The effects of shared peptides on protein quantitation in label-free proteomics by LC/MS/MS

Assessment of differential protein abundance from the observed properties of detected peptides is an essential part of protein profiling based on shotgun proteomics. However, the abundance observed for shared peptides may be due to contributions from multiple proteins that are affected differently by a given treatment. Excluding shared peptides eliminates this ambiguity but may significantly decrease the number of proteins for which abundance estimates can be obtained. Peptide sharing within a family of biologically related proteins does not cause ambiguity if family members have a common response to treatment. On the basis of this concept, we have developed an approach for including shared peptides in the analysis of differential protein abundance in protein profiling. Data from a recent proteomics study of lung tissue from mice exposed to lipopolysaccharide, cigarette smoke, and a combination of these agents are used to illustrate our method. Starting from data where about half of the implicated database protein involved shared peptides, 82% of the affected proteins were grouped into families, based on FASTA annotation, with closure on peptide sharing. In many cases, a common abundance relative to control was sufficient to explain ion-current peak areas for peptides, both unique and shared, that identified biologically related proteins in a peptide-sharing closure group. On the basis of these results, we propose that peptide-sharing closure groups provide a way to include abundance data for shared peptides in quantitative protein profiling by high-throughput mass spectrometry.     

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.     

The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics

Peptide detectability is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification. Previously, predictors for peptide detectability trained on standard or complex samples were proposed. Although the models trained on complex samples may benefit from the large training data sets, it is unclear to what extent they are affected by the unequal abundances of identified proteins. To address this challenge and improve detectability prediction, we present a new algorithm for the iterative learning of peptide detectability from complex mixtures. We provide evidence that the new method approximates detectability with useful accuracy and, based on its design, can be used to interpret the outcome of other learning strategies. We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability. We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis. Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification.     

EBprot: Statistical analysis of labeling‐based quantitative proteomics data

Labeling‐based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein‐level ratios, which is obtained by summarizing peptide‐level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide‐protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide‐level analysis of EBprot provides better receiver‐operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein‐level ratios. We also demonstrate superior classification performance of peptide‐level EBprot analysis in a spike‐in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF‐stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide‐level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling‐based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/ . All MS data have been deposited in the ProteomeXchange with identifier PXD001426 ( http://proteomecentral.proteomexchange.org/dataset/PXD001426/ ).     

Solid-Phase Cross-Linking (SPCL): a new tool for protein structure studies

A wide range of chemical reagents are available to study the protein-protein interactions or protein structures. After reaction with such chemicals, covalently modified proteins are digested, resulting in shorter peptides that are analyzed by mass spectrometry (MS). Used especially when NMR of X-ray data are lacking, this methodology requires the identification of modified species carrying relevant information, among the unmodified peptides. To overcome the drawbacks of existing methods, we propose a more direct strategy relying on the synthesis of solid-supported cleavable monofunctional reagents and cross-linkers that react with proteins and that selectively release, after protein digestion and washings, the modified peptide fragments ready for MS analysis. Using this Solid-Phase Cross-Linking (SPCL) strategy, only modified sequences are analyzed and consistent data can be easily obtained since the signals of interest are not masked or suppressed by over-represented unmodified materials.     

A combined bioinformatic approach oriented to the analysis and design of peptides with high affinity to MHC class I molecules

