Large-scale Identification Of Proteins Research Articles

Large-scale identification of calcium oxalate stone inhibitory proteins in normal human urine

Recent evidence has shown that proteins in normal human urine can inhibit calcium oxalate (CaOx) kidney stone formation. Herein, we performed fast protein liquid chromatography (FPLC) to fractionate normal human urinary proteins using anion-exchange (DEAE) and size-exclusion (Superdex 200) materials. FPLC fractions (F1-F15) were examined by CaOx crystallization, growth, aggregation and crystal-cell adhesion assays. The fractions with potent inhibitory activities against CaOx crystals were then subjected to mass spectrometric protein identification. The data revealed that 13 of 15 fractions showed inhibitory activities in at least one crystal assay. Integrating CaOx inhibitory scores demonstrated that F6, F7 and F8 had the most potent inhibitory activities. NanoLC-ESI-Qq-TOF MS/MS identified 105, 93 and 53 proteins in F6, F7 and F8, respectively. Among them, 60 were found in at least two fractions and/or listed among known inhibitors with solid experimental evidence in the StoneMod database (https://www.stonemod.org). Interestingly, 10 of these 60 potential inhibitors have been reported with lower urinary levels in CaOx stone formers compared with healthy (non-stone) individuals, strengthening their roles as potent CaOx stone inhibitors. Our study provides the largest dataset of potential CaOx stone inhibitory proteins that will be useful for further elucidations of stone-forming mechanisms and ultimately for therapeutic/preventive applications.

Recent advances in protein precipitation-based methods for drug-target screening

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

DPI_CDF: druggable protein identifier using cascade deep forest

BackgroundDrug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor’s performance is still not satisfactory.MethodsIn this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.ResultsThe empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew’s-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.AvailabilityThe benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation.

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

Proximity-Dependent Biotinylation and Identification of Flagellar Proteins in Trypanosoma cruzi.

The flagellated kinetoplastid protozoan and causative agent of human Chagas disease, Trypanosoma cruzi, inhabits both invertebrate and mammalian hosts over the course of its complex life cycle. In these disparate environments, T. cruzi uses its single flagellum to propel motile life stages and, in some instances, to establish intimate contact with the host. Beyond its role in motility, the functional capabilities of the T. cruzi flagellum have not been defined. Moreover, the lack of proteomic information for this organelle, in any parasite life stage, has limited functional investigation. In this study, we employed a proximity-dependent biotinylation approach based on the differential targeting of the biotin ligase TurboID to the flagellum or cytosol in replicative stages of T. cruzi to identify proteins that are enriched in the flagellum by mass spectrometry. Proteomic analysis of the resulting biotinylated protein fractions yielded 218 candidate flagellar proteins in T. cruzi epimastigotes (insect stage) and 99 proteins in intracellular amastigotes (mammalian stage). Forty of these enriched flagellar proteins were common to both parasite life stages and included orthologs of known flagellar proteins in other trypanosomatid species, proteins specific to the T. cruzi lineage and hypothetical proteins. With the validation of flagellar localization for several of the identified candidates, our results demonstrate that TurboID-based proximity proteomics is an effective tool for probing subcellular compartments in T. cruzi. The proteomic data sets generated in this work offer a valuable resource to facilitate functional investigation of the understudied T. cruzi flagellum. IMPORTANCE Trypanosoma cruzi is a protozoan parasite that causes Chagas disease, which causes substantial morbidity and mortality in South and Central America. Throughout its life cycle, T. cruzi interacts with insect and mammalian hosts via its single flagellum, establishing intimate contact with host membranes. Currently, few flagellar proteins have been identified in T. cruzi that could provide insight into the mechanisms involved in mediating physical and biochemical interactions with the host. Here, we set out to identify flagellar proteins in the main replicative stages of T. cruzi using a proximity-labeling approach coupled with mass spectrometry. The >200 candidate flagellar proteins identified represent the first large-scale identification of candidate flagellar proteins in T. cruzi with preliminary validation. These data offer new avenues to investigate the biology of T. cruzi-host interactions, a promising area for development of new strategies aimed at the control of this pathogen.

Quantitative Phosphoproteomic Analysis Reveals Potential Regulatory Mechanisms of Early Fruit Enlargement in Pecan (Carya illinoinensis)

Pecan (Carya illinoinensis) is a popular tree nut. Its fruit development undergoes slow growth, rapid expansion, core hardening, and kernel maturation stages. However, little is known about how pecan initiates fruit development and enlargement after pollination. In this study, we performed the first large-scale identification of potential phosphorylation sites and proteins at early development of pecan fruit by a label-free phosphoproteomic quantification technique. A total of 2155 phosphosites were identified from 1953 phosphopeptides covering 1311 phosphoproteins in unpollinated pistils and fruits at 5 and 9 weeks after pollination. Of these, 699 nonredundant phosphoproteins were differentially phosphorylated (DP). Furthermore, the phosphorylation intensity of DP proteins in brassinolide (BR) and auxin signaling were analyzed, and the function of CiBZR1 was investigated. Ectopic expression of CiBZR1 resulted in BR response phenotypes with curled leaves and fruit, while enlarged seed size in Arabidopsis. Subcellular localization and transcriptional activation activity assay demonstrated that CiBZR1 distributed in both the nucleus and cytoplasm with transcriptional activity. When two phosphosites mutated, CiBZR1S201P,S205G moved to the nucleus completely, while the transcriptional activity remained unchanged. Taken together, our data reveal extensive phosphoproteins and lay a foundation to comprehensively dissect the potential post-translational regulation mechanism of early development of pecan fruit.

