Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 2009;27:190-198. [PMID: 19169245 DOI: 10.1038/nbt.1524]

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

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

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Absolute quantitation of human wild-type DNAI1 protein in lung tissue using a nanoLC-PRM-MS-based targeted proteomics approach coupled with immunoprecipitation

BACKGROUND

Dynein axonemal intermediate chain 1 protein (DNAI1) plays an essential role in cilia structure and function, while its mutations lead to primary ciliary dyskinesia (PCD). Accurate quantitation of DNAI1 in lung tissue is crucial for comprehensive understanding of its involvement in PCD, as well as for developing the potential PCD therapies. However, the current protein quantitation method is not sensitive enough to detect the endogenous level of DNAI1 in complex biological matrix such as lung tissue.

METHODS

In this study, a quantitative method combining immunoprecipitation with nanoLC-MS/MS was developed to measure the expression level of human wild-type (WT) DNAI1 protein in lung tissue. To our understanding, it is the first immunoprecipitation (IP)-MS based method for absolute quantitation of DNAI1 protein in lung tissue. The DNAI1 quantitation was achieved through constructing a standard curve with recombinant human WT DNAI1 protein spiked into lung tissue matrix.

RESULTS

This method was qualified with high sensitivity and accuracy. The lower limit of quantitation of human DNAI1 was 4 pg/mg tissue. This assay was successfully applied to determine the endogenous level of WT DNAI1 in human lung tissue.

CONCLUSIONS

The results clearly demonstrate that the developed assay can accurately quantitate low-abundance WT DNAI1 protein in human lung tissue with high sensitivity, indicating its high potential use in the drug development for DNAI1 mutation-caused PCD therapy.

Integrated untargeted and targeted proteomics to unveil plasma prognostic markers for patients with acute paraquat poisoning: A pilot study

Paraquat (PQ) is a widely used but strongly toxic herbicide, which can induce multiple organ failure. The overall survival rate of the poisoned patients was only 54.4% due to lack of specific antidotes. Besides, the definite pathogenic mechanism of PQ is still not fully understood. In this pilot study, untargeted and targeted proteomics were integrated to explore the expression characteristics of plasma protein in PQ poisoned patients, and identify the differentially expressed proteins between survivors and non-survivors. A total of 494 plasma proteins were detected, and of which 47 were upregulated and 44 were downregulated in PQ poisoned patients compared to healthy controls. Among them, five differential plasma proteins (S100A9, S100A8, MB, ACTB and RAB11FIP3) were further validated by multiple reaction monitoring (MRM)-based targeted proteomic approach, and three of them (S100A9, S100A8 and ACTB) were confirmed to be correlated with PQ poisoning. Meanwhile, 84 dysregulated plasma proteins were identified in non-survivors compared with survivors. Moreover, targeted proteomic and ROC analysis suggested that ACTB had a good performance in predicting the prognosis of PQ poisoned patients. These findings highlighted the value of label-free and mass spectrometry-based proteomics in screening prognostic biomarkers of PQ poisoning and studying the mechanism of PQ toxicity.

Development of an ID-LC-MS/MS method using targeted proteomics for quantifying cardiac troponin I in human serum

BACKGROUND

Cardiac troponin is a complex protein consisting of the three subunits I, T and C located in heart muscle cells. When the heart muscle is damaged, it is released into the blood and can be detected. Cardiac troponin I (cTnI) is considered the most reliable and widely accepted test for detecting and confirming acute myocardial infarction. However, there is no current standardization between the commercial assays for cTnI quantification. Our work aims to create a measurement procedure that is traceable to the International System of Units for accurately measuring cardiac cTnI levels in serum samples from patients.

METHODS

The workflow begins with immobilizing anti-cTnI antibodies onto magnetic nanoparticles to form complexes. These complexes are used to isolate cTnI from serum. Next, trypsin is used to enzymatically digest the isolated cTnI. Finally, the measurement of multiple cTnI peptides is done simultaneously using isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS).

RESULTS

The maximum antibody immobilization was achieved by combining 1 mg of nanoparticles with 100 μg of antibody, resulting in an average of 59.2 ± 5.7 μg/mg of immobilized antibody. Subsequently, the anti-cTnI-magnetic nanoparticle complex was utilized to develop and validate a method for quantifying cTnI in human serum using ID-LC-MS/MS and a protein calibration approach. The analytical method was assessed regarding linearity and recovery. The developed method enables the quantification of cTnI from 0.7 to 24 μg/L (R > 0.996). The limit of quantification was 1.8 μg/L and the limit of detection was 0.6 μg/L. Intermediate precision was ≤ 9.6% and repeatability was 2.0-8.7% for all quality control materials. The accuracy of the analyzed quality control materials was between 90 and 110%. Total measurement uncertainties for target value assignment (n = 6) were found to be ≤ 12.5% for all levels.

