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Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

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Shotgun proteomics based on peptide fractionation by using liquid chromatography has become the common procedure for proteomic studies, although in the very beginning of the field, protein separation by using electrophoresis was the main tool. Nonetheless, during the last two decades, the electrophoretic techniques for peptide mixtures fractionation have evolved as a result of relevant technological improvements. We also proposed the combination of sodium dodecyl sulfate polyacrylamide gel electrophoresis for protein fractionation and sodium dodecyl sulfate free polyacrylamide gel electrophoresis for peptide separation as a novel procedure for proteomic studies. Here, we present an optimized device for sodium dodecyl sulfate free polyacrylamide gel electrophoresis improving peptide recoveries respect to the established electrophoretic technique off gel electrophoresis meanwhile conserving the excellent resolution described for the former technique in slab gel based systems. The device simultaneously allows the separation and the collection of fractionated peptides in solution.

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CIGB-552 is a second generation antitumor peptide that displays potent cytotoxicity in lung and colon cancer cells. The nuclear subproteome of HT-29 colon adenocarcinoma cells treated with CIGB-552 peptide was identified and analyzed [1]. This data article provides supporting evidence for the above analysis.

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The second generation peptide CIGB-552 has a pro-apoptotic effect on H460 non-small cell lung cancer cells and displays a potent cytotoxic effect in HT-29 colon adenocarcinoma cells though its action mechanism is ill defined. Here, we present the first proteomic study of peptide effect in HT-29 cells using subcellular fractionation, protein and peptide fractionation by DF-PAGE and LC-MS/MS peptide identification. In particular, we explored the nuclear proteome of HT-29 cells at a 5h treatment identifying a total of 68 differentially modulated proteins, 49 of which localize to the nucleus. The differentially modulated proteins were analyzed following a system biology approach. Results pointed to a modulation of apoptosis, oxidative damage removal, NF-κB activation, inflammatory signaling and of cell adhesion and motility. Further Western blot and flow-cytometry experiments confirmed both pro-apoptotic and anti-inflammatory effects of CIGB-552 peptide in HT-29 cells.

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Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

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The Java BioWareHouse (JBioWH) project is an open-source platform-independent programming framework that allows a user to build his/her own integrated database from the most popular data sources. JBioWH can be used for intensive querying of multiple data sources and the creation of streamlined task-specific data sets on local PCs. JBioWH is based on a MySQL relational database scheme and includes JAVA API parser functions for retrieving data from 20 public databases (e.g. NCBI, KEGG, etc.). It also includes a client desktop application for (non-programmer) users to query data. In addition, JBioWH can be tailored for use in specific circumstances, including the handling of massive queries for high-throughput analyses or CPU intensive calculations. The framework is provided with complete documentation and application examples and it can be downloaded from the Project Web site at http://code.google.com/p/jbiowh. A MySQL server is available for demonstration purposes at hydrax.icgeb.trieste.it:3307. Database URL: http://code.google.com/p/jbiowh.

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SUMMARY: Protein identification by mass spectrometry is commonly accomplished using a peptide sequence matching search algorithm, whose sensitivity varies inversely with the size of the sequence database and the number of post-translational modifications considered. We present the Spectrum Identification Machine, a peptide sequence matching tool that capitalizes on the high-intensity b1-fragment ion of tandem mass spectra of peptides coupled in solution with phenylisotiocyanate to confidently sequence the first amino acid and ultimately reduce the search space. We demonstrate that in complex search spaces, a gain of some 120% in sensitivity can be achieved. AVAILABILITY: All data generated and the software are freely available for academic use at http://proteomics.fiocruz.br/software/sim. CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Peptide sequence matching algorithms used for peptide identification by tandem mass spectrometry (MS/MS) enumerate theoretical peptides from the database, predict their fragment ions, and match them to the experimental MS/MS spectra. Here, we present an approach for scoring MS/MS identifications based on the high mass accuracy matching of precursor ions, the identification of a high intensity b1 fragment ion, and partial sequence tags from phenylthiocarbamoyl-derivatized peptides. This derivatization process boosts the b1 fragment ion signal, which turns it into a powerful feature for peptide identification. We demonstrate the effectiveness of our scoring system by implementing it on a computational tool called "HI-bone" and by identifying mass spectra of an Escherichia coli sample acquired on an Orbitrap Velos instrument using Higher-energy C-trap dissociation. Following this strategy, we identified 1614 peptide spectrum matches with a peptide false discovery rate (FDR) below 1%. These results were significantly higher than those from Mascot and SEQUEST using a similar FDR.

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

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Computational algorithms to explore the conformational space of small molecules are complex and computer demand field in chemoinformatics. In this paper a hybrid algorithm to explore the conformational space of organic molecules is presented. This hybrid algorithm is based in a systematic search approach combined with a Monte Carlo based method in order to obtain an ensemble of low-energy conformations simulating the flexibility of small chemical compounds. The Monte Carlo method uses the Metropolis criterion to accept or reject a conformation through an in-house implementation of the MMFF94s force field to calculate the conformational energy. The parallel design of this algorithm, based on the message passing interface (MPI) paradigm, was implemented. The results showed a performance increase in the terms of speed and efficiency.

