucial; current proteomic repositories contain enough data (spectra/identifications) that can be used to increase the annotations in protein databases and to generate new tools for protein identification; (ii) spectral libraries, de novo sequencing and database search tools should be combined to increase the number of protein identifications; (iii) protein probabilities and FDR are not yet sufficiently mature; (iv) computational proteomics software needs to become more intuitive; and at the same time appropriate education and training should be provided to help in the efficient exchange of knowledge between mass spectrometrists and experimental biologists and bioinformaticians in order to increase their bioinformatics background, especially statistics knowledge.

PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification.

Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.

PeaksPTM: Mass spectrometry-based identification of peptides with unspecified modifications.

Tandem mass spectrometry (MS/MS) has been routinely used to identify peptides from a protein sequence database. To identify post-translationally modified peptides, most existing software requires the specification of a few possible modifications. However, such knowledge of possible modifications is not always available. In this paper, we describe a new algorithm for identifying modified peptides without requiring the user to specify the possible modifications; instead, all modifications from the Unimod database are considered. Meanwhile, several new techniques are employed to avoid the exponential growth of the search space, as well as to control the false discoveries due to this unrestricted search approach. Finally, a software tool, PeaksPTM, has been developed and already achieved a stronger performance than competitive tools for unrestricted identification of post-translational modifications.

Better score function for peptide identification with ETD MS/MS spectra.

BACKGROUND: Tandem mass spectrometry (MS/MS) has become the primary way for protein identification in proteomics. A good score function for measuring the match quality between a peptide and an MS/MS spectrum is instrumental for the protein identification. Traditionally the to-be-measured peptides are fragmented with the collision induced dissociation (CID) method. More recently, the electron transfer dissociation (ETD) method was introduced and has proven to produce better fragment ion ladders for larger and more basic peptides. However, the existing software programs that analyze ETD MS/MS data are not as advanced as they are for CID. RESULTS: To take full advantage of ETD data, in this paper we develop a new score function to evaluate the match between a peptide and an ETD MS/MS spectrum. Experiments on real data demonstrated that this newly developed score function significantly improved the de novo sequencing accuracy of the PEAKS software on ETD data. CONCLUSION: A new and better score function for ETD MS/MS peptide identification was developed. The method used to develop our ETD score function can be easily reused to train new score functions for other types of MS/MS data.

Complexities and algorithms for glycan sequencing using tandem mass spectrometry.

Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan sequencing, which determines the primary structure of a glycan using tandem mass spectrometry (MS/MS), remains one of the most important tasks in proteomics. Analogous to peptide de novo sequencing, glycan de novo sequencing determines the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.