CONCLUSIONS

The analytical method demonstrated high analytical performance in accurately quantifying cardiac troponin I levels in human serum. The proposed analytical method has the potential to facilitate the harmonization of cTnI results between clinical laboratories, assign target values to secondary certified reference materials and support reliable measurement of cTnI.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Mass spectrometry coupled to liquid chromatography is one of the most powerful technologies for proteome quantification in biomedical samples. In peptide-centric workflows, protein mixtures are enzymatically digested to peptides prior their analysis. However, proteome-wide quantification studies rarely identify all potential peptides for any given protein, and targeted proteomics experiments focus on a set of peptides for the proteins of interest. Consequently, proteomics relies on the use of a limited subset of all possible peptides as proxies for protein quantitation. In this work, we evaluated the stability of the human proteotypic peptides during 21 days and trained a deep learning model to predict peptide stability directly from tryptic sequences, which together constitute a resource of broad interest to prioritize and select peptides in proteome quantification experiments.

Safe food through better labelling; a robust method for the rapid determination of caprine and bovine milk allergens

Accidental milk cross-contamination is one of the most common causes for costly food recalls. Yet, quantifying trace-levels of allergen is time-consuming and current methods are not adapted for routine analyses making quality control for trace-level allergen content impractical. This perpetuates voluntary "may-contain" statements that are unhelpful for people suffering from food allergies. Here, we developed a rapid LC-MS method enabling milk allergen quantification by comparing all tryptic-peptides of major milk allergens. The bovine-specific αS-2 casein peptide and allergen-epitope NAVPITPTLNR provided excellent performance in sensitivity (LOD 1 mg.kg^-1; LOQ 2 mg.kg^-1) across various dairy products, good recovery rates in baked croissants (77% with a 10% inter-day RSD) and a linear range of 2-2,000 mg.kg^-1. The method can be used for routine determination of trace-contamination with bovine milk allergen and the adulteration of high-value caprine dairy products with lower-value bovine milk products, protecting consumer trust and the growing population suffering from food allergies.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Serum proteomics analysis of biomarkers for evaluating clinical response to MTX/IGU therapy in early rheumatoid arthritis

Methotrexate (MTX) and iguratimod (IGU) are conventional synthetic disease modifying antirheumatic drugs widely used in the treatment of Rheumatoid arthritis (RA) in China. Although MTX combined with IGU can significantly inhibit the progression of RA, some patients do not respond to the treatment. The purpose of this study is to explore the difference of serum protein expression between RA patients with good and poor response to the combined therapy by label-free quantitative proteomic approach. From the proteomics data, a total of 782 proteins in the serum of RA patients were detected, and of which 9 were upregulated and 18 were downregulated in the good response group compared to poor response group. Among them, four significantly differentially expressed proteins (RELN, LDHA, MRC1 and TKT) were further validated by multiple reaction monitoring (MRM)-based quantification approach, and three of them (RELN, LDHA and MRC1) were confirmed to be correlated with the response to MTX/IGU therapy. Logistic regression and ROC analysis indicated that the combination of RELN, LDHA and MRC1 had good performance in evaluating the response. This result proved the different serum proteins signature fingerprint between response group and non-response group; and highlighted the potential of the label-free and mass spectrometry-based quantitative proteomic approach in screening biomarkers for evaluating clinical response to MTX/IGU therapy in RA.

Identification of transient receptor potential melastatin 3 proteotypic peptides employing an efficient membrane protein extraction method for natural killer cells

Introduction: Mutations and misfolding of membrane proteins are associated with various disorders, hence they make suitable targets in proteomic studies. However, extraction of membrane proteins is challenging due to their low abundance, stability, and susceptibility to protease degradation. Given the limitations in existing protocols for membrane protein extraction, the aim of this investigation was to develop a protocol for a high yield of membrane proteins for isolated Natural Killer (NK) cells. This will facilitate genetic analysis of membrane proteins known as transient receptor potential melastatin 3 (TRPM3) ion channels in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) research.

Methods: Two protocols, internally identified as Protocol 1 and 2, were adapted and optimized for high yield protein extraction. Protocol 1 utilized ultrasonic and salt precipitation, while Protocol 2 implemented a detergent and chloroform/methanol approach. Protein concentrations were determined by the Pierce Bicinchoninic Acid (BCA) and the Bio-Rad DC (detergent compatible) protein assays according to manufacturer’s recommendation. Using Protocol 2, protein samples were extracted from NK cells of n = 6 healthy controls (HC) and n = 4 ME/CFS patients. In silico tryptic digest and enhanced signature peptide (ESP) predictor were used to predict high-responding TRPM3 tryptic peptides. Trypsin in-gel digestion was performed on protein samples loaded on SDS-PAGE gels (excised at 150–200 kDa). A liquid chromatography-multiple reaction monitoring (LC-MRM) method was optimized and used to evaluate the detectability of TRPM3 n = 5 proteotypic peptides in extracted protein samples.

