Dipeptidyl Peptidase-IV Inhibitory Activity and Related Molecular Mechanism of Bovine α-Lactalbumin-Derived Peptides.

Identifying DPP-IV inhibitory peptides from dietary protein has attracted increased attention. In the present study, bovine α-lactalbumin hydrolysates were generated by alcalase for various hydrolysis times, and DPP-IV inhibitory activity of these hydrolysates was determined. The 4 h hydrolysates displayed the most potent DPP-IV inhibitory activity, with DPP-IV inhibition rate of 82.30 ± 1.39% at concentration of 1.0 mg/mL. DPP-IV inhibitory peptides were isolated from the 4 h-hydrolysates with gel filtration chromatography and reversed-phase high-performance liquid chromatography (RP-HPLC). Using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI MS/MS), two DPP-IV inhibitory peptides were identified, and their amino acid sequences were Glu-Leu-Lys-Asp-Leu-Lys-Gly-Tyr (ELKDLKGY) and Ile-Leu-Asp-Lys-Val-Gly-Ile-Asn-Tyr (ILDKVGINY), respectively. Furthermore, molecular docking analysis showed that peptides ELKDLKGY and ILDKVGINY could form hydrogen bonds, pi-cation interactions, and salt bridges with DPP-IV. These findings indicated that bovine α-lactalbumin may be a potential source of natural DPP-IV inhibitor.

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models.

Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.

Fast and Automated Characterization of Antibody Variants with 4D HPLC/MS.

Characterization of unknown monoclonal antibody (mAb) variants is important in order to identify their potential impact on safety, potency, and stability. Ion exchange chromatography (IEC) coupled with UV detection is frequently used to separate and quantify mAb variants in routine quality control (QC). However, characterization of the chromatographic peaks resulting from an IEC separation is an extremely time-consuming process, involving many cumbersome steps. Presented here is an online four-dimensional high performance liquid chromatography-mass spectrometry (4D HPLC/MS) approach, developed to circumvent these limitations. To achieve this, a 2D HPLC system was extended through the introduction of additional modules, hence enabling fully automated bioseparation of mAbs, fractionation of peaks, reduction, tryptic digestion, and reversed-phase (RP) separation of resulting peptides followed by MS detection. The entire separation and analytical process for an unknown peak is performed in less than 1.5 h, leading to a significant time savings, with comparable sequence coverage. To show the comparability with the traditional offline process, a proof of concept study with a previously characterized mAb1 is presented in this paper.

Bioinformatic prediction of Trichoplax adhaerens regulatory peptides.

Trichoplax adhaerens (phylum Placozoa) is a very simple organism that lacks a nervous system. However, its genome contains many genes essential for neuronal function and development. I report the results of regulatory peptide predictions for this enigmatic animal. Extensive transcriptome, genome, and predicted proteome mining allowed us to predict four insulins, at least five short peptide precursors, one granulin, one paracrine regulator of cell growth, and one complex temptin-attractin pheromone signaling system. The expression of three insulins, four short peptide precursors, granulin, and one out of the six temptin genes was detected. Five predicted regulatory peptide precursors could potentially release over 60 different mature peptides. Some of the predicted peptides are somewhat similar to anthozoan RW amides, Aplysia pedal peptide 3, and PRQFV amide. Other predicted short peptides could not readily be classified into established families. These data provide the foundation for the molecular, biochemical, physiological, and behavioral studies of one the most primitive animal coordination systems, and give unique insight into the origins and early evolution of the nervous system.

On the amyloid datasets used for training PAFIG--how (not) to extend the experimental dataset of hexapeptides.

BACKGROUND: Amyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer's or Parkinson's diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification. RESULTS: The amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo-positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides. CONCLUSIONS: Our study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.

Comparison of in-gel protein separation techniques commonly used for fractionation in mass spectrometry-based proteomic profiling.

