NeuroPedia: neuropeptide database and spectral library

Penultimate proline in neuropeptides

A recent ion mobility spectrometry-mass spectrometry (IMS-MS) study revealed that tryptic peptide ions containing a proline residue at the second position from the N-terminus (i.e., penultimate proline) frequently adopt multiple conformations, owing to the cis-trans isomerization of Xaa(1)-Pro(2) peptide bonds [J. Am. Soc. Mass Spectrom. 2015, 26, 444]. Here, we present a statistical analysis of a neuropeptide database that illustrates penultimate proline residues are frequently found in neuropeptides. In order to probe the effect of penultimate proline on neuropeptide conformations, IMS-MS experiments were performed on two model peptides in which penultimate proline residues were known to be important for biological activity: the N-terminal region of human neuropeptide Y (NPY1-9, Tyr(1)-Pro(2)-Ser(3)-Lys(4)-Pro(5)-Asp(6)-Asn(7)-Pro(8)-Gly(9)-NH2) and a tachykinin-related peptide (CabTRP Ia, Ala(1)-Pro(2)-Ser(3)-Gly(4)-Phe(5)-Leu(6)-Gly(7)-Met(8)-Arg(9)-NH2). From these studies, it appears that penultimate prolines allow neuropeptides to populate multiple conformations arising from the cis-trans isomerization of Xaa(1)-Pro(2) peptide bonds. Although it is commonly proposed that the role of penultimate proline residues is to protect peptides from enzymatic degradation, the present results indicate that penultimate proline residues also are an important means of increasing the conformational heterogeneity of neuropeptides.

Advances in Mass Spectrometric Tools for Probing Neuropeptides

Neuropeptides are important mediators in the functionality of the brain and other neurological organs. Because neuropeptides exist in a wide range of concentrations, appropriate characterization methods are needed to provide dynamic, chemical, and spatial information. Mass spectrometry and compatible tools have been a popular choice in analyzing neuropeptides. There have been several advances and challenges, both of which are the focus of this review. Discussions range from sample collection to bioinformatic tools, although avenues such as quantitation and imaging are included. Further development of the presented methods for neuropeptidomic mass spectrometric analysis is inevitable, which will lead to a further understanding of the complex interplay of neuropeptides and other signaling molecules in the nervous system.

NeuroPep: a comprehensive resource of neuropeptides

Neuropeptides play a variety of roles in many physiological processes and serve as potential therapeutic targets for the treatment of some nervous-system disorders. In recent years, there has been a tremendous increase in the number of identified neuropeptides. Therefore, we have developed NeuroPep, a comprehensive resource of neuropeptides, which holds 5949 non-redundant neuropeptide entries originating from 493 organisms belonging to 65 neuropeptide families. In NeuroPep, the number of neuropeptides in invertebrates and vertebrates is 3455 and 2406, respectively. It is currently the most complete neuropeptide database. We extracted entries deposited in UniProt, the database (www.neuropeptides.nl) and NeuroPedia, and used text mining methods to retrieve entries from the MEDLINE abstracts and full text articles. All the entries in NeuroPep have been manually checked. 2069 of the 5949 (35%) neuropeptide sequences were collected from the scientific literature. Moreover, NeuroPep contains detailed annotations for each entry, including source organisms, tissue specificity, families, names, post-translational modifications, 3D structures (if available) and literature references. Information derived from these peptide sequences such as amino acid compositions, isoelectric points, molecular weight and other physicochemical properties of peptides are also provided. A quick search feature allows users to search the database with keywords such as sequence, name, family, etc., and an advanced search page helps users to combine queries with logical operators like AND/OR. In addition, user-friendly web tools like browsing, sequence alignment and mapping are also integrated into the NeuroPep database. Database URL: http://isyslab.info/NeuroPep

Drug target mining and analysis of the Chinese tree shrew for pharmacological testing

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research.

NeuroPID: a classifier of neuropeptide precursors

Neuropeptides (NPs) are short secreted peptides produced in neurons. NPs act by activating signaling cascades governing broad functions such as metabolism, sensation and behavior throughout the animal kingdom. NPs are the products of multistep processing of longer proteins, the NP precursors (NPPs). We present NeuroPID (Neuropeptide Precursor Identifier), an online machine-learning tool that identifies metazoan NPPs. NeuroPID was trained on 1418 NPPs annotated as such by UniProtKB. A large number of sequence-based features were extracted for each sequence with the goal of capturing the biophysical and informational-statistical properties that distinguish NPPs from other proteins. Training several machine-learning models, including support vector machines and ensemble decision trees, led to high accuracy (89–94%) and precision (90–93%) in cross-validation tests. For inputs of thousands of unseen sequences, the tool provides a ranked list of high quality predictions based on the results of four machine-learning classifiers. The output reveals many uncharacterized NPPs and secreted cell modulators that are rich in potential cleavage sites. NeuroPID is a discovery and a prediction tool that can be used to identify NPPs from unannotated transcriptomes and mass spectrometry experiments. NeuroPID predicted sequences are attractive targets for investigating behavior, physiology and cell modulation. The NeuroPID web tool is available at http:// neuropid.cs.huji.ac.il.

NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes

MOTIVATION

The evolution of multicellular organisms is associated with increasing variability of molecules governing behavioral and physiological states. This is often achieved by neuropeptides (NPs) that are produced in neurons from a longer protein, named neuropeptide precursor (NPP). The maturation of NPs occurs through a sequence of proteolytic cleavages. The difficulty in identifying NPPs is a consequence of their diversity and the lack of applicable sequence similarity among the short functionally related NPs.