Results: The detergent-based protocol protein yield was significantly higher (p < 0.05) compared with the ultrasonic-based protocol. The Pierce BCA protein assay showed more reproducibility and compatibility compared to the Bio-Rad DC protein assay. Two high-responding tryptic peptides (GANASAPDQLSLALAWNR and QAILFPNEEPSWK) for TRPM3 were detectable in n = 10 extracted protein samples from NK cells isolated from HC and ME/CFS patients.

Conclusion: A method was optimized for high yield protein extraction from human NK cells and for the first time TRPM3 proteotypic peptides were detected using LC-MRM. This new method provides for future research to assess membrane protein structural and functional relationships, particularly to facilitate proteomic investigation of TRPM3 ion channel isoforms in NK cells in both health and disease states, such as ME/CFS.

Targeted proteomics for rapid and robust peanut allergen quantification

This study improves LC-MS-based trace level peanut allergen quantification in processed food by refining method robustness, total analysis time and method sensitivity. Extraction buffer (six compared) and peptide choice were optimised and found to profoundly affect method robustness. A rapid extraction and in-solution digestion method was developed omitting subsequent reduction, alkylation and sample clean-up steps effectively reducing total analysis time from the previously reported ∼5.5-20 h to ∼2.5 h. For the three best performing peptides, accurate quantification (CVs < 15%) with matrix-matched calibration curves (R² = 0.99-0.97) was achieved for peanut muffin and ice-cream with excellent linearity (0.25-1000 mg kg^-1). The best performing peptide enabled excellent recovery rates in ice-cream (106.0 ± 15.1%) and peanut muffin (72.7 ± 13.4%). Sensitivity (LOD = 0.25-0.5 mg kg^-1; LOQ = 0.5-1.0 mg kg^-1) was 2- to 20-fold improved compared to previous methods depending on the peptide. These methodological improvements contribute to robust peanut detection in food and can be translated to additional food-borne allergens.

Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning

Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion and liquid chromatography and in a mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or overestimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of tandem mass spectrometry experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments and can be used with labeled or label-free approaches and with data-independent or data-dependent acquisition.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

UPLC-MS/MS-Based Analysis of Trastuzumab in Plasma Samples: Application in Breast Cancer Patients Sample Monitoring

Trastuzumab is a target-based recombinant humanized IgG1 monoclonal antibody (mAbs), extensively employed for treatment of metastatic breast cancer with human epidermal growth receptor 2 (HER2) overexpression. Studies around the world have reported that mAbs have substantial inter-patient unpredictable absorption, distribution, metabolism, and excretion (ADME-pharmacokinetics) because of multiple elements manipulating the concentration of mAbs in plasma. Herein, we have established a bioanalytical technique using UPLC-MS/MS with an easy sample workup method and in-solution digestion protocol to assay the trastuzumab plasma samples from breast cancer patients in clinical studies. Surrogated proteolytic peptides were used for accurate quantification of trastuzumab (CanMab) with a trastuzumab signature peptide with [13C6, 15N4]-arginine and [13C6, 15N2]-lysine stable isotope-labeled (SIL) peptide. Experiments to validate the method were accurately carried out according to the guidelines mentioned in the bioanalytical method validation protocol. The evaluation established excellent linearity over a wide range of 5–500 µg/mL. The experimental procedure was efficaciously performed in a pilot study of five breast cancer patients and residual concentrations of drugs from responding and non-responding subjects were compared. The receiver operating characteristic (ROC) examination displayed that 52.25 µg/mL was the Cmin threshold predictive response with a satisfactory sensitivity of 88.58% and specificity of 79.25%.

Deep learning approaches for data-independent acquisition proteomics

INTRODUCTION

Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

AREAS COVERED

This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases.

EXPERT OPINION

While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.

Tutorial: using nanoneedles for intracellular delivery

Intracellular delivery of advanced therapeutics, including biologicals and supramolecular agents, is complex because of the natural biological barriers that have evolved to protect the cell. Efficient delivery of therapeutic nucleic acids, proteins, peptides and nanoparticles is crucial for clinical adoption of emerging technologies that can benefit disease treatment through gene and cell therapy. Nanoneedles are arrays of vertical high-aspect-ratio nanostructures that can precisely manipulate complex processes at the cell interface, enabling effective intracellular delivery. This emerging technology has already enabled the development of efficient and non-destructive routes for direct access to intracellular environments and delivery of cell-impermeant payloads. However, successful implementation of this technology requires knowledge of several scientific fields, making it complex to access and adopt by researchers who are not directly involved in developing nanoneedle platforms. This presents an obstacle to the widespread adoption of nanoneedle technologies for drug delivery. This tutorial aims to equip researchers with the knowledge required to develop a nanoinjection workflow. It discusses the selection of nanoneedle devices, approaches for cargo loading and strategies for interfacing to biological systems and summarises an array of bioassays that can be used to evaluate the efficacy of intracellular delivery.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Characterization of HLA-A*33:03 epitopes via immunoprecipitation and LC-MS/MS