Large-scale identification of potential phase-separation proteins from plants using a cell-free system

Large-scale identification of potential phase-separation proteins from plants using a cell-free system

SWATH-MS based quantitative proteomics analysis reveals novel proteins involved in PAMP triggered immunity against potato late blight pathogen Phytophthora infestans.

Potato is the most important non-grain food in the world, while late blight caused by Phytophthora infestans seriously threatens the production of potato. Since pathogen-associated molecular patterns (PAMPs) are relatively conserved, PAMP-triggered immunity (PTI) can provide durable resistance to late blight for potato. However, knowledge of the regulatory mechanisms of PTI against oomycete pathogens at protein levels remains limited due to the small number of identified proteins. In the present work, changes in the proteome profile of Nicotiana benthamiana leaves upon P. infestans PAMP induction were examined using the SWATH-MS (sequential windowed acquisition of all theoretical mass spectra) approach, which provides quantification of protein abundances and large-scale identification of PTI-related proteins. A total of 4401 proteins have been identified, of which 1429 proteins were differentially expressed at least at one time point of 8, 12, 24 and 48h after PAMP induction, compared with the expression at 0h when immediately after PAMP induction. They were further analyzed by expression clustering and gene ontology (GO) enrichment analysis. Through functional verification, six novel DEPs of 19 candidates were proved to be involved in PTI responses, including mitochondrial phosphate carrier protein (MPT) 3, vesicle-associated membrane protein (VAMP) 714, lysophospholipase (LysoPL) 2, ascorbate peroxidase (APX) 1, heat shock 70 kDa protein (HSP) 2 and peptidyl-prolyl cis-trans isomerase FKBP (FKBP) 15-1. Taken together, the time course approach and the resulting large-scale proteomic analyses have enlarged our understanding of PTI mechanisms and provided a valuable resource for the discovery of complex protein networks involved in the resistance response of potato to late blight.

In-depth proteomic analysis reveals unique subtype-specific signatures in human small-cell lung cancer.

BackgroundSmall‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance.MethodsPellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues.ResultsOur quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y (e.g. GPX8, PKD2 and UFO).ConclusionsWe report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies.

DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods.

Protein Identification by Database Searching of Mass Spectrometry Data in the Teaching of Proteomics

Mass spectrometry is essential for large-scale protein identification in proteomics. By database searching of mass spectrometry data, it is possible to identify proteins without the need to interpret MS/MS spectra (de novo sequencing), which is time-consuming and not feasible as a systematic tool in proteomic analysis. Many authors have reported initiatives to teach protein mass spectrometry to undergraduate students, but database searching is usually not emphasized. Here, we propose a class about database searching of mass spectrometry data for protein identification in order to complement a theoretical proteomics course. It includes a presentation about the theory of database searching and a practical, computer-based activity in which students can learn how to perform analysis of mass spectrometry data for proteomics purposes. Our class model fills a widespread gap in the teaching of proteomics and improves students’ understanding of protein identification by mass spectrometry.

Software Options for the Analysis of MS-Proteomic Data.

Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.

ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence.

Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification.

Chemoproteomic Profiling of O-GlcNAcylation in Caenorhabditis elegans.

Genetic studies have revealed essential functions of O-linked N-acetylglucosamine (O-GlcNAc) modification in Caenorhabditis elegans. However, large-scale identification of O-GlcNAcylated proteins and mapping the modification sites in C. elegans remain relatively unexplored. By using a chemoproteomic strategy, we herein report the identification of 108 high-confidence O-GlcNAcylated proteins and 64 modification sites in C. elegans. Furthermore, quantitative proteomics upon altering O-GlcNAcylation show that the abundance of a large number of proteins are affected by O-GlcNAc. These proteins are involved in regulating reproduction and lifespan, which may correlate with the previously observed phenotypes in genetic studies. The data set in this study reveals the O-GlcNAc modification landscape in C. elegans and provides a valuable resource for dissecting the biological function of O-GlcNAcylation.

Calculation of False Discovery Rate for Peptide and Protein Identification.

