Large Scale Protein Profiling by Combination of Protein Fractionation and Multidimensional Protein Identification Technology (MudPIT)

Coupling large-scale sequencing projects with the amino acid sequence information that can be gleaned from tandem mass spectrometry (MS/MS) has made it much easier to analyze complex mixtures of proteins. The limits of this “shotgun” approach, in which the protein mixture is proteolytically digested before separation, can be further expanded by separating the resulting mixture of peptides prior to MS/MS analysis. Both single dimensional high pressure liquid chromatography (LC) and multidimensional LC (LC/LC) can be directly interfaced with the mass spectrometer to allow for automated collection of tremendous quantities of data. While there is no single technique that addresses all proteomic challenges, the shotgun approaches, especially LC/LC-MS/MS-based techniques such as MudPIT (multidimensional protein identification technology), show advantages over gel-based techniques in speed, sensitivity, scope of analysis, and dynamic range. Advances in the ability to quantitate differences between samples and to detect for an array of post-translational modifications allow for the discovery of classes of protein biomarkers that were previously unassailable.

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Faculty Opinions recommendation of Multidimensional protein identification technology-selected reaction monitoring improving detection and quantification for protein biomarker studies.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13489139.15223056 ◽

2012 ◽

Author(s):

Jin-Xiong She ◽

Wenbo Zhi

Keyword(s):

Protein Identification ◽

Selected Reaction Monitoring ◽

Reaction Monitoring ◽

Protein Biomarker ◽

Multidimensional Protein Identification Technology ◽

Detection And Quantification

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KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Frontiers in Genetics ◽

10.3389/fgene.2021.811158 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yuran Jia ◽

Shan Huang ◽

Tianjiao Zhang

Keyword(s):

Dna Binding ◽

Prediction Accuracy ◽

Large Scale ◽

Protein Identification ◽

Feature Fusion ◽

Binding Protein ◽

Rapid Development ◽

Dna Binding Protein ◽

Feature Extraction Method ◽

Independent Test Dataset

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

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Recent developments and prospects in the composition, extraction, stability, delivery system, digestion and food applications of plant oil bodies

10.22541/au.162844018.83758344/v1 ◽

2021 ◽

Author(s):

Jia Hao ◽

Duoxia Xu ◽

Yangping Cao

Keyword(s):

Delivery System ◽

Large Scale ◽

Protein Identification ◽

Meat Products ◽

Calorie Intake ◽

Oil Bodies ◽

Plant Oil ◽

Electrostatic Deposition ◽

Food Applications ◽

The Stability

Oil bodies (OBs) are micron- or submicron-sized sub-organelles widely found in plants seeds and nuts. The structure OBs is composed of a core of triglycerides covered by a phospholipid-protein layer, which ensures the stability of the OBs under extreme environmental conditions and further protects core lipids as energy reserves. As naturally pre-emulsified oil-in-water emulsions, OBs have been gradually applied to replace synthetically engineered oil droplets. In this paper, the recent research on the composition, extraction, stability, delivery system, digestion, food applications and future perspectives of plant OBs are reviewed. Recent studies have focused on the OBs surface protein identification and function, large-scale extraction techniques such as enzyme assisted, high pressure, ultrasound, and extrusion and the reconstituted OBs. Electrostatic deposition of polysaccharides significantly improves the stability of OBs emulsions. OBs emulsions have promising applications