Human leukocyte antigen (HLA) class I has more than 18,000 alleles, each of which binds to a set of unique peptides from the cellular degradome. Deciphering the interaction between antigenic peptides and HLA proteins is crucial for understanding immune responses in autoimmune diseases and cancer. In this study, we aimed to characterize the peptidome that binds to HLA-A*33:03, which is one of the most prevalent HLA-A alleles in the Northeast Asian population, but poorly studied. For this purpose, we analyzed the HLA-A*33:03 monoallelic B cell line using immunoprecipitation of HLA-A and peptide complexes, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this study, we identified 5731 unique peptides that were associated with HLA A*33:03, and experimentally validated the affinity of 40 peptides for HLA-A*33:03 and their stability in HLA A*33:03-peptides complexes. To our knowledge, this study represents the largest dataset of peptides associated with HLA-A*33:03. Also, this is the first study in which HLA A*33:03-associated peptides were experimentally validated.

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

Simultaneous and quantitative monitoring transcription factors in human embryonic stem cell differentiation using mass spectrometry-based targeted proteomics

Human embryonic stem cells (hESCs) can be self-propagated indefinitely in culture while holding the capacity to generate almost all cell types. Although this powerful differentiation ability of hESCs has become a potential source of cell replacement therapies, application of stem cells in clinical practice relies heavily on the exquisite control of their developmental fate. In general, an essential first step in differentiation is to exit the pluripotent state, which is precariously balanced and depends on a variety of factors, mainly centering on the core transcriptional mechanism. To date, much evidence has indicated that transcription factors such as Sox2, Oct4, and Nanog control the self-renewal and pluripotency of hESCs. Their expression displays a restricted spatial-temporal pattern and their small changes in level can significantly affect directed differentiation and the cell type derived. So far, few assays have been developed to monitor this process. Herein, we provided a mass spectrometry (MS)-based approach for simultaneous and quantitative monitoring of these transcription factors, in an attempt to provide insight into their contributions in hESC differentiation.

PepS: An Innovative Microfluidic Device for Bedside Whole Blood Processing before Plasma Proteomics Analyses

Immunoassays have been used for decades in clinical laboratories to quantify proteins in serum and plasma samples. However, their limitations make them inappropriate in some cases. Recently, mass spectrometry (MS) based proteomics analysis has emerged as a promising alternative method when seeking to assess panels of protein biomarkers with a view to providing protein profiles to monitor health status. Up to now, however, translation of MS-based proteomics to the clinic has been hampered by its complexity and the substantial time and human resources necessary for sample preparation. Plasma matrix is particularly tricky to process as it contains more than 3000 proteins with concentrations spanning an extreme dynamic range (10¹⁰). To address this preanalytical challenge, we designed a microfluidic device (PepS) automating and accelerating blood sample preparation for bottom-up MS-based proteomics analysis. The microfluidic cartridge is operated through a dedicated compact instrument providing fully automated fluid processing and thermal control. In less than 2 h, the PepS device allows bedside plasma separation from whole blood, volume metering, depletion of albumin, protein digestion with trypsin, and stabilization of tryptic peptides on solid-phase extraction sorbent. For this first presentation, the performance of the PepS device was assessed using discovery proteomics and targeted proteomics, detecting a panel of three protein biomarkers routinely assayed in clinical laboratories (alanine aminotransferase 1, C-reactive protein, and myoglobin). This innovative microfluidic device and its associated instrumentation should help to streamline and simplify clinical proteomics studies.

Peptidomics-Driven Strategy Reveals Peptides and Predicted Proteases Associated With Oral Cancer Prognosis

Protease activity has been associated with pathological processes that can lead to cancer development and progression. However, understanding the pathological unbalance in proteolysis is challenging because changes can occur simultaneously at protease, their inhibitor, and substrate levels. Here, we present a pipeline that combines peptidomics, proteomics, and peptidase predictions for studying proteolytic events in the saliva of 79 patients and their association with oral squamous cell carcinoma (OSCC) prognosis. Our findings revealed differences in the saliva peptidome of patients with (pN+) or without (pN0) lymph-node metastasis and delivered a panel of ten endogenous peptides correlated with poor prognostic factors plus five molecules able to classify pN0 and pN+ patients (area under the receiver operating characteristic curve > 0.85). In addition, endopeptidases and exopeptidases putatively implicated in the processing of differential peptides were investigated using cancer tissue gene expression data from public repositories, reinforcing their association with poorer survival rates and prognosis in oral cancer. The dynamics of the OSCC-related proteolysis were further explored via the proteomic profiling of saliva. This revealed that peptidase/endopeptidase inhibitors exhibited reduced levels in the saliva of pN+ patients, as confirmed by selected reaction monitoring-mass spectrometry, while minor changes were detected in the level of saliva proteases. Taken together, our results indicated that proteolytic activity is accentuated in the saliva of patients with OSCC and lymph-node metastasis and, at least in part, is modulated by reduced levels of salivary peptidase inhibitors. Therefore, this integrated pipeline provided better comprehension and discovery of molecular features with implications in the oral cancer metastasis prognosis.

