An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage, Quantitative Accuracy and Precision.

Tandem mass tags (TMT) are widely used in proteomics to simultaneously quantify multiple samples in a single experiment. The tags can be easily added to the primary amines of peptides/proteins through chemical reactions. In addition to amines, TMT reagents also partially react with the hydroxyl groups of serine, threonine, and tyrosine residues under alkaline conditions, which significantly compromises the analytical sensitivity and precision. Under alkaline conditions, reducing the TMT molar excess can partially mitigate overlabeling of histidine-free peptides, but has a limited effect on peptides containing histidine and hydroxyl groups. Here, we present a method under acidic conditions to suppress overlabeling while efficiently labeling amines, using only one-fifth of the TMT amount recommended by the manufacturer. In a deep-scale analysis of a yeast/human two-proteome sample, we systematically evaluated our method against the manufacturer's method and a previously reported TMT-reduced method. Our method reduced overlabeled peptides by 9-fold and 6-fold, respectively, resulting in the substantial enhancement in peptide/protein identification rates. More importantly, the quantitative accuracy and precision were improved as overlabeling was reduced, endowing our method with greater statistical power to detect 42% and 12% more statistically significant yeast proteins compared to the standard and TMT-reduced methods, respectively. Mass spectrometric data have been deposited in the ProteomeXchange Consortium via the iProX partner repository with the data set identifier PXD047052.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics.

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

Assessment of Tractable Cysteines for Covalent Targeting by Screening Covalent Fragments.

Targeted covalent inhibition and the use of irreversible chemical probes are important strategies in chemical biology and drug discovery. To date, the availability and reactivity of cysteine residues amenable for covalent targeting have been evaluated by proteomic and computational tools. Herein, we present a toolbox of fragments containing a 3,5-bis(trifluoromethyl)phenyl core that was equipped with chemically diverse electrophilic warheads showing a range of reactivities. We characterized the library members for their reactivity, aqueous stability and specificity for nucleophilic amino acids. By screening this library against a set of enzymes amenable for covalent inhibition, we showed that this approach experimentally characterized the accessibility and reactivity of targeted cysteines. Interesting covalent fragment hits were obtained for all investigated cysteine-containing enzymes.

Computational assessment of the feasibility of protonation-based protein sequencing.

Recent advances in DNA sequencing methods revolutionized biology by providing highly accurate reads, with high throughput or high read length. These read data are being used in many biological and medical applications. Modern DNA sequencing methods have no equivalent in protein sequencing, severely limiting the widespread application of protein data. Recently, several optical protein sequencing methods have been proposed that rely on the fluorescent labeling of amino acids. Here, we introduce the reprotonation-deprotonation protein sequencing method. Unlike other methods, this proposed technique relies on the measurement of an electrical signal and requires no fluorescent labeling. In reprotonation-deprotonation protein sequencing, the terminal amino acid is identified through its unique protonation signal, and by repeatedly cleaving the terminal amino acids one-by-one, each amino acid in the peptide is measured. By means of simulations, we show that, given a reference database of known proteins, reprotonation-deprotonation sequencing has the potential to correctly identify proteins in a sample. Our simulations provide target values for the signal-to-noise ratios that sensor devices need to attain in order to detect reprotonation-deprotonation events, as well as suitable pH values and required measurement times per amino acid. For instance, an SNR of 10 is required for a 61.71% proteome recovery rate with 100 ms measurement time per amino acid.

Assessment of Protein Profiles of RNAlater Stored and Fresh PBMC Cells Using Different Protein Extraction Buffers.

For proteome analyses, the tissue samples are mostly preserved either snap frozen or formalin-fixed, paraffin-embedded form. Use of RNAlater-a non-toxic solution primarily used to stabilize the RNA content of samples-in tissue preservation for proteome analysis recently described equally reliable with snap-frozen preservation in human tissues. Even though RNALater storage has great potential in the preservation of Peripheral Blood Mononuclear Cells (PBMC), its impact on the results of proteome analysis is poorly described at qualitative and quantitative measures. The present study investigated protein profiles of RNAlater preserved and fresh PBMCs using three extraction buffers viz. Triton X-100, RIPA and SDS. Proteins are separated in SDS-PAGE and quantified using densitometry. On an average 19.3 bands from fresh and 15.6 bands from RNAlater storage cells were obtained with a molecular weight ranging from 25 to > 250 kDa. RNAlater storage generated a fewer number and lesser quantity of low molecular weight proteins while yielded a similar or high quantity of high molecular weight protein fractions. The principal component analysis showed that Triton X-100 is inferior as compared to SDS and RIPA with respect to their protein bands and quantity yielded. While RNAlater is effective in preserving PBMC for proteome analysis, our findings warrant caution in its use in proteomics experiments especially if the target is low molecular weight proteins.