Fractionation of complex samples at the cellular, subcellular, protein, or peptide level is an indispensable strategy to improve the sensitivity in mass spectrometry-based proteomic profiling. This study revisits, evaluates, and compares the most common gel-based protein separation techniques i.e. 1D SDS-PAGE, 1D preparative SDS-PAGE, IEF-IPG, and 2D-PAGE in their performance as fractionation approaches in nano LC-ESI-MS/MS analysis of a mixture of protein standards and mitochondrial extracts isolated from rat liver. This work demonstrates that all the above techniques provide complementary protein identification results, but 1D SDS-PAGE and IEF-IPG had the highest number of identifications. The IEF-IPG technique resulted in the highest average number of detected peptides per protein. The 2D-PAGE was evaluated as a protein fractionation approach. This work shows that the recovery of proteins and resulting proteolytic digests is highly dependent on the total volume of the gel matrix. The performed comparison of the fractionation techniques demonstrates the potential of a combination of orthogonal 1D SDS-PAGE and IEF-IPG for the improved sensitivity of profiling without significant decrease in throughput.

Validated hemoglobin-depletion approach for red blood cell lysate proteome analysis by means of 2D PAGE and Orbitrap MS.

The analysis of the cytosolic red blood cell (RBC) proteome is negatively affected by the high intracellular amount of hemoglobin complicating the detection of low-abundant cytosolic proteins. In this study, an alternative approach for the preparation of hemoglobin-depleted RBC lysates is presented, which was established in combination with downstream 2D PAGE analysis and Orbitrap MS. Hemoglobin removal was accomplished by using HemoVoid(TM) depletion reagent, which enabled a very efficient enrichment of low-abundant proteins by simultaneously reducing the hemoglobin concentration of the sample. After defining selected sample preparation protocol characteristics including specificity/selectivity, precision and linearity, a 2D reference map (pH 4-7) of the cytosolic RBC proteome was generated and a total of 189 different proteins were identified. Thus, the presented approach proved to be highly suitable to prepare reproducible high-resolution 2D protein maps of the RBC cytosol and provides a helpful tool for future studies investigating disease- or storage-induced changes of the cytosolic RBC proteome.

Homogeneous and heterogeneous tertiary structure ensembles of amyloid-ß peptides.

The interplay of modern molecular simulation and high-quality nuclear magnetic resonance (NMR) experiments has reached a fruitful stage for quantitative characterization of structural ensembles of disordered peptides. Amyloid-ß 1-42 (Aß42), the primary peptide associated with Alzheimer's disease, and fragments such as Aß21-30 are both classified as intrinsically disordered peptides (IDPs). We use a variety of NMR observables to validate de novo molecular dynamics simulations in explicit water to characterize the tertiary structure ensemble of Aß42 and Aß21-30 from the perspective of their classification as IDPs. Unlike the Aß21-30 fragment that conforms to expectations of an IDP that is primarily extended, we find that Aß42 samples conformations reflecting all possible secondary structure categories and spans the range of IDP classifications from collapsed structured states to highly extended conformations, making it an IDP with a far more heterogeneous tertiary ensemble.

Reduction of protein concentration range difference followed by multicolumn fractionation prior to 2-DE and LC-MS/MS profiling of serum proteins.

This article is concerned with the reduction of protein concentration range differences by the peptide beads library technology (ProteoMiner™ or "equalizer" technology), which in principle allows the enrichment of proteins to the same concentration level (i.e. protein equalizer) regardless of the original protein abundance in a given biological fluid such as human serum, which is the subject of our investigation. After the equalization step, the captured proteins from human serum were fractionated on a series of tandem monolithic columns with surface-bound iminodiacetic acid ligands to which three different metal ions, namely, Zn²+, Ni²+ and Cu²+ were immobilized to yield the so-called immobilized metal affinity chromatography columns. These three monolithic columns were connected to a reversed-phase column packed with polystyrene divinyl benzene beads. Aliquots taken from the four collected fractions from the four tandem columns were subsequently fractionated by 2-DE. Also, aliquots from the four collected fractions were tryptically digested and analyzed by LC-MS/MS. The strategy of subsequent fractionation on the four tandem columns after equalization allowed the identification of more proteins than simply using the equalization by ProteoMiner™ . The equalizer technology was compared to the immuno-subtraction approach. While the ProteoMiner™ technology is superior in terms of the overall number of captured proteins, it only complements the immuno-subtraction approach since the latter can capture the proteins that were not captured by the former.

