scholarly journals The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines

Proteomes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 13
Author(s):  
Delphine Vincent ◽  
Keith Savin ◽  
Simone Rochfort ◽  
German Spangenberg
Keyword(s):  
Search Engines ◽  
Cannabis Sativa ◽  
Protein Extraction ◽  
Shotgun Proteomics ◽  
Search Algorithms ◽  
Protein Digestion ◽  
Top Down ◽  
Model Species ◽  
Intact Proteins ◽  
Proteomics Experiment

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.

scholarly journals A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds

2019 ◽  
Vol 20 (22) ◽  
pp. 5630 ◽  
Author(s):  
Delphine Vincent ◽  
Vilnis Ezernieks ◽  
Simone Rochfort ◽  
German Spangenberg
Keyword(s):  
Cannabis Sativa ◽  
Protein Extraction ◽  
Shotgun Proteomics ◽  
Reproductive Organs ◽  
Sequence Coverage ◽  
Post Translational Modifications ◽  
Apical Buds ◽  
Medicinal Cannabis ◽  
Close Relatives

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules 2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes 2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules 2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

scholarly journals Optimisation of Protein Extraction from Medicinal Cannabis Mature Buds for Bottom-Up Proteomics

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 659 ◽  
Author(s):  
Delphine Vincent ◽  
Simone Rochfort ◽  
German Spangenberg
Keyword(s):  
Mass Spectrometry ◽  
Cannabis Sativa ◽  
Protein Extraction ◽  
Guanidine Hydrochloride ◽  
Shotgun Proteomics ◽  
Extraction Methods ◽  
Reproductive Organs ◽  
Apical Buds ◽  
Liquid Chromatography Tandem Mass ◽  
Medicinal Cannabis

Medicinal cannabis is used to relieve the symptoms of certain medical conditions, such as epilepsy. Cannabis is a controlled substance and until recently was illegal in many jurisdictions. Consequently, the study of this plant has been restricted. Proteomics studies on Cannabis sativa reported so far have been primarily based on plant organs and tissues other than buds, such as roots, hypocotyl, leaves, hempseeds and flour. As far as we know, no optimisation of protein extraction from cannabis reproductive tissues has been attempted. Therefore, we set out to assess different protein extraction methods followed by mass spectrometry-based proteomics to recover, separate and identify the proteins of the reproductive organs of medicinal cannabis, apical buds and isolated trichomes. Database search following shotgun proteomics was limited to protein sequences from C. sativa and closely related species available from UniprotKB. Our results demonstrate that a buffer containing the chaotrope reagent guanidine hydrochloride recovers many more proteins than a urea-based buffer. In combination with a precipitation with trichloroacetic acid, such buffer proved optimum to identify proteins using a trypsin digestion followed by nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) analyses. This is validated by focusing on enzymes involved in the phytocannabinoid pathway.

scholarly journals Precursor ion independent algorithm for top-down shotgun proteomics

2009 ◽  
Vol 20 (11) ◽  
pp. 2154-2166 ◽  
Author(s):  
Yihsuan S. Tsai ◽  
Alexander Scherl ◽  
Jason L. Shaw ◽  
C. Logan MacKay ◽  
Scott A. Shaffer ◽  
...  
Keyword(s):  
Shotgun Proteomics ◽  
Top Down

scholarly journals Mass Spectrometry Based-Proteomic Analysis of Anisakis spp.: A Preliminary Study towards a New Diagnostic Tool

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 693 ◽  
Author(s):  
Valeria Marzano ◽  
Stefania Pane ◽  
Gianluca Foglietta ◽  
Stefano Levi Mortera ◽  
Pamela Vernocchi ◽  
...  
Keyword(s):  
Diagnostic Tool ◽  
Protein Extraction ◽  
Shotgun Proteomics ◽  
Diagnostic Tools ◽  
Routine Practice ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Proteomics Approach ◽  
Anisakis Spp ◽  
Tof Ms

Anisakiasis is nowadays a well-known infection, mainly caused by the accidental ingestion of Anisakis larvae, following the consumption of raw or undercooked fishes and cephalopods. Due to the similarity of symptoms with those of common gastrointestinal disorders, this infection is often underestimated, and the need for new specific diagnostic tools is becoming crucial. Given the remarkable impact that MALDI–TOF MS biotyping had in the last decade in clinical routine practice for the recognition of bacterial and fungi strains, a similar scenario could be foreseen for the identification of parasites, such as nematodes. In this work, a MALDI–TOF MS profiling of Anisakis proteome was pursued with a view to constructing a first spectral library for the diagnosis of Anisakis infections. At the same time, a shotgun proteomics approach by LC–ESI–MS/MS was performed on the two main fractions obtained from protein extraction, to evaluate the protein species enriched by the protocol. A set of MALDI–TOF MS signals associated with proteins originating in the ribosomal fraction of the nematode extract was selected as a potential diagnostic tool for the identification of Anisakis spp.