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IPG (Immobilized pH Gradient) based separations are frequently used as the first step in shotgun proteomics methods; it yields an increase in both the dynamic range and resolution of peptide separation prior to the LC-MS analysis. Experimental isoelectric point (pI) values can improve peptide identifications in conjunction with MS/MS information. Thus, accurate estimation of the pI value based on the amino acid sequence becomes critical to perform these kinds of experiments. Nowadays, pI is commonly predicted using the charge-state model [1], and/or the cofactor algorithm [2]. However, none of these methods is capable of calculating the pI value for basic peptides accurately. In this manuscript, we present an new approach that can significant improve the pI estimation, by using Support Vector Machines (SVM) [3], an experimental amino acid descriptor taken from the AAIndex database [4] and the isoelectric point predicted by the charge-state model. Our results have shown a strong correlation (R(2)=0.98) between the predicted and observed values, with a standard deviation of 0.32 pH units across the complete pH range.

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A random hexapeptide library (one-bead-one-compound), containing sixteen amino acids (16(6) different sequences) was synthesized on a Tentagel resin previously modified with a dipeptide linker (Asp-Pro). This peptide bond is highly susceptible to cleavage under mild acidic conditions in a salt-free solution prepared with H(2)(16)O/H(2)(18)O (60/40% v/v). In the hydrolysis, hexapeptides are released with an additional Asp residue partially labeled with (18)O at the C-terminus. These conditions are fully compatible with ESI-MS analysis and facilitate sequencing by MS, as N- and C-terminal ions can be easily differentiated in MS/MS spectra. The peptides were sequenced manually and also with de novo sequencing programs, and identifying them in a database containing all possible heptapeptide sequences or in a filtered database. The proposed strategy is also compatible with stepwise Edman degradation using either intact beads or the released free peptides.

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In this work, a method devised for the selective isolation of multiply-charged peptide applied to a complex protein mixture was evaluated for the first time using a mass spectrometer with low resolution (LTQ). In this procedure, all primary amino groups of tryptic peptides derived from human Liver tissue interstitial fluid (TIF) are blocked, restricting their positive charge, at acidic pH, to the presence of histidine and arginine residues. After strong cation exchange chromatography, multiply-charged peptides (#R+#H > 1) are retained in the column and separated with high selectivity from singly (#R+#H = 1) and neutral peptides (#R +#H = 0) which are collected together in the flow-through. Using Liquid chromatography electrospray ionization tandem mass spectrometry analysis the retained fraction displayed a 95% enrichment of multiply charged peptides while in the flow-through; only 4% of multiply-charged peptides were identified.

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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%.

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SDS-free polyacrylamide gel electrophoresis is an effective alternative approach to peptide fractionation. Here we describe a discontinuous buffer system at acid pH that improves the separation of acidic peptides from tryptic digestion. MOPS and chloride act as trailing and leading ions, respectively, in this system, while histidine operates as counterion and buffers all solutions. In these electrophoretic conditions, peptides with pI below 5.5 migrate with low overall electrophoretic mobilities but high differences from one another, which allows for their efficient resolution. In silico analysis of several proteomes shows that the acid pH system allows a peptide simplification of 2.5-fold with respect to the total peptide mixture, and still a proteome coverage of about 95% is achievable. A straightforward method with a protocol including proteomic studies was achieved for SDS-PAGE of proteins, enzyme treatment and further peptide fractionation by SDS-free acid PAGE.

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Edman degradation in the gas phase has been observed by collision activated dissociation of N-terminal phenylthiocarbamoyl (PTC) protonated peptide to yield abundant complementary b1 and y(n-1) ion pairs. Here, we demonstrated the relation between the observed losses of aniline and/or the entire PTC derivatizing group with the availability of mobile protons using electrospray ionization mass spectrometry. In order to select the peptides with more efficient fragmentation, while simplifying the mixture of peptides, we extend the phenylisotiocyanate (PITC) derivatization of amino groups to the selective isolation of multiply charged peptides (those having the number of arginines and histidines residues higher than one) using a procedure previously developed in our group. Thus, it was possible to identify in the filtered protein database the sequence of the isolated multiply charged peptides derived from a single protein and a complex mixture of proteins extracted from Escherichia coli using only the molecular mass and the N-terminal amino acid information. For this purpose, we developed a novel bioinformatic tool for automatic identification of peptides from liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments, which potentially can be used in high-throughput proteomics.

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Here we demonstrate the usefulness of peptide fractionation by SDS-free polyacrylamide gel electrophoresis and its applicability to proteomics studies. In the absence of SDS, the driving force for the electrophoretic migration toward the anode is supplied by negatively charged acidic amino acid residues and other residues as phosphate, sulfate and sialic acid, while the resulting mobility depends on both the charge and the molecular mass of the peptides. A straightforward method was achieved for SDS-PAGE of proteins, enzyme digestion, peptide transfer and fractionation by SDS-free PAGE, which was named dual-fractionation polyacrylamide gel electrophoresis (DF-PAGE). This method increases the number of identified proteins 2.5-fold with respect to the proteins identified after direct analysis, and more than 80% of assigned peptides were found in unique SDS-free gel slices. A vast majority of identified peptides (93%) have p I values below 7.0, and 7% have p I values between 7.0 and 7.35. Peptide digests that were derived from complex protein mixtures were in consequence simplified as peptides that are positively charged are not recovered in the present conditions. The analysis of a membrane protein extract from Neisseria meningitidis by this approach allowed the identification of 97 proteins, including low-abundance components.

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