We report on a new method to compute the antigenic degree of peptides from available experimental data on peptide binding affinity to class I MHC molecules. The methodology is a combination of two strategies at different levels of information. The first, at the primary structure level, consists in expressing the peptides binding activity as a profile of amino acid contributions, amino acid similarity being accounted for by their characteristic physicochemical properties and their position within the sequence. The higher level of the strategy is based on a meticulous analysis of the contact interface of the peptides with the cleft constituting the receptor region of a particular class I MHC molecule. Interaction interfaces are inferred by docking the peptide onto the receptor groove of the MHC molecule; evaluation of the affinity of the peptide to the receptor is then performed by analysis of the electrostatic and hydrophobic energies on points of the interaction interface.The result is a robust system for analysis of peptide affinity to class I MHC molecules since while the first analysis dictates the composition of active sequences at the amino acid level, the second translates this information to the atomic level, where the molecular interaction can be analyzed in terms of the intrinsic interatomic forces and energies. Evaluation results for the methodology are encouraging since high affinity peptides are reflected by high scores at both levels of information, and are proportionally lower for peptides of medium and lower affinity for which interaction surfaces show relatively lower electrostatic complementarity and hydrophobic correlation than for the former.     

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.     

Automated extraction of backbone deuteration levels from amide H/2H mass spectrometry experiments

A Fourier deconvolution method has been developed to explicitly determine the amount of backbone amide deuterium incorporated into protein regions or segments by hydrogen/deuterium (H/D) exchange with high-resolution mass spectrometry. Determination and analysis of the level and number of backbone amide exchanging in solution provide more information about the solvent accessibility of the protein than do previous centroid methods, which only calculate the average deuterons exchanged. After exchange, a protein is digested into peptides as a way of determining the exchange within a local area of the protein. The mass of a peptide upon deuteration is a sum of the natural isotope abundance, fast exchanging side-chain hydrogens (present in MALDI-TOF H/2H data) and backbone amide exchange. Removal of the components of the isotopic distribution due to the natural isotope abundances and the fast exchanging side-chains allows for a precise quantification of the levels of backbone amide exchange, as is shown by an example from protein kinase A. The deconvoluted results are affected by overlapping peptides or inconsistent mass envelopes, and evaluation procedures for these cases are discussed. Finally, a method for determining the back exchange corrected populations is presented, and its effect on the data is discussed under various circumstances.     

Online mass spectrometric analysis of proteins/peptides following electrolytic cleavage of disulfide bonds

The disulfide bond bridge is an important post-translational modification for proteins. This study presents a structural analysis of biologically active peptides and proteins containing disulfide bonds using electrochemistry (EC) online combined with desorption electrospray ionization mass spectrometry (DESI-MS), in which the sample undergoes electrolytic disulfide cleavage in an electrochemical flow cell followed by MS detection. Using this EC/DESI-MS method, the disulfide-containing peptides can be quickly identified from enzymatic digestion mixtures, simply based on the abrupt decrease in their relative ion abundances after electrolysis. Peptide mass mapping and tandem MS analysis of the ions of the resulting free peptide chains can possibly establish the disulfide linkage pattern and sequence the precursor peptides. In this regard, the method provides much more chemical information than previous analogous electrochemical analyses. In addition, derivatization of thiols by selective selenamide reagents is useful for easy recognition of reduced peptide ions and the number of their free thiols. Furthermore, electrolytic reduction of proteins (e.g., α-lactalbumin) leads to increased charges on the detected protein ions, revealing the role of disulfide bonds on maintaining protein conformation. This electrochemical mass spectrometric method is fast (completed in few minutes) and does not need chemical reductants, potentially having valuable applications in proteomics research.     

Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC-MS/MS data

The study of changes in protein levels between samples derived from cells representing different biological conditions is a key to the understanding of cellular function. There are two main methods available that allow both for global scanning for significantly varying proteins and targeted profiling of proteins of interest. One method is based on 2-D gel electrophoresis and image analysis of labelled proteins. The other method is based on LC-MS/MS analysis of either unlabelled peptides or peptides derived from isotopically labelled proteins or peptides. In this study, the non-labelling approach was used involving a new software, DeCyder MS Differential Analysis Software (DeCyder MS) intended for automated detection and relative quantitation of unlabelled peptides in LC-MS/MS data.Total protein extracts of E. coli strains expressing varying levels of dihydrofolate reductase and integron integrase were digested with trypsin and analyzed using a nanoscale liquid chromatography system, Ettan MDLC, online connected to an LTQTM linear ion-trap mass spectrometer fitted with a nanospray interface. Acquired MS data were subjected to DeCyder MS analysis where 2-D representations of the peptide patterns from individual LC-MS/MS analyses were matched and compared.This approach to unlabelled quantitative analysis of the E. coli proteome resulted in relative protein abundances that were in good agreement with results obtained from traditional methods for measuring protein levels.     