Shotgun proteomics coupled to nanoparticle-based biomarker enrichment reveals a novel panel of extracellular matrix proteins as candidate serum protein biomarkers for early-stage breast cancer detection

Background

The lack of specificity and high degree of false positive and false negative rates when using mammographic screening for detecting early-stage breast cancer is a critical issue. Blood-based molecular assays that could be used in adjunct with mammography for increased specificity and sensitivity could have profound clinical impact. Our objective was to discover and independently verify a panel of candidate blood-based biomarkers that could identify the earliest stages of breast cancer and complement current mammographic screening approaches.

Methods

We used affinity hydrogel nanoparticles coupled with LC-MS/MS analysis to enrich and analyze low-abundance proteins in serum samples from 20 patients with invasive ductal carcinoma (IDC) breast cancer and 20 female control individuals with positive mammograms and benign pathology at biopsy. We compared these results to those obtained from five cohorts of individuals diagnosed with cancer in organs other than breast (ovarian, lung, prostate, and colon cancer, as well as melanoma) to establish IDC-specific protein signatures. Twenty-four IDC candidate biomarkers were then verified by multiple reaction monitoring (LC-MRM) in an independent validation cohort of 60 serum samples specifically including earliest-stage breast cancer and benign controls (19 early-stage (T1a) IDC and 41 controls).

Results

In our discovery set, 56 proteins were increased in the serum samples from IDC patients, and 32 of these proteins were specific to IDC. Verification of a subset of these proteins in an independent cohort of early-stage T1a breast cancer yielded a panel of 4 proteins, ITGA2B (integrin subunit alpha IIb), FLNA (Filamin A), RAP1A (Ras-associated protein-1A), and TLN-1 (Talin-1), which classified breast cancer patients with 100% sensitivity and 85% specificity (AUC of 0.93).

Conclusions

Using a nanoparticle-based protein enrichment technology, we identified and verified a highly specific and sensitive protein signature indicative of early-stage breast cancer with no false positives when assessing benign and inflammatory controls. These markers have been previously reported in cell-ECM interaction and tumor microenvironment biology. Further studies with larger cohorts are needed to evaluate whether this biomarker panel improves the positive predictive value of mammography for breast cancer detection.

Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk In Vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction

Protein hydrolysates show great promise as bioactive food and feed ingredients and for valorization of side-streams from e.g., the fish processing industry. We present a novel approach for hydrolysate characterization that utilizes proteomics data for calculation of weighted mean peptide properties (length, molecular weight, and charge) and peptide-level abundance estimation. Using a novel bioinformatic approach for subsequent prediction of biofunctional properties of identified peptides, we are able to provide an unprecedented, in-depth characterization. The study further characterizes bulk emulsifying, foaming, and in vitro antioxidative properties of enzymatic hydrolysates derived from cod frame by application of Alcalase and Neutrase, individually and sequentially, as well as the influence of heat pre-treatment. All hydrolysates displayed comparable or higher emulsifying activity and stability than sodium caseinate. Heat-treatment significantly increased stability but showed a negative effect on the activity and degree of hydrolysis. Lower degrees of hydrolysis resulted in significantly higher chelating activity, while the opposite was observed for radical scavenging activity. Combining peptide abundance with bioinformatic prediction, we identified several peptides that are likely linked to the observed differences in bulk emulsifying properties. The study highlights the prospects of applying proteomics and bioinformatics for hydrolysate characterization and in food protein science.

Serum proteomics analysis of candidate predictive biomarker panel for the diagnosis of trastuzumab-based therapy resistant breast cancer

Human epidermal growth factor receptor 2 (HER2)-positive is a particularly aggressive type of the breast cancer. Trastuzumab-based therapy is a standard treatment for HER2-positive breast cancer, but some patients are resistant to the therapy. Serum proteins have been used to predict therapeutic benefit for various cancers, but whether serum proteins can serve as biomarkers for HER2-positive breast cancer remains unclear. Using an isobaric Tandem Mass Tag (TMT) label-based quantitative proteomic, we discovered 18 differentially expressed proteins in the serum of trastuzumab-based therapy resistant patients before therapy. Then, four proteins were selected and validated using an LC-MS/MS-based multiple reaction monitoring quantification method, and it was confirmed that three proteins (SRGN, LDHA and CST3) were correlated with trastuzumab-based therapy resistance. Finally, the trastuzumab-based therapy resistance diagnostic score was calculated and acquired by means of a logistic regression pattern based on the level of these three proteins. In summary, we develop a serum-based protein signature that potentially predicts the therapeutic effects of trastuzumab-based therapy for HER2-positive breast cancer patients.

CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies

Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, Compiled Interactive Resource for Extracellular and Surface Studies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess.

Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Peptide Selection for Accurate Targeted Protein Quantification via a Dimethylation High-Resolution Mass Spectrum Strategy with a Peptide Release Kinetic Model

A crucial step in accurate targeted protein quantification using targeted proteomics is to determine optimal proteotypic peptides representing targeted proteins. In this study, a workflow of peptide selection to determine proteotypic peptides using a dimethylation high-resolution mass spectrum strategy with a peptide release kinetic model was investigated and applied in peptide selection of bovine serum albumin. After specificity, digestibility, recovery, and stability evaluation of tryptic peptides in bovine serum albumin, the optimal proteotypic peptide was selected as LVNELTEFAK. The quantification method using LVNELTEFAK gave a linear range of 1-100 ppm with the coefficient greater than 0.9990, and the detection limit of bovine serum albumin in milk was 0.78 mg/kg. Compared with the proteotypic peptides selected by Skyline, the method showed a better performance in method validation. The workflow exhibited high comprehensiveness and efficiency in peptide selection, facilitating accurate targeted protein quantification in the food matrix, which lack protein standards.

Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs

LC/ESI/MS is the technique of choice for qualitative and quantitative food monitoring; however, analysis of a large number of compounds is challenged by the availability of standard substances. The impediment of detection of food contaminants has been overcome by the suspect and non-targeted screening. Still, the results from one laboratory cannot be compared with the results of another laboratory as quantitative results are required for this purpose. Here we show that the results of the suspect and non-targeted screening for pesticides can be made quantitative with the aid of in silico predicted electrospray ionization efficiencies and this allows direct comparison of the results obtained in two different laboratories. For this purpose, six cereal matrices were spiked with 134 pesticides and analysed in two independent labs; a high correlation for the results with the R² of 0.85.

Comprehensive and comparative exploration of the Atp7b−/− mouse plasma proteome

Wilson's disease (WD) is a rare genetic disease caused by mutations in the ATP7B gene. In this study, we used MS-based proteomics to explore the plasma proteome of the Atp7b^−/− mouse, a genetic and phenotypic model for WD.

Domain-specific Quantification of Prion Protein in Cerebrospinal Fluid by Targeted Mass Spectrometry

Therapies currently in preclinical development for prion disease seek to lower prion protein (PrP) expression in the brain. Trials of such therapies are likely to rely on quantification of PrP in cerebrospinal fluid (CSF) as a pharmacodynamic biomarker and possibly as a trial endpoint. Studies using PrP ELISA kits have shown that CSF PrP is lowered in the symptomatic phase of disease, a potential confounder for reading out the effect of PrP-lowering drugs in symptomatic patients. Because misfolding or proteolytic cleavage could potentially render PrP invisible to ELISA even if its concentration were constant or increasing in disease, we sought to establish an orthogonal method for CSF PrP quantification. We developed a multi-species targeted mass spectrometry method based on multiple reaction monitoring (MRM) of nine PrP tryptic peptides quantified relative to an isotopically labeled recombinant protein standard for human samples, or isotopically labeled synthetic peptides for nonhuman species. Analytical validation experiments showed process replicate coefficients of variation below 15%, good dilution linearity and recovery, and suitable performance for both CSF and brain homogenate and across humans as well as preclinical species of interest. In n = 55 CSF samples from individuals referred to prion surveillance centers with rapidly progressive dementia, all six human PrP peptides, spanning the N- and C-terminal domains of PrP, were uniformly reduced in prion disease cases compared with individuals with nonprion diagnoses. Thus, lowered CSF PrP concentration in prion disease is a genuine result of the disease process and not an artifact of ELISA-based measurement. As a result, dose-finding studies for PrP lowering drugs may need to be conducted in presymptomatic at-risk individuals rather than in symptomatic patients. We provide a targeted mass spectrometry-based method suitable for preclinical quantification of CSF PrP as a tool for drug development.