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

Advances of the technologies in large-scale membrane proteome identification

Membrane proteins play important functions not only as receptors and transporters, but also in many other important intracellular functions such as photosynthetic and respiratory electron transport. Identification of membrane proteins is a necessary step to understand their functions. Membrane proteins are generally highly hydrophobic and difficult to be resolved by aqueous solutions, and large-scale proteomic identification of membrane proteins has been a great technical challenge. Significant efforts have been invested in the field to improve the solubility of membrane proteins in aqueous solutions that are compatible for mass spectrometry analysis. This review summarizes the main technological achievements in the field of membrane proteomics particularly for the improvement of membrane protein identification, and uses the photosynthetic model cyanobacterium Synechocystis sp. PCC6803 as an example to illustrate how technology advances push forward the field in terms of the increased coverage of membrane proteome identification.

Metaproteomics: Much More than Measuring Gene Expression in Microbial Communities.

Metaproteomics is the large-scale identification and quantification of proteins from microbial communities and thus provides direct insight into the phenotypes of microorganisms on the molecular level. Initially, metaproteomics was mainly used to assess the "expressed" metabolism and physiology of microbial community members. However, recently developed metaproteomic tools allow quantification of per-species biomass to determine community structure, in situ carbon sources of community members, and the uptake of labeled substrates by community members. In this perspective, I provide a brief overview of the questions that we can currently address, as well as new metaproteomics-based approaches that we and others are developing to address even more questions in the study of microbial communities and plant and animal microbiota. I also highlight some areas and technologies where I anticipate developments and potentially major breakthroughs in the next 5 years and beyond.

Metaproteomics of Freshwater Microbial Communities.

Recent advances in metaproteomics haveprovided us a link between genomic expression and functional characterization of environmental microbial communities. Therefore, the large-scale identification of proteins expressed by environmental microbiomes allowsan unprecedented view of their in situ metabolism and function. However, one of the main challenges in metaproteomics remains the lack of robust analytical pipelines. This is especially true for aquatic environments with low protein concentrations and the presence of compounds that are known to interfere with traditional sample preparation pipelines and downstream LC-MS/MS analyses. In this chapter, a semiquantitative method that spans from sample preparation to functional annotation is provided. This method has been shown to provide in-depth and representative results of both the eukaryotic and prokaryotic fractions of freshwater microbiomes.

Large-scale identification of human cerebrovascular proteins: Inter-tissue and intracerebral vascular protein diversity.

The human cerebrovascular system is responsible for regulating demand-dependent perfusion and maintaining the blood-brain barrier (BBB). In addition, defects in the human cerebrovasculature lead to stroke, intracerebral hemorrhage, vascular malformations, and vascular cognitive impairment. The objective of this study was to discover new proteins of the human cerebrovascular system using expression data from the Human Protein Atlas, a large-scale project which allows public access to immunohistochemical analysis of human tissues. We screened 20,158 proteins in the HPA and identified 346 expression patterns correlating to blood vessels in human brain. Independent experiments showed that 51/52 of these distributions could be experimentally replicated across different brain samples. Some proteins (40%) demonstrated endothelial cell (EC)-enriched expression, while others were expressed primarily in vascular smooth muscle cells (VSMC; 18%); 39% of these proteins were expressed in both cell types. Most brain EC markers were tissue oligospecific; that is, they were expressed in endothelia in an average of 4.8 out of 9 organs examined. Although most markers expressed in endothelial cells of the brain were present in all cerebral capillaries, a significant number (21%) were expressed only in a fraction of brain capillaries within each brain sample. Among proteins found in cerebral VSMC, virtually all were also expressed in peripheral VSMC and in non-vascular smooth muscle cells (SMC). Only one was potentially brain specific: VHL (Von Hippel-Lindau tumor suppressor). HRC (histidine rich calcium binding protein) and VHL were restricted to VSMC and not found in non-vascular tissues such as uterus or gut. In conclusion, we define a set of brain vascular proteins that could be relevant to understanding the unique physiology and pathophysiology of the human cerebrovasculature. This set of proteins defines inter-organ molecular differences in the vasculature and confirms the broad heterogeneity of vascular cells within the brain.

Differential accumulation of Xanthomonas campestris pv. campestris proteins during the interaction with the host plant: Contributions of an in vivo system.

Xanthomonas campestris pv. campestris (Xcc) is the causal agent of black rot, a highly destructive disease that affects all brassicas. This work aimed to study the interaction Xcc-Brassica oleracea using an in vivo system in an attempt to identify proteins involved in pathogenicity. We used label-free shotgun 2D-nanoUPLC/MSE to analyze Xcc proteins in three conditions: in the interaction with susceptible (REK) and resistant (REU) plants and in culture medium (control condition). A model of Xcc-susceptible host interaction is proposed and shows that Xcc increases the abundance of several crucial proteins for infection and cell protection. In this study, we also confirmed the differential expression by qPCR analysis of selected genes. This is the first report showing a large-scale identification of proteins in an in vivo host plant condition. Considering that most studies involving phytopathogens are in vitro (growth in culture medium or in plant extract), this work contributes with relevant information related to the plant-pathogen interaction in planta.