Laserspray ionization-ion mobility spectrometry-mass spectrometry: baseline separation of isomeric amyloids without the use of solvents desorbed and Ionized directly from a surface.

The ability of laserspray ionization (LSI) to produce multiply charged ions by laser ablation from the solid state, directly from a surface, and at atmospheric pressure allows protein analysis on an ion mobility spectrometry (IMS)-mass spectrometry (MS) instrument (SYNAPT G2) having a mass-to-charge limit of 8000. The matrix, 2,5-dihydroxyacetophenone, lowers the thermal requirements for desolvation of matrix/analyte clusters to produce the highly charged LSI ions under gentle conditions to retain structural integrity of the proteins. Examples include cytochrome C and lysozyme. The solvent-free IMS gas-phase separation is used to baseline separate in the drift time dimension the isomeric solubility restricted ß-amyloid (1-42) from the reversed (42-1). The LSI process is shown to be sufficiently soft to preserve structural integrity and permit separation according to the different shapes. These results suggest that LSI-IMS-MS potentially combines speed of analysis and imaging capability common to matrix-assisted laser desorption/ionization and multiple charging with the potential for structural analysis common to electrospray ionization.

Mass spectrometric analyses of peptides and proteins in human gingival crevicular fluid.

Gingival crevicular fluid (GCF) is a pathophysiological fluid that flows into the oral cavity. Human GCF was collected using sterile glass microcapillary tubes from inflamed periodontal sites in patients who had a history of periodontal disease and were in the maintenance phase of treatment. Samples from individual sites were analyzed using MS techniques both before and following HPLC. GCF samples were also pooled and subjected to SDS-PAGE, in-gel digestion and MS analyses using both MALDI-TOF/TOF MS and nanoLC-ESI-MS/MS. MS spectra were used to search human protein sequence databases for protein identification. With these approaches, 33 peptides and 66 proteins were positively identified in human GCF. All of the peptides discovered in this study are reported in GCF here for the first time. Forty-three of the identified proteins, such as actin and the actin binding proteins profilin, cofilin and gelsolin, have not been reported in GCF before.

A proteomic-based approach for detection of chicken in meat mixes.

A proteomic-based method has been developed for the detection of chicken meat within mixed meat preparations. The procedure is robust and simple, comprising the extraction of myofibrillar proteins, enrichment of target proteins using OFFGEL isoelectric focusing, in-solution trypsin digestion of myosin light chain 3, and analysis of the generated peptides by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Using this approach, it was possible for example to detect 0.5% contaminating chicken in pork meat with high confidence. Quantitative detection of chicken meat was done by using AQUA stable isotope peptides made from the sequence of previously selected species-specific peptide biomarkers. Linearity was observed between the amount of the peptide biomarker and the amount of chicken present in the mixture; further independent replication is required now to validate the method. Apart from its simplicity, this approach has the advantage that it can be used effectively for the detection of both raw and cooked meat. The method is robust, reliable, and sensitive, representing a serious alternative to methods currently in use for these purposes. It is amenable to highly processed foods which can be particularly problematic, as the tertiary protein structure is often affected in processed food precluding immunoassays. In addition, this proteomic analysis will permit the determination of definitive discriminatory sequence, unlike the DNA PCR based methods used presently. The present article also demonstrates the translation of the technology to routine mass spectrometry equipment, making the methodology suitable for public analysts.

