scholarly journals MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

2020 ◽  
Author(s):  
Alexander R. Pelletier ◽  
Yun-En Chung ◽  
Zhibin Ning ◽  
Nora Wong ◽  
Daniel Figeys ◽  
...  

ABSTRACTMass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Author(s):  
Luferov An ◽  
Kartashova Nv ◽  
Strelyaeva Av ◽  
Kuznetcov Rm

Objective: The study was carried out with an objective to characterize the possible bioactive phytochemical constituents from fruits of Schisandra chinensis Bail. by liquid chromatography–mass spectrometry analysis.Methods: Plant material was collected from Schisandra chinensis during August–October. The dried plant fruits were extracted with solvents using ethanol 95% extractor. The results of chromatography–MS analysis performed on the instrument Agilent Technologies established the presence of major and minor components. It was conducted a qualitative and quantitative comparison of infusions using software ChemStationE 02.00 and full library of mass spectra NIST 05.


2018 ◽  
Vol 32 (19) ◽  
pp. 1659-1667 ◽  
Author(s):  
Kenny Bravo-Rodriguez ◽  
Birte Hagemeier ◽  
Lea Drescher ◽  
Marian Lorenz ◽  
Juliana Rey ◽  
...  

Zygote ◽  
2019 ◽  
Vol 28 (2) ◽  
pp. 170-173
Author(s):  
Thaís T.S. Souza ◽  
Maria J.B. Bezerra ◽  
Maurício F. van Tilburg ◽  
Celso S. Nagano ◽  
Luciana D. Rola ◽  
...  

SummaryThe aim of this study was to characterize the protein profile of ovarian follicular fluid (FF) of brown brocket deer (Mazama gouazoubira). Five adult females received an ovarian stimulation treatment and the FF was collected by laparoscopy from small/medium (≤3.5 mm) and large (>3.5 mm) follicles. Concentrations of soluble proteins in FF samples were measured and proteins were analyzed by 1-D SDS-PAGE followed by tryptic digestion and tandem mass spectrometry. Data from protein list defined after a Mascot database search were analyzed using the STRAP software tool. For the protein concentration, no significant difference (P > 0.05) was observed between small/medium and large follicles: 49.2 ± 22.8 and 56.7 ± 27.4 μg/μl, respectively. Mass spectrometry analysis identified 13 major proteins, but with no significant difference (P > 0.05) between follicle size class. This study provides insight into elucidating folliculogenesis in brown brocket deer.


2019 ◽  
Author(s):  
Wenfa Ng

Mass spectrometry-enabled microbial identification has successfully demonstrated the feasibility of using profiled biomolecules for identifying microorganisms based on a chemometric or proteome database search approach. However, mechanisms driving the preferential ionization and detection of particular biomolecules in various types of mass spectrometry remain poorly understood. Specifically, mass spectra obtained from different microbial species remain poorly annotated with respect to the specific types of biomolecules accounting for the peaks. For example, while ribosomal proteins are known to be a significant class of biomolecules that could partially account for the profiled mass peaks in mass spectra of microorganisms, other classes of proteins and biomolecules remain poorly annotated. This raises the important question of how different mass spectrometry approaches ionize different types of biomolecules from a cellular matrix. Specifically, mass spectra of microorganisms reveal that only a couple of mass peaks could capture the phylogeny of a species. However, the proteome of a cell is much larger and more complicated, and yet is not fully profiled by different types of mass spectrometry methods. For example, electrospray ionization mass spectrometry (ESI-MS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could only provide a small snapshot of the entire bacterial proteome. It could be argued that different mass spectrometry methods provide complementary views of a particular proteome. However, the question remains, how do proteins and biomolecules interact with the different sample preparation and mass spectrometry analysis methods for generating an ion cloud for separation in a mass spectrometer? Thus, efforts could be directed towards understanding how different types of proteins could be preferentially ionized by MALDI-TOF MS. Specifically, different reagents could be used to perform chemical pretreatment on the proteome, which would subsequently be analyzed by mass spectrometry. Thus, a correlative map between types of chemical pretreatment used and the corresponding mass spectra could be obtained. Collectively, knowledge gleaned from the research would illuminate the chemical basis by which specific biomolecules are preferentially ionized under particular conditions, which would inform the development of strategies for increasing the subset of biomolecules ionized from a cellular proteome. Such chemical rules would also aid in the interpretation of mass spectra obtained, particularly in understanding the biological context of the experiment. Overall, the key goal of this research is to help answer the question: what is the biological basis and context of the mass spectrum obtained from cells?


2012 ◽  
Vol 3 (2) ◽  
pp. 64-85 ◽  
Author(s):  
Syarifah Adilah Mohamed Yusoff ◽  
Ibrahim Venkat ◽  
Umi Kalsom Yusof ◽  
Rosni Abdullah

Mass spectrometry is an emerging technique that is continuously gaining momentum among bioinformatics researchers who intend to study biological or chemical properties of complex structures such as protein sequences. This advancement also embarks in the discovery of proteomic biomarkers through accessible body fluids such as serum, saliva, and urine. Recently, literature reveals that sophisticated computational techniques mimetic survival and natural processes adapted from biological life for reasoning voluminous mass spectrometry data yields promising results. Such advanced approaches can provide efficient ways to mine mass spectrometry data in order to extract parsimonious features that represent vital information, specifically in discovering disease-related protein patterns in complex proteins sequences. This article intends to provide a systematic survey on bio-inspired approaches for feature subset selection via mass spectrometry data for biomarker analysis.


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