RESULTS

Herein, we describe Neuropeptide Precursor Identifier (NeuroPID), a machine learning scheme that predicts metazoan NPPs. NeuroPID was trained on hundreds of identified NPPs from the UniProtKB database. Some 600 features were extracted from the primary sequences and processed using support vector machines (SVM) and ensemble decision tree classifiers. These features combined biophysical, chemical and informational-statistical properties of NPs and NPPs. Other features were guided by the defining characteristics of the dibasic cleavage sites motif. NeuroPID reached 89-94% accuracy and 90-93% precision in cross-validation blind tests against known NPPs (with an emphasis on Chordata and Arthropoda). NeuroPID also identified NPP-like proteins from extensively studied model organisms as well as from poorly annotated proteomes. We then focused on the most significant sets of features that contribute to the success of the classifiers. We propose that NPPs are attractive targets for investigating and modulating behavior, metabolism and homeostasis and that a rich repertoire of NPs remains to be identified.

AVAILABILITY

NeuroPID source code is freely available at http://www.protonet.cs.huji.ac.il/neuropid

Recent advances in quantitative neuroproteomics

The field of proteomics is undergoing rapid development in a number of different areas including improvements in mass spectrometric platforms, peptide identification algorithms and bioinformatics. In particular, new and/or improved approaches have established robust methods that not only allow for in-depth and accurate peptide and protein identification and modification, but also allow for sensitive measurement of relative or absolute quantitation. These methods are beginning to be applied to the area of neuroproteomics, but the central nervous system poses many specific challenges in terms of quantitative proteomics, given the large number of different neuronal cell types that are intermixed and that exhibit distinct patterns of gene and protein expression. This review highlights the recent advances that have been made in quantitative neuroproteomics, with a focus on work published over the last five years that applies emerging methods to normal brain function as well as to various neuropsychiatric disorders including schizophrenia and drug addiction as well as of neurodegenerative diseases including Parkinson's disease and Alzheimer's disease. While older methods such as two-dimensional polyacrylamide electrophoresis continued to be used, a variety of more in-depth MS-based approaches including both label (ICAT, iTRAQ, TMT, SILAC, SILAM), label-free (label-free, MRM, SWATH) and absolute quantification methods, are rapidly being applied to neurobiological investigations of normal and diseased brain tissue as well as of cerebrospinal fluid (CSF). While the biological implications of many of these studies remain to be clearly established, that there is a clear need for standardization of experimental design and data analysis, and that the analysis of protein changes in specific neuronal cell types in the central nervous system remains a serious challenge, it appears that the quality and depth of the more recent quantitative proteomics studies is beginning to shed light on a number of aspects of neuroscience that relates to normal brain function as well as of the changes in protein expression and regulation that occurs in neuropsychiatric and neurodegenerative disorders.

The spectral networks paradigm in high throughput mass spectrometry

High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS(2)) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS(2) data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS(2) spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4-6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS(2) spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.

Evaluation of database search programs for accurate detection of neuropeptides in tandem mass spectrometry experiments

Neuropeptide identification in mass spectrometry experiments using database search programs developed for proteins is challenging. Unlike proteins, the detection of the complete sequence using a single spectrum is required to identify neuropeptides or prohormone peptides. This study compared the performance of three open-source programs used to identify proteins, OMSSA, X!Tandem and Crux, to identify prohormone peptides. From a target database of 7850 prohormone peptides, 23550 query spectra were simulated across different scenarios. Crux was the only program that correctly matched all peptides regardless of p-value and at p-value < 1 × 10^–2, 33%, 64%, and >75%, of the 5, 6, and ≥7 amino acid-peptides were detected. Crux also had the best performance in the identification of peptides from chimera spectra and in a variety of missing ion scenarios. OMSSA, X!Tandem and Crux correctly detected 98.9% (99.9%), 93.9% (97.4%) and 88.7% (98.3%) of the peptides at E- or p-value < 1 × 10^–6 (< 1 × 10^–2), respectively. OMSSA and X!Tandem outperformed the other programs in significance level and computational speed, respectively. A consensus approach is not recommended because some prohormone peptides were only identified by one program.

Cathepsin H functions as an aminopeptidase in secretory vesicles for production of enkephalin and galanin peptide neurotransmitters

Peptide neurotransmitters function as key intercellular signaling molecules in the nervous system. These peptides are generated in secretory vesicles from proneuropeptides by proteolytic processing at dibasic residues, followed by removal of N- and/or C-terminal basic residues to form active peptides. Enkephalin biosynthesis from proenkephalin utilizes the cysteine protease cathepsin L and the subtilisin-like prohormone convertase 2 (PC2). Cathepsin L generates peptide intermediates with N-terminal basic residue extensions, which must be removed by an aminopeptidase. In this study, we identified cathepsin H as an aminopeptidase in secretory vesicles that produces (Met)enkephalin (ME) by sequential removal of basic residues from KR-ME and KK-ME, supported by in vivo knockout of the cathepsin H gene. Localization of cathepsin H in secretory vesicles was demonstrated by immunoelectron microscopy and immunofluorescence deconvolution microscopy. Purified human cathepsin H sequentially removes N-terminal basic residues to generate ME, with peptide products characterized by nano-LC-MS/MS tandem mass spectrometry. Cathepsin H shows highest activities for cleaving N-terminal basic residues (Arg and Lys) among amino acid fluorogenic substrates. Notably, knockout of the cathepsin H gene results in reduction of ME in mouse brain. Cathepsin H deficient mice also show a substantial decrease in galanin peptide neurotransmitter levels in brain. These results illustrate a role for cathepsin H as an aminopeptidase for enkephalin and galanin peptide neurotransmitter production.