NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes

Ofer, Dan; Linial, Michal

doi:10.1093/bioinformatics/btt725

Cited by 40 publications

(42 citation statements)

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“…The signal peptide prediction of putative neuropeptides was conducted with SignalP v5.0 [77]. The presence of neuropeptide precursors were detected with the software NeuroPID [78]; the prediction of cleavage sites and neuropeptides were analyzed using the NeuroPred programm [79] and to predict transmembrane helices of neuropeptide receptors we used TMHMM Server v.2.0 [80].…”

Section: Neuropeptide Identi Cationmentioning

confidence: 99%

Description of a Brain Neuropeptidome of the Malaria Vector Nyssorhynchus Albimanus and Neuropeptide Differential Expression During Infection with Plasmodium Berghei

Alvarado-Delgado¹,

Martínez-Barnetche²,

Téllez-Sosa³

et al. 2020

Preprint

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Background Insect neuropeptides, mainly synthesized in the brain, play a central role in the control of many physiological processes. A neuropeptidome of the mosquito Nyssorhynchus albimanus was described based in a comparative analysis of the mosquito genome complemented with high-throughput sequencing of brain transcriptomes. In addition, neuropeptides differentially expressed during Plasmodium infection were identified.Results We identified 3,811 transcripts associated to translation, oxidation-reduction process, protein binding, ATP binding, integral components of membrane and ribosome, among others. We identified 29 neuropeptide transcripts that predicted at least 60 biopeptides, including pyrokinin, glycoprotein hormone alpha (GPA2), prothoracicotropic hormone, neuropeptide-like precursor 1 (NPLP1), allatostatin C, orcokinin, corazonin, adipokinetic hormone I, SIFamide, pyrokinin capa-like, pigment-dispersing factor, adipokinetic hormone/corazonin-related peptide (ACP), tachykinin-related peptide, trissin, neuropeptide F, short neuropeptide F (sNPF), diuretic hormone 31, bursicon, crustacean cardioactive peptide (CCAP), allatotropin, allatostatin 1, partner of bursicon (PBURS), ecdysis triggering hormone (ETH), diuretic hormone 44 (Dh44), insulin-like peptides 5, 1, 3, 7, 2 (ILPs) and eclosion hormone (EH) and seven neuropeptide receptors. Transcript mapping to the Ny. albimanus genome provided evidence for the re-annotation of the myosuppressin gene. A quantitative analysis documented increased expression of adipokinetic hormone/corazonin-related peptide, pyrokinin and corazonin in the mosquito brain after Plasmodium berghei infection.Conclusion This work represents an initial effort to characterize the neuropeptide repertoire of Ny. albimanus and provides new information for understanding neuroregulation of the mosquito response during Plasmodium infection .

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Section: Neuropeptide Identi Cationmentioning

confidence: 99%

Description of a Brain Neuropeptidome of the Malaria Vector Nyssorhynchus Albimanus and Neuropeptide Differential Expression During Infection with Plasmodium Berghei

Alvarado-Delgado¹,

Martínez-Barnetche²,

Téllez-Sosa³

et al. 2020

Preprint

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“…Each sequence is converted into a vector of primary sequence-derived features (∼560 features). The features cover the amino acid composition, bigrams frequencies (400 features) and additional 140 biophysical and statistical characteristics from the sequence (6). The performance of the NeuroPID was assessed using different predictive models.…”

Section: Outline—features To Predictionsmentioning

confidence: 99%

“…Currently, NPPs are sporadically identified from sequencing projects of organisms and from the collections of large-scale proteomics and transcriptomics experiments. We developed a systematic approach for identifying candidate NPPs (6). Large-scale mass spectrometry (MS) proteomics identified thousands of spectra that derived from NPP genes.…”

Section: Introductionmentioning

confidence: 99%

NeuroPID: a classifier of neuropeptide precursors

Karsenty

Rappoport

Ofer

et al. 2014

Nucleic Acids Research

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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.

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“…An initial effort in this direction for extracting features from whole proteins is ProFET (17) which showed success in a broad range of classification tasks. ProFET introduced the use of global and local engineered features for classifying neuropeptides (18), thermophile sequences, structural classes and more. However, different types of features and representations are required for residuelevel annotation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, NeuroPep database (22) includes over 5000 experimentally identified peptides from $500 organisms. Despite this impressive collection, many active peptides remain unidentified due to their small length, altered mass by posttranslational modifications (PTM) and poor sequence conservation (18,23,24).…”

Section: Introductionmentioning

confidence: 99%

ASAP: A Machine-Learning Framework for Local Protein Properties

Ofer

Brandes

Linial

2015

Preprint

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Determining residue-level protein properties, such as sites of post-translational modifications (PTMs), is vital to understanding protein function. Experimental methods are costly and time-consuming, while traditional rule-based computational methods fail to annotate sites lacking substantial similarity. Machine Learning (ML) methods are becoming fundamental in annotating unknown proteins and their heterogeneous properties. We present ASAP (Amino-acid Sequence Annotation Prediction), a universal ML framework for predicting residue-level properties. ASAP extracts numerous features from raw sequences, and supports easy integration of external features such as secondary structure, solvent accessibility, intrinsically disorder or PSSM profiles. Features are then used to train ML classifiers. ASAP can create new classifiers within minutes for a variety of tasks, including PTM prediction (e.g. cleavage sites by convertase, phosphoserine modification). We present a detailed case study for ASAP: CleavePred, an ASAP-based model to predict protein precursor cleavage sites, with state-of-the-art results. Protein cleavage is a PTM shared by a wide variety of proteins sharing minimal sequence similarity. Current rule-based methods suffer from high false positive rates, making them suboptimal. The high performance of CleavePred makes it suitable for analyzing new proteomes at a genomic scale. The tool is attractive to protein design, mass spectrometry search engines and the discovery of new bioactive peptides from precursors. ASAP functions as a baseline approach for residue-level protein sequence prediction. CleavePred is freely accessible as a web-based application. Both ASAP and CleavePred are open-source with a flexible Python API.Database URL: ASAP's and CleavePred source code, webtool and tutorials are available at:

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NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes

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

Cited by 40 publications

References 50 publications

Description of a Brain Neuropeptidome of the Malaria Vector Nyssorhynchus Albimanus and Neuropeptide Differential Expression During Infection with Plasmodium Berghei

Description of a Brain Neuropeptidome of the Malaria Vector Nyssorhynchus Albimanus and Neuropeptide Differential Expression During Infection with Plasmodium Berghei

NeuroPID: a classifier of neuropeptide precursors

ASAP: A Machine-Learning Framework for Local Protein Properties

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