Unveiling Taenia solium kinome profile and its potential for new therapeutic targets.

Background: Helminth infections cause widespread morbidity and are a significant global disease burden. One among them is Neurocysticercosis, a central nervous system infection caused by the larvae Taenia solium, leading to epilepsy. Helminths are strong immune modulators and can survive for a long time in adverse host environments. Kinases are molecular switches and are essential to initiate/propagate signaling cascades and are detrimental to the regulation of homeostasis. They have been implicated in the progression of many diseases and are potentially lucrative drug targets.Objective: To identify kinases in T. solium proteome and prioritize them as drug targets.Methodology: A Hidden Markov Model (HMM) was used to curate and classify kinases into families based on sequence homology to model organisms followed by phylogenetic analysis of each family. To predict potential drug targets, kinases were identified based on a homologically lethal relationship to C. elegans but non-lethal to humans. Kinases thus selected were searched for matching ligands in SARFkinase and DrugBank databases.Result and conclusion: T. solium kinases make up 1.8% of its proteome, CMGC is the largest kinase family and RGC is the smallest and catalytically inactive family. We predict 23-potential kinases to be drug targets for T. solium.[Figure: see text].

Target 2035: probing the human proteome.

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver.

Here, we present a major update to the SUPERFAMILY database and the webserver. We describe the addition of new SUPERFAMILY 2.0 profile HMM library containing a total of 27 623 HMMs. The database now includes Superfamily domain annotations for millions of protein sequences taken from the Universal Protein Recourse Knowledgebase (UniProtKB) and the National Center for Biotechnology Information (NCBI). This addition constitutes about 51 and 45 million distinct protein sequences obtained from UniProtKB and NCBI respectively. Currently, the database contains annotations for 63 244 and 102 151 complete genomes taken from UniProtKB and NCBI respectively. The current sequence collection and genome update is the biggest so far in the history of SUPERFAMILY updates. In order to the deal with the massive wealth of information, here we introduce a new SUPERFAMILY 2.0 webserver (http://supfam.org). Currently, the webserver mainly focuses on the search, retrieval and display of Superfamily annotation for the entire sequence and genome collection in the database.

Systematic and Quantitative Assessment of Hydrogen Peroxide Reactivity With Cysteines Across Human Proteomes.

Protein cysteinyl residues are the mediators of hydrogen peroxide (H2O2)-dependent redox signaling. However, site-specific mapping of the selectivity and dynamics of these redox reactions in cells poses a major analytical challenge. Here we describe a chemoproteomic platform to systematically and quantitatively analyze the reactivity of thousands of cysteines toward H2O2 in human cells. We identified >900 H2O2-sensitive cysteines, which are defined as the H2O2-dependent redoxome. Although redox sites associated with antioxidative and metabolic functions are consistent, most of the H2O2-dependent redoxome varies dramatically between different cells. Structural analyses reveal that H2O2-sensitive cysteines are less conserved than their redox-insensitive counterparts and display distinct sequence motifs, structural features, and potential for crosstalk with lysine modifications. Notably, our chemoproteomic platform also provides an opportunity to predict oxidation-triggered protein conformational changes. The data are freely accessible as a resource at http://redox.ncpsb.org/OXID/.

Architecture of the human interactome defines protein communities and disease networks.

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance.

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

A Mutation Model from First Principles of the Genetic Code.

The paper presents a neutral Codons Probability Mutations (CPM) model of molecular evolution and genetic decay of an organism. The CPM model uses a Markov process with a 20-dimensional state space of probability distributions over amino acids. The transition matrix of the Markov process includes the mutation rate and those single point mutations compatible with the genetic code. This is an alternative to the standard Point Accepted Mutation (PAM) and BLOcks of amino acid SUbstitution Matrix (BLOSUM). Genetic decay is quantified as a similarity between the amino acid distribution of proteins from a (group of) species on one hand, and the equilibrium distribution of the Markov chain on the other. Amino acid data for the eukaryote, bacterium, and archaea families are used to illustrate how both the CPM and PAM models predict their genetic decay towards the equilibrium value of 1. A family of bacteria is studied in more detail. It is found that warm environment organisms on average have a higher degree of genetic decay compared to those species that live in cold environments. The paper addresses a new codon-based approach to quantify genetic decay due to single point mutations compatible with the genetic code. The present work may be seen as a first approach to use codon-based Markov models to study how genetic entropy increases with time in an effectively neutral biological regime. Various extensions of the model are also discussed.