Comprehensive analysis of low-abundance proteins in human urinary exosomes using peptide ligand library technology, peptide OFFGEL fractionation and nanoHPLC-chip-MS/MS.

Human urinary exosomes are 30-100 nm vesicles that originate as the internal vesicles in multivesicular bodies from every renal epithelial cell type facing the urinary track and may serve as a suitable noninvasive starting material for biomarker discovery relevant to a variety of renal disease. To comprehensively explore the low-abundance proteome, combinatorial peptide ligand libraries, combined with peptide OFFGEL electrophoresis were employed for the enrichment and separation of relatively low-abundant proteins in urinary exosomes. After analysis by nanoHPLC-chip-MS/MS, 512 proteins were identified, including a large number of proteins with extreme molecular weight or extreme pI value, which could not be well mapped by using traditional 2-D-gel-based separation methods. This in-depth analysis of low-abundant proteins in urinary exosomes led to an increased understanding of molecular composition of these little vesicles and may be helpful for the discovery of novel biomarker. Our work also provides an effective strategy of concentration and identification of low-abundance proteome from complex bio-samples.

Secreted proteome of the murine multipotent hematopoietic progenitor cell line DKmix.

Administration of the multipotent hematopoietic progenitor cell (HPC) line DKmix improved cardiac function after myocardial infarction and accelerated dermal wound healing due to paracrine mechanisms. The aim of this study was to analyse the secreted proteins of DKmix cells in order to identify the responsible paracrine factors and assess their relevance to the wide spectrum of therapeutic effects. A mass spectrometry (MS)-based approach was used to identify secreted proteins of DKmix cells. Serum free culture supernatants of DKmix-conditioned medium were collected and the proteins present were separated, digested by trypsin and the resulting peptides were then analyzed by matrix-assisted laser desorption/ionization tandem time-of-flight (MALDI-TOF/TOF) MS. Overall 95 different proteins were identified. Among them, secretory proteins galectin-3 and gelsolin were identified. These proteins are known to stimulate cell migration and influence wound healing and cardiac remodelling. The remaining proteins originate from intracellular compartments like cytoplasm (69%), nucleus (12%), mitochondria (4%), and cytoplasmic membrane (3%) indicating permeable or leaky DKmix cells in the conditioned medium. Additionally, a sandwich immunoassay was used to detect and quantify cytokines and chemokines. Interleukin-6 (IL-6), interleukin-13 (IL-13), monocyte-chemoattractant protein-1 (MCP-1), monocyte-chemoattractant protein-3 (MCP-3), monocyte-chemoattractant protein-1alpha (MIP-1alpha) and monocyte-chemoattractant protein-1beta (MIP-1beta) were detected in low concentrations. This study identified a subset of proteins present in the DKmix-conditioned medium that act as paracrine modulators of tissue repair. Moreover, it suggests that DKmix-derived conditioned medium might have therapeutic potency by promoting tissue regeneration.

De novo prediction of the elemental composition of peptides and proteins based on a single mass.

Identification of peptides and proteins is a common task in mass spectrometry-based proteomics but often fails to deliver a comprehensive list of identifications. Downstream analysis, quantitative or qualitative, depends on the outcome of this process. Despite continuous improvement of computational methods, a large fraction of the screened peptides and/or proteins remains unidentified. We introduce here pacMASS, a method that de novo predicts the elemental composition of peptides and small proteins based on a single accurate mass, ie, the observed monoisotopic or average mass. This novel approach returns in a fast and memory efficient manner a limited number of elemental compositions per queried peptide or protein.

Novel alpha D-conopeptides and their precursors identified by cDNA cloning define the D-conotoxin superfamily.