Probabilistic Limit of Detection for Ricin Identification Using a Shotgun Proteomics Assay

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/μL for a 2-μL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Rapid Identification of New Delhi Metallo-β-Lactamase (NDM) Using Tryptic Peptides and LC-MS/MS

There is significant interest in the development of mass spectrometry (MS) methods for antimicrobial resistance protein detection, given the ability of these methods to confirm protein expression. In this work, we studied the performance of a liquid chromatography, tandem MS multiple-reaction monitoring (LC-MS/MS MRM) method for the direct detection of the New Delhi metallo-β-lactamase (NDM) carbapenemase in clinical isolates. Using a genoproteomic approach, we selected three unique peptides (SLGNLGDADTEHYAASAR, AFGAAFPK, and ASMIVMSHSAPDSR) specific to NDM that were efficiently ionized and spectrally well-defined. These three peptides were used to build an assay with turnaround time of 90 min. In a blind set, the assay detected 21/24 bla _NDM-containing isolates and 76/76 isolates with negative results, corresponding to a sensitivity value of 87.5% (95% confidence interval [CI], 67.6% to 97.3%) and a specificity value of 100% (95% CI, 95.3% to 100%). One of the missed identifications was determined by protein fractionation to be due to low (∼0.1 fm/μg) NDM protein expression (below the assay limit of detection). Parallel disk diffusion susceptibility testing demonstrated this isolate to be meropenem susceptible, consistent with low NDM expression. Total proteomic analysis of the other two missed identifications did not detect NDM peptides but detected other proteins expressed from the bla _NDM-containing plasmids, confirming that the plasmids were not lost. The measurement of relative NDM concentrations over the entire isolate test set demonstrated variability spanning 4 orders of magnitude, further confirming the remarkable range that may be seen in levels of NDM expression. This report highlights the sensitivity of LC-MS/MS to variations in NDM protein expression, with implications for how this technology may be used.

Proteomic Insights into Phycobilisome Degradation, A Selective and Tightly Controlled Process in The Fast-Growing Cyanobacterium Synechococcus elongatus UTEX 2973

Phycobilisomes (PBSs) are large (3-5 megadalton) pigment-protein complexes in cyanobacteria that associate with thylakoid membranes and harvest light primarily for photosystem II. PBSs consist of highly ordered assemblies of pigmented phycobiliproteins (PBPs) and linker proteins that can account for up to half of the soluble protein in cells. Cyanobacteria adjust to changing environmental conditions by modulating PBS size and number. In response to nutrient depletion such as nitrogen (N) deprivation, PBSs are degraded in an extensive, tightly controlled, and reversible process. In Synechococcus elongatus UTEX 2973, a fast-growing cyanobacterium with a doubling time of two hours, the process of PBS degradation is very rapid, with 80% of PBSs per cell degraded in six hours under optimal light and CO₂ conditions. Proteomic analysis during PBS degradation and re-synthesis revealed multiple proteoforms of PBPs with partially degraded phycocyanobilin (PCB) pigments. NblA, a small proteolysis adaptor essential for PBS degradation, was characterized and validated with targeted mass spectrometry. NblA levels rose from essentially 0 to 25,000 copies per cell within 30 min of N depletion, and correlated with the rate of decrease in phycocyanin (PC). Implications of this correlation on the overall mechanism of PBS degradation during N deprivation are discussed.

Purple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomics

Emerging virus diseases present a global threat to public health. To detect viral pathogens in time-critical scenarios, accurate and fast diagnostic assays are required. Such assays can now be established using mass spectrometry-based targeted proteomics, by which viral proteins can be rapidly detected from complex samples down to the strain-level with high sensitivity and reproducibility. Developing such targeted assays involves tedious steps of peptide candidate selection, peptide synthesis, and assay optimization. Peptide selection requires extensive preprocessing by comparing candidate peptides against a large search space of background proteins. Here we present Purple (Picking unique relevant peptides for viral experiments), a software tool for selecting target-specific peptide candidates directly from given proteome sequence data. It comes with an intuitive graphical user interface, various parameter options and a threshold-based filtering strategy for homologous sequences. Purple enables peptide candidate selection across various taxonomic levels and filtering against backgrounds of varying complexity. Its functionality is demonstrated using data from different virus species and strains. Our software enables to build taxon-specific targeted assays and paves the way to time-efficient and robust viral diagnostics using targeted proteomics.

Identification of the OXA-48 Carbapenemase Family by Use of Tryptic Peptides and Liquid Chromatography-Tandem Mass Spectrometry

Phenotypic detection of the OXA-48-type class D β-lactamases in Enterobacteriaceae is challenging. We describe a rapid (less than 90 min) assay for the identification of OXA-48 family carbapenemases in subcultured bacterial isolates based on a genoproteomic approach. Following in silico trypsin digestion to ascertain theoretical core peptides common to the OXA-48 family, liquid chromatography-tandem mass spectrometry (LC-MS/MS) data-dependent acquisition was used to identify candidate peptide markers. Two peptides were selected based on performance characteristics: ANQAFLPASTFK, a core peptide common to all 12 OXA-48 family β-lactamase members, and YSVVPVYQEFAR, a highly specific peptide common to 11 of 12 OXA-48 family proteins providing the basis for an LC-MS/MS multiple reaction monitoring assay. An accuracy assessment was performed that included 98 isolates, 26 of which were OXA-48 positive. Two additional specificity assessments were performed including a mixture of isolates positive for OXA-48, KPC, NDM, VIM, and IMP carbapenemases. A combination of expert rules and expert judgment was applied by blinded operators to identify positive isolates. All isolates containing an OXA-48 family carbapenemase across all three test sets were correctly identified with no false positives, demonstrating 100% sensitivity (95% confidence interval [CI], 91.2% to 100%) and 100% specificity (95% CI, 96.2% to 100%) for the assay. These findings provide a framework for an LC-MS/MS-based method for the direct detection of OXA-48 family carbapenemases from cultured isolates that may have utility in predicting carbapenem resistance and tracking hospital outbreaks of OXA-48-carrying organisms.