scholarly journals Exploring peptide capture by anti-protein antibodies for LC-MS-based biomarker determination

2018 ◽  
Author(s):  
Maren Levernæs ◽  
Bassem Farhat ◽  
Inger Oulie ◽  
Sazan S. Abdullah ◽  
Elisabeth Paus ◽  
...  
Keyword(s):  
Protein Extraction ◽  
High Sensitivity ◽  
Intact Protein ◽  
Protein Biomarkers ◽  
Promising Technique ◽  
Intact Proteins ◽  
Protein Levels ◽  
Endogenous Protein ◽  
Sensitivity And Selectivity

<p>Immunocapture LC-MS/MS is a promising technique to ensure high sensitivity and selectivity of low-abundant protein biomarkers. For this purpose, the use of monoclonal antibodies (mAb) is especially attractive as they are renewable reagents that can be standardized. In this article we investigated the possibility of using mAbs developed against intact proteins (anti-protein antibodies) to capture proteotypic epitope peptides. Three mAbs were tested, and all selectively extracted proteotypic epitope peptides from a complex sample. Compared to intact protein extraction, this concept which we call peptide capture by anti-protein antibodies provided cleaner extracts, which further improved the sensitivity. Analysis of three patient samples demonstrated that p can be used for the determination of different endogenous protein levels. </p><p></p>

scholarly journals PSVII-9 Post transcriptional modifications may lead to changes in newborn goats’ skeletal muscle proteome as a consequence of maternal feed restriction at different stages of gestation

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 167-168
Author(s):  
Thaís Costa ◽  
Tiago Mendes ◽  
Felipe Moura ◽  
Ranyeri Souza ◽  
Marta Fontes ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Protein Extraction ◽  
Muscle Metabolism ◽  
Shotgun Proteomics ◽  
Nucleotide Metabolism ◽  
Feed Restriction ◽  
Label Free ◽  
Citrate Cycle ◽  
Maintenance Requirement ◽  
The Impact

Abstract We aimed to investigate the impact of maternal feed restriction at different stages of gestation on proteomic profile in the skeletal muscle of newborn goats. A total of 14 pregnant dams were randomly divided into one of the follow dietary treatments: Animals fed at 50% of maintenance requirement from 8-84 d of gestation and then fed at 100% maintenance requirement from day 85 of gestation to parturition (RM, n = 6), and animals fed at 100% of maintenance requirement from 8-84 d of gestation and then fed at 50% maintenance requirement from day 85 of gestation to parturition (MR, n = 8). Longissimus muscle was sampled from male newborn goats and submitted to sarcoplasmic protein extraction and liquid chromatography coupled to mass spectrometry (LC-MS) analysis. The raw data were processed with MaxQuant (1.6.3.3) software with parameters set to default values. Label-free quantification (LFQ) was added and only protein ratios calculated from at least two unique peptides were considered. Our data showed 3 differentially expressed proteins down-regulated in RM (q-value &lt; 0.05). Additionally, we observed proteins present exclusively in each treatment (RM= 137 proteins; MR= 41 proteins). The overall enriched pathways in RM newborn goats are associated with glycolysis (PKM), NADPH synthesis (PGD), lipid oxidation (ECHS1, ACAT1) and citrate cycle (ACO1, OGDH). While the overall enriched pathways in MR newborn goats are associated with glycolysis/gluconeogenesis (GAPDH, ENO) and citrate cycle (PDHA, IDH3A). In addition, correlation analysis between shotgun proteomics and RNAseq data from the same samples showed that there were no relationships between proteins and transcripts observed. These results indicate that maternal feed restriction during different stages of gestation alters enzymes and protein domains abundance associated with nucleotide metabolism in the skeletal muscle of newborn goats. Moreover, the lack of correlation between protein-transcript suggests the importance of post-transcriptional regulation of skeletal muscle metabolism as a consequence of maternal feed restriction at different stages of gestation.

scholarly journals Open-pFind enables precise, comprehensive and rapid peptide identification in shotgun proteomics

