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.

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.

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.

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.

A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers.

Biomarkers have become an essential component of Alzheimer disease (AD) research and because of the pervasiveness of AD pathology in the elderly, the same biomarkers are used in cognitive aging research. A number of current issues suggest that an unbiased descriptive classification scheme for these biomarkers would be useful. We propose the "A/T/N" system in which 7 major AD biomarkers are divided into 3 binary categories based on the nature of the pathophysiology that each measures. "A" refers to the value of a ß-amyloid biomarker (amyloid PET or CSF Aß42); "T," the value of a tau biomarker (CSF phospho tau, or tau PET); and "N," biomarkers of neurodegeneration or neuronal injury ([(18)F]-fluorodeoxyglucose-PET, structural MRI, or CSF total tau). Each biomarker category is rated as positive or negative. An individual score might appear as A+/T+/N-, or A+/T-/N-, etc. The A/T/N system includes the new modality tau PET. It is agnostic to the temporal ordering of mechanisms underlying AD pathogenesis. It includes all individuals in any population regardless of the mix of biomarker findings and therefore is suited to population studies of cognitive aging. It does not specify disease labels and thus is not a diagnostic classification system. It is a descriptive system for categorizing multidomain biomarker findings at the individual person level in a format that is easy to understand and use. Given the present lack of consensus among AD specialists on terminology across the clinically normal to dementia spectrum, a biomarker classification scheme will have broadest acceptance if it is independent from any one clinically defined diagnostic scheme.

Proteomic peptide phage display uncovers novel interactions of the PDZ1-2 supramodule of syntenin.

Syntenin has crucial roles in cell adhesion, cell migration and synaptic transmission. Its closely linked postsynaptic density-95, discs large 1, zonula occludens-1 (PDZ) domains typically interact with C-terminal ligands. We profile syntenin PDZ1-2 through proteomic peptide phage display (ProP-PD) using a library that displays C-terminal regions of the human proteome. The protein recognizes a broad range of peptides, with a preference for hydrophobic motifs and has a tendency to recognize cryptic internal ligands. We validate the interaction with nectin-1 through orthogonal assays. The study demonstrates the power of ProP-PD as a complementary approach to uncover interactions of potential biological relevance.

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.

Development of novel neurokinin 3 receptor (NK3R) selective agonists with resistance to proteolytic degradation.

Neurokinin B (NKB) regulates the release of gonadotropin-releasing hormone (GnRH) via activation of the neurokinin-3 receptor (NK3R). We evaluated the biological stability of NK3R selective agonists to develop novel NK3R agonists to regulate reproductive functions. On the basis of degradation profiles, several peptidomimetic derivatives were designed. The modification of senktide with (E)-alkene dipeptide isostere generated a novel potent NK3R agonist with high stability and prolonged bioactivity.

Origins of the reassortant 2009 pandemic influenza virus through proteotyping with mass spectrometry.

The application of a proteotyping approach employing high resolution mass spectrometry based is shown to be able to determine the gene origin of all major viral proteins in a triple reassortant pandemic 2009 influenza strain. Key to this approach is the identification of unique swine-host-specific signature and indicator peptides that are characteristic of influenza viruses circulating in North American and Eurasian swine herds in the years prior to the 2009 influenza pandemic. These swine-and human pandemic-specific signatures enable the origins of viral proteins in a clinical virus specimen to be determined and such strains to be rapidly and directly differentiated from other co-circulating seasonal influenza viruses from the same period. The proteotyping strategy offers advantages over traditional RT-PCR-based approaches that are currently the mainstay of influenza surveillance at the molecular level.

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.

Through the eye of an electrospray needle: mass spectrometric identification of the major peptides and proteins in the milk of the one-humped camel (Camelus dromedarius).

The milk of the one-humped camel (Camelus dromedarius) reportedly offers medicinal benefits, perhaps because of its unique bioactive components. Milk proteins were determined by (1) two-dimensional gel electrophoresis and peptide mass mapping and (2) liquid chromatography-tandem mass spectrometry (LC-MS/MS) following one-dimensional polyacrylamide gel electrophoresis. Over 200 proteins were identified: some known camel proteins including heavy-chain immunoglobulins and others exhibiting regions of exact homology with proteins from other species. Indigenous peptides were also identified following isolation and concentration by two strategies: (1) gel-eluted liquid fraction entrapment electrophoresis and (2) small-scale electrophoretic separation. Extracts were analyzed by LC-MS/MS and peptides identified by matching strategies, by de novo sequencing and by applying a sequence tag tool requiring similarity to the proposed sequence, but not an exact match. A plethora of protein cleavage products including some novel peptides were characterized. These studies demonstrate that camel milk is a rich source of peptides, some of which may serve as nutraceuticals.