Engineered peptide barcodes for in-depth analyses of binding protein libraries

Binding protein generation typically relies on laborious screening cascades that process candidate molecules individually. We have developed NestLink, a binder selection and identification technology able to biophysically characterize thousands of library members at once without the need to handle individual clones at any stage of the process. NestLink uses genetically encoded barcoding peptides termed flycodes, which were designed for maximal detectability by mass spectrometry and support accurate deep sequencing. We demonstrate NestLink's capacity to overcome the current limitations of binder-generation methods in three applications. First, we show that hundreds of binder candidates can be simultaneously ranked according to kinetic parameters. Next, we demonstrate deep mining of a nanobody immune repertoire for membrane protein binders, carried out entirely in solution without target immobilization. Finally, we identify rare binders against an integral membrane protein directly in the cellular environment of a human pathogen. NestLink opens avenues for the selection of tailored binder characteristics directly in tissues or in living organisms.

Evaluation of variable selection methods for random forests and omics data sets

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE).  In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

Rapid detection of colistin resistance protein MCR-1 by LC-MS/MS

Background

Colistin (polymyxin E) and polymixin B are important bactericidal antibiotics used in the treatment of serious infections caused by multi-drug resistant Gram-negative organisms. Transferrable plasmid-mediated colistin resistance, conferred by the product of the mcr-1 gene, has emerged as a global healthcare threat. Consequently, the rapid detection of the MCR-1 protein in clinical bacterial isolates has become increasingly important. We used a genoproteomic approach to identify unique peptides of the MCR-1 protein that could be detected rapidly by liquid chromatography tandem mass spectrometry (LC–MS/MS).

Methods

MCR-1 tryptic peptides that were efficiently ionized and readily detectable were characterized in a set of mcr-1-containing isolates with triple quadrupole LC–MS. Three optimal peptides were selected for the development of a rapid multiple reaction monitoring LC–MS/MS assay for the MCR-1 protein. To investigate the feasibility of rapid detection of the MCR-1 protein in bacterial isolates using this assay, a blinded 99-sample test set was built that included three additional mcr-1-containing clinical isolates tested in triplicate (9 samples) and 90 negative control isolates.

Results

All of the mcr-1-containing isolates in the test set were accurately identified with no false positive detections by three independent, blinded operators, yielding an overall performance of 100% sensitivity and specificity for multiple operators. Among the three peptides tested in this study, the best performing was DTFPQLAK. The isolate-to-result time for the assay as implemented is less than 90 min.

Conclusions

This work demonstrates the feasibility of rapid detection of the MCR-1 protein in bacterial isolates by LC–MS/MS.

Electronic supplementary material

The online version of this article (10.1186/s12014-019-9228-2) contains supplementary material, which is available to authorized users.

Unraveling the hidden universe of small proteins in bacterial genomes

Identification of small open reading frames (smORFs) encoding small proteins (≤ 100 amino acids; SEPs) is a challenge in the fields of genome annotation and protein discovery. Here, by combining a novel bioinformatics tool (RanSEPs) with “‐omics” approaches, we were able to describe 109 bacterial small ORFomes. Predictions were first validated by performing an exhaustive search of SEPs present in Mycoplasma pneumoniae proteome via mass spectrometry, which illustrated the limitations of shotgun approaches. Then, RanSEPs predictions were validated and compared with other tools using proteomic datasets from different bacterial species and SEPs from the literature. We found that up to 16 ± 9% of proteins in an organism could be classified as SEPs. Integration of RanSEPs predictions with transcriptomics data showed that some annotated non‐coding RNAs could in fact encode for SEPs. A functional study of SEPs highlighted an enrichment in the membrane, translation, metabolism, and nucleotide‐binding categories. Additionally, 9.7% of the SEPs included a N‐terminus predicted signal peptide. We envision RanSEPs as a tool to unmask the hidden universe of small bacterial proteins.

A rapid methods development workflow for high-throughput quantitative proteomic applications

Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors

Mass spectrometry (MS) has become a predominant choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose an algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on data sets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins. The advantages of LFAQ were further confirmed using the data from published papers.

Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification

Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from ~76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.