2018 ◽  
Author(s):  
Hao Chi ◽  
Chao Liu ◽  
Hao Yang ◽  
Wen-Feng Zeng ◽  
Long Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Large Fraction ◽  
Olfactory Receptors ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Isotopic Labeling ◽  
Search Space ◽  
Global Scale ◽  
Search Algorithms

ABSTRACTShotgun proteomics has grown rapidly in recent decades, but a large fraction of tandem mass spectrometry (MS/MS) data in shotgun proteomics are not successfully identified. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi- or non-specific digestion and co-eluting peptides. Tested on two metabolically labeled MS/MS datasets, Open-pFind reported 50.5‒117.0% more peptide-spectrum matches (PSMs) than the seven other advanced algorithms. More importantly, the Open-pFind results were more credible judged by the verification experiments using stable isotopic labeling. Tested on four additional large-scale datasets, 70‒85% of the spectra were confidently identified, and high-quality spectra were nearly completely interpreted by Open-pFind. Further, Open-pFind was over 40 times faster than the other three open search algorithms and 2‒3 times faster than three restricted search algorithms. Re-analysis of an entire human proteome dataset consisting of ∼25 million spectra using Open-pFind identified a total of 14,064 proteins encoded by 12,723 genes by requiring at least two uniquely identified peptides. In this search results, Open-pFind also excelled in an independent test for false positives based on the presence or absence of olfactory receptors. Thus, a practical use of the open search strategy has been realized by Open-pFind for the truly global-scale proteomics experiments of today and in the future.

scholarly journals The Canon of Dutch Literature According to Google

2019 ◽  
Author(s):  
Lucas van der Deijl ◽  
Antal van den Bosch ◽  
Roel Smeets
Keyword(s):  
Knowledge Base ◽  
Search Engines ◽  
Literary History ◽  
Information Sources ◽  
Search Algorithms ◽  
Web Pages ◽  
Printed Media ◽  
Literary Histories ◽  
Dutch Literature ◽  
The Web

Literary history is no longer written in books alone. As literary reception thrives in blogs, Wikipedia entries, Amazon reviews, and Goodreads pro les, the Web has become a key platform for the exchange of information on literature. Al- though conventional printed media in the eld—academic monographs, literary supplements, and magazines—may still claim the highest authority, online me- dia presumably provide the rst (and possibly the only) source for many readers casually interested in literary history. Wikipedia o ers quick and free answers to readers’ questions and the range of topics described in its entries dramatically exceeds the volume any printed encyclopedia could possibly cover. While an important share of this expanding knowledge base about literature is produced bottom-up (user based and crowd-sourced), search engines such as Google have become brokers in this online economy of knowledge, organizing information on the Web for its users. Similar to the printed literary histories, search engines prioritize certain information sources over others when ranking and sorting Web pages; as such, their search algorithms create hierarchies of books, authors, and periods.

scholarly journals TDFragMapper: a visualization tool for evaluating experimental parameters in top-down proteomics

2021 ◽  
Author(s):  
Jonathan Steven Dhenin ◽  
Diogo Borges Lima ◽  
Mathieu Dupre ◽  
Julia Chamot-Rooke
Keyword(s):  
Software Tool ◽  
Rapid Evaluation ◽  
Visualization Tool ◽  
Sequence Coverage ◽  
Top Down ◽  
Intact Proteins ◽  
Fragment Ions ◽  
Experimental Parameters ◽  
Demonstration Video

We present a new software-tool allowing an easy visualization of fragment ions and thus a rapid evaluation of key experimental parameters on the sequence coverage obtained for the MS/MS analysis of intact proteins. Our tool can deal with multiple fragmentation methods. We demonstrate that TDFragMapper can rapidly highlight the experimental fragmentation parameters that are critical to the characterization of intact proteins of various size using top-down proteomics. TDFragMapper, a demonstration video and user tutorial are freely available at https://msbio.pasteur.fr/tdfragmapper, for academic use; all data are thus available from the ProteomeXchange consorti-um (identifier PXD024643).

scholarly journals PeptideMind – applying machine learning algorithms to assess replicate quality in shotgun proteomic data

2020 ◽  
Author(s):  
D.C.L. Handler ◽  
P.A. Haynes
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Shotgun Proteomics ◽  
Machine Learning Algorithms ◽  
Proteomics Data ◽  
Proteomic Data ◽  
Proteomics Experiment ◽  
Significance Measures ◽  
Proteomics Data Analysis

AbstractAssessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

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