Prevalence of transmitted HIV drug resistance in Iran between 2010 and 2011.

OBJECTIVE: Drug-resistant (DR) HIV emerges during combined antiretroviral treatment (cART), creating concern about widespread transmission of DR-HIV as cART is expanded in resource-limited countries. The aim of this study was to determine the predominant HIV-1 subtypes and prevalence of transmitted DR mutations among antiretroviral-naïve patients in Iran. DESIGN: To monitor transmission of DR HIV, a threshold surveillance based on the world health organization (WHO) guidelines was implemented in Iran. METHODS: For this HIVDR threshold surveillance study, blood samples were collected from 50 antiretroviral-naïve HIV-1-infected patients. Antiretroviral-resistant mutations were determined by sequencing HIV-1 protease, reverse transcriptase and integrase regions. The HIV-1 subtype was determined by sequencing the p17 and C2-V5 regions of the gag and env genes, respectively. RESULTS: Phylogenetic analyses of the sequenced regions revealed that 45 (95.7%) of 47 samples that were successfully obtained were CRF35_AD. The remaining two cases were subtype B (2.1%) and CRF01_AE (2.1%). Consistent results were obtained also from Env and Gag sequences. Regarding prevalence of transmitted DR viruses, two cases were found to harbor reverse transcriptase-inhibitor-resistant mutations (4.3%). In addition, although not in the WHO list for surveillance of transmitted mutations, 13 minor protease-inhibitor-resistant mutations listed in the International AIDS Society-USA panel of drug resistance mutations were found. No DR mutations were detected in the integrase region. CONCLUSIONS: Our study clarified that CRF35_AD is the major subtype among HIV-1-infected patients in Iran. According to the WHO categorization method of HIVDR threshold survey, the prevalence of transmitted drug resistant HIV in Iran was estimated as moderate (5-15%).

Mass spectrometric distinction of in-source and in-solution pyroglutamate and succinimide in proteins: a case study on rhG-CSF.

Formation of cyclic intermediates involving water or ammonia loss is a common occurrence in any reaction involving terminal amines or hydroxyl group containing species. Proteins that have both these functional groups in abundance are no exception, and presence of amino acids such as asparagine, glutamines, aspartic acids, and glutamic acids aid in formation of such intermediates. In the biopharma scenario, such intermediates lead to product- or process-related impurities that might be immunogenic. Mass spectroscopy is a powerful technique that is used to decipher the presence and physicochemical characteristics of such impurities. However, such intermediates can also form in situ during mass spectrometric analysis. We present here the detection of in-source and in-solution formation of succinimide and pyroglutamate in the protein granulocyte colony stimulating factor. We also propose an approach for quick differentiation of such in-situ species from the tangible impurities. We believe that this will not only reduce the time spent in unambiguous identification of succinimide- and/or pyroglutamate-related impurity in bio-pharmaceutics but also provide a platform for similar studies on other impurities that may form due to stabilized intermediates.

Antimicrobial peptide elicitors: new hope for the post-antibiotic era.

Antimicrobial peptides or host defense peptides are fundamental components of human innate immunity. Recent and growing evidence suggests they have a role in a broad range of diseases, including cancer, allergies and susceptibility to infection, including HIV/AIDS. Antimicrobial peptide elicitors (APEs) are physical, biological or chemical agents that boost human antimicrobial peptide expression. The current knowledge of APEs and their potential use in the treatment of human infectious diseases are reviewed, and a classification system for APEs is proposed. The efficient use of APEs in clinical practice could mark the beginning of the urgently needed post-antibiotic era, but further trials assessing their efficacy and safety are required.

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.

Automated analysis of mouse serum peptidome using restricted access media and nanoliquid chromatography-tandem mass spectrometry.

We demonstrate use of restricted access media with reversed phase functionality (RAM-RP) for analysis of low molecular weight proteins and peptides in mouse serum (75 µl) using a custom designed modular automated processing system (MAPS). RAM-RP fractionation with simultaneous removal of high molecular weight and high abundance proteins is integrated with a follow-on buffer exchange module (BE) to ensure compatibility with subsequent processing steps (trypsin digestion and intact peptide separation prior to mass spectrometric analysis). The high sample capacity afforded by chromatographic methods generates enough sample to achieve comprehensive serum peptidome identification (357 proteins) through tandem mass spectrometric analysis of both intact and digested peptides. Sample losses during transfer between modules are minimized through precise fluidic control; no clogging occurred over several months of serum processing in our low back pressure system. Computer controlled operation of both modules and thorough optimization yield excellent run-to-run reproducibility and protein/peptide overlap in analytical repeats. The robustness of our results demonstrate that the RAM-RP-BE workflow executed on our MAPS platform shows tremendous potential for high throughput peptidome processing, particularly with regard to direct analysis of small-volume serum samples.