A Bayesian hierarchical model for identifying epitopes in peptide microarray data

Peptide Microarray Immunoassay (PMI for brevity) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. PMI studies aim at recognizing antigen-specific antibodies from serum samples and at detecting epitope regions of the protein antigen. PMI data present new challenges for statistical analysis mainly due to the structural dependence among peptides. A PMI is made of a complete library of consecutive peptides. They are synthesized by systematically shifting a window of a fixed number of amino acids through the finite sequence of amino acids of the antigen protein as ordered in the primary structure of the protein. This implies that consecutive peptides have a certain number of amino acids in common and hence are structurally dependent. We propose a new flexible Bayesian hierarchical model framework, which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the structural dependence between peptides through a suitable latent Markov structure. The proposed model is illustrated using PMI data from a recent study about egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.     

Probing Protein Sequences as Sources for Encrypted Antimicrobial Peptides

Starting from the premise that a wealth of potentially biologically active peptides may lurk within proteins, we describe here a methodology to identify putative antimicrobial peptides encrypted in protein sequences. Candidate peptides were identified using a new screening procedure based on physicochemical criteria to reveal matching peptides within protein databases. Fifteen such peptides, along with a range of natural antimicrobial peptides, were examined using DSC and CD to characterize their interaction with phospholipid membranes. Principal component analysis of DSC data shows that the investigated peptides group according to their effects on the main phase transition of phospholipid vesicles, and that these effects correlate both to antimicrobial activity and to the changes in peptide secondary structure. Consequently, we have been able to identify novel antimicrobial peptides from larger proteins not hitherto associated with such activity, mimicking endogenous and/or exogenous microorganism enzymatic processing of parent proteins to smaller bioactive molecules. A biotechnological application for this methodology is explored. Soybean (Glycine max) plants, transformed to include a putative antimicrobial protein fragment encoded in its own genome were tested for tolerance against Phakopsora pachyrhizi, the causative agent of the Asian soybean rust. This procedure may represent an inventive alternative to the transgenic technology, since the genetic material to be used belongs to the host organism and not to exogenous sources.     

Recognized sequence and conformation in design of linear peptides as a competitive inhibitor for HMG-CoA reductase

This study is an attempt to develop a simple search method for lead peptide candidates, which include constrained structures in a recognized sequence, using the design of a competitive inhibitor for HMG-CoA reductase (HMGR). A structure-functional analysis of previously synthesized peptides proposes that a competitive inhibitory peptide can be designed by maintaining bioactive conformation in a recognized sequence. A conformational aspect of the structure-based approach was applied to the peptide design. By analysis of the projections obtained through a principle component analysis (PCA) for short linear and cyclic peptides, a head-to-tail peptide cycle is considered as a model for its linear analogy. It is proposed that activities of the linear peptides based on an identical amino acid sequence, which are obtained from a less flexible peptide cycle, would be relatively higher than those obtained from more flexible cyclic peptides. The design criterion was formulated in terms of a 'V' parameter, reflecting a relative deviation of an individual peptide cycle from an average statistical peptide cycle based on all optimized structures of the cyclic peptides in set. Twelve peptide cycles were selected for the peptide library. Comparing the calculated 'V' parameters, two cyclic peptides (GLPTGG and GFPTGG) were selected as lead cycles from the library. Based on these sequences, six linear peptides obtained by breaking the cycle at different positions were selected as lead peptide candidates. The linear GFPTGG peptide, showing the highest inhibitory activity against HMGR, increases the inhibitory potency nearly tenfold. Kinetic analysis reveals that the GFPTGG peptide is a competitive inhibitor of HMG-CoA with an equilibrium constant of inhibitor binding (K(i)) of 6.4 +/- 0.3 microM. Conformational data support a conformation of the designed peptides close to the bioactive conformation of the previously synthesized active peptides.     