AlphaD-conotoxins are peptide inhibitors of nicotinic acetylcholine receptors (nAChRs) first described from Conus vexillum (alphaD-VxXIIA-C and renamed here to alphaD-VxXXA, alphaD-VxXXB, and alphaD-VxXXC). In this study, we report cDNA sequences encoding D-superfamily conopeptides identified in the Clade XII Conidae Conus vexillum, Conus capitaneus, Conus mustelinus, and Conus miles, together with partial sequences of corresponding peptides from this family. The D-superfamily signal peptide sequences display greater heterogeneity than reported for other conotoxin superfamilies. Phylogenetic analysis of the relationships among alphaD-conotoxin precursors reveals two distinct groups containing either an EMM or AVV signal peptide sequence motif. Homodimer and heterodimer combinations of predicted mature toxin sequences likely account for the partial amino acid sequences and mass values observed for several of the native dimeric peptide components identified in C. capitaneus, C. miles, and C. mustelinus venom. The discovery of the precursors and several novel conotoxins from different species defines this large conotoxin family and expands our understanding of sequence diversification mechanisms in Conus species.

Inclusion bodies: specificity in their aggregation process and amyloid-like structure.

The accumulation of aggregated protein in the cell is associated with the pathology of many diseases and constitutes a major concern in protein production. Intracellular aggregates have been traditionally regarded as nonspecific associations of misfolded polypeptides. This view is challenged by studies demonstrating that, in vitro, aggregation often involves specific interactions. However, little is known about the specificity of in vivo protein deposition. Here, we investigate the degree of in vivo co-aggregation between two self-aggregating proteins, Abeta42 amyloid peptide and foot-and-mouth disease virus VP1 capsid protein, in prokaryotic cells. In addition, the ultrastructure of intracellular aggregates is explored to decipher whether amyloid fibrils and intracellular protein inclusions share structural properties. The data indicate that in vivo protein aggregation exhibits a remarkable specificity that depends on the establishment of selective interactions and results in the formation of oligomeric and fibrillar structures displaying amyloid-like properties. These features allow prokaryotic Abeta42 intracellular aggregates to act as effective seeds in the formation of Abeta42 amyloid fibrils. Overall, our results suggest that conserved mechanisms underlie protein aggregation in different organisms. They also have important implications for biotechnological and biomedical applications of recombinant polypeptides.

Seeding induced by alpha-synuclein oligomers provides evidence for spreading of alpha-synuclein pathology.

Lewy bodies, alpha-synuclein (alpha-syn) immunopositive intracellular deposits, are the pathological hallmark of Parkinson's disease (PD). Interestingly, Lewybody-like structures have been identified in fetal tissue grafts about one decade after transplantation into the striatum of PD patients. One possible explanation for the accelerated deposition of alpha-syn in the graft is that the aggregation of alpha-syn from the host tissue to the graft is spread by a prion disease-like mechanism. We discuss here an in vitro model which might recapitulate some aspects of disease propagation in PD. We found here that in vitro-generated alpha-syn oligomers induce transmembrane seeding of alpha-syn aggregation in a dose- and time-dependent manner. This effect was observed in primary neuronal cultures as well as in neuronal cell lines. The seeding oligomers were characterized by a distinctive lithium dodecyl sulfate-stable oligomer pattern and could be generated in a dynamic process out of pore-forming oligomers. We propose that alpha-syn oligomers form as a dynamic mixture of oligomer types with different properties and that alpha-syn oligomers can be converted into different types depending on the brain milieu conditions. Our data indicate that extracellular alpha-syn oligomers can induce intracellular alpha-syn aggregation, therefore we hypothesize that a similar mechanism might lead to alpha-syn pathology propagation.

NeuRiPP: Neural network identification of RiPP precursor peptides.

Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. Current machine learning tools have limitations, since they are specific to the RiPPclass they are trained for and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network archictectures that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP was able to identify PP sequences in significantly more putative RiPP clusters than current tools while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that were recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools.

MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database.

MOTIVATION: The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset. RESULTS: The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4. AVAILABILITY: http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.