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. RESULTS: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. CONCLUSIONS: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

An assessment of the amount of untapped fold level novelty in under-sampled areas of the tree of life.

Previous studies of protein fold space suggest that fold coverage is plateauing. However, sequence sampling has been -and remains to a large extent- heavily biased, focusing on culturable phyla. Sustained technological developments have fuelled the advent of metagenomics and single-cell sequencing, which might correct the current sequencing bias. The extent to which these efforts affect structural diversity remains unclear, although preliminary results suggest that uncultured organisms could constitute a source of new folds. We investigate to what extent genomes from uncultured and under-sampled phyla accessed through single cell sequencing, metagenomics and high-throughput culturing efforts have the potential to increase protein fold space, and conclude that i) genomes from under-sampled phyla appear enriched in sequences not covered by current protein family and fold profile libraries, ii) this enrichment is linked to an excess of short (and possibly partly spurious) sequences in some of the datasets, iii) the discovery rate of novel folds among sequences uncovered by current fold and family profile libraries may be as high as 36%, but would ultimately translate into a marginal increase in global discovery of novel folds. Thus, genomes from under-sampled phyla should have a rather limited impact on increasing coarse grained tertiary structure level novelty.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. BIOLOGICAL SIGNIFICANCE: QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics.

Biocompatibility assessment of haemodialysis membrane materials by proteomic investigations.

The exposure of blood to an artificial surface such as the haemodialysis membrane results in the nearly instantaneous deposition of a layer of plasma proteins. The composition of the protein layer profoundly influences all subsequent events, and to a large extent determines the biocompatibility of the biomaterial. In the present study, we examine the protein adsorption capacity and coagulation profiles of the polysulfone-based helixone material in comparison to cellulose triacetate. A differential profiling investigation using shotgun proteomics data-independent analysis was applied to eluates obtained with each membrane after a dialysis session, in order to assess the function of desorbed proteins. Functional classification and network analysis performed using bioinformatics tools shed light on the involvement of adsorbed proteins into important molecular processes, such as lipid transport and metabolism, cell growth differentiation and communication, and the coagulation cascade. The collected evidence was further validated by targeted mass spectrometry using selected reaction monitoring on proteotypic transitions of key protein effectors, confirming the different panels of adsorbed protein on each membrane. The coagulation profile during haemodialysis of patients under polysulfone-based helixone filter cartridges was also assessed showing a slightly higher platelet activation profile after the dialysis session. The overall collected evidence highlights a modulation of the coagulation biological pathway during haemodialysis, which is largely influenced by the biomaterial used.

Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility.

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.

Deep conservation of human protein tandem repeats within the eukaryotes.

Tandem repeats (TRs) are a major element of protein sequences in all domains of life. They are particularly abundant in mammals, where by conservative estimates one in three proteins contain a TR. High generation-scale duplication and deletion rates were reported for nucleic TR units. However, it is not known whether protein TR units can also be frequently lost or gained providing a source of variation for rapid adaptation of protein function, or alternatively, tend to have conserved TR unit configurations over long evolutionary times. To obtain a systematic picture, we performed a proteome-wide analysis of the mode of evolution for human protein TRs. For this purpose, we propose a novel method for the detection of orthologous TRs based on circular profile hidden Markov models. For all detected TRs, we reconstructed bispecies TR unit phylogenies across 61 eukaryotes ranging from human to yeast. Moreover, we performed additional analyses to correlate functional and structural annotations of human TRs with their mode of evolution. Surprisingly, we find that the vast majority of human TRs are ancient, with TR unit number and order preserved intact since distant speciation events. For example, ≥ 61% of all human TRs have been strongly conserved at least since the root of all mammals, approximately 300 Ma. Further, we find no human protein TR that shows evidence for strong recent duplications and deletions. The results are in contrast to the high generation-scale mutability of nucleic TRs. Presumably, most protein TRs fold into stable and conserved structures that are indispensable for the function of the TR-containing protein. All of our data and results are available for download from http://www.atgc-montpellier.fr/TRE.