Peptide entry inhibitors of enveloped viruses: The importance of interfacial hydrophobicity

There are many peptides known that inhibit the entry of enveloped viruses into cells, including one peptide that is successfully being used in the clinic as a drug. In this review, we discuss the discovery, antiviral activity and mechanism of action of such peptides. While peptide entry inhibitors have been discovered by a wide variety of approaches (structure-based, accidental, intentional, rational and brute force) we show here that they share a common physical chemical property: they are at least somewhat hydrophobic and/or amphipathic and have a propensity to interact with membrane interfaces. We propose that this propensity drives a shared mechanism of action for many peptide entry inhibitors, involving direct interactions with viral and cellular membranes, as well as interactions with the complex hydrophobic protein/lipid interfaces that are exposed, at least transiently, during virus–cell fusion. By interacting simultaneously with the membrane interfaces and other critical hydrophobic surfaces, we hypothesize that peptide entry inhibitors can act by changing the physical chemistry of the membranes, and the fusion protein interfaces bridging them, and by doing so interfere with the fusion of cellular and viral membranes. Based on this idea, we propose that an approach that focuses on the interfacial hydrophobicity of putative entry inhibitors could lead to the efficient discovery of novel, broad-spectrum viral entry inhibitors. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova.     

Automated comparative proteomics based on multiplex tandem mass spectrometry and stable isotope labeling

Comparative proteomic approaches using isotopic labeling and MS have become increasingly popular. Conventionally quantification is based on MS or extracted ion chromatogram (XIC) signals of differentially labeled peptides. However, in these MS-based experiments, the accuracy and dynamic range of quantification are limited by the high noise levels of MS/XIC data. Here we report a quantitative strategy based on multiplex (derived from multiple precursor ions) MS/MS data. One set of proteins was metabolically labeled with [13C6]lysine and [15N4]arginine; the other set was unlabeled. For peptide analysis after tryptic digestion of the labeled proteins, a wide precursor window was used to include both the light and heavy versions of each peptide for fragmentation. The multiplex MS/MS data were used for both protein identification and quantification. The use of the wide precursor window increased sensitivity, and the y ion pairs in the multiplex MS/MS spectra from peptides containing labeled and unlabeled lysine or arginine offered more information for, and thus the potential for improving, protein identification. Protein ratios were obtained by comparing intensities of y ions derived from the light and heavy peptides. Our results indicated that this method offers several advantages over the conventional XIC-based approach, including increased sensitivity for protein identification and more accurate quantification with more than a 10-fold increase in dynamic range. In addition, the quantification calculation process was fast, fully automated, and independent of instrument and data type. This method was further validated by quantitative analysis of signaling proteins in the EphB2 pathway in NG108 cells.     

BioPD: a web-based information center for bioactive peptides

Bioactive peptide database (BioPD) is a web-based knowledge base that contains more than 1100 protein sequences from human, mouse and rat, which are putative or are known to be bioactive peptides. In addition to peptide sequences and the annotation, the database also contains gene sequences with annotation, protein interaction and disease data related to the peptides. Each entry has as many references as possible to support the information represented. BioPD consists of six parts: PROTEIN, GENE, DISEASE, LINKS, INTERACTION, and REFERENCE. The database is searchable through keyword, gene and protein name, receptor name, etc. The links to PDB, InterPro, Pfam, OMIM, etc. are provided in each entry. Thus BioPD is formed as an information center for the bioactive peptide and serves as a gateway for exploration of bioactive peptides. The database can be accessed at http://biopd.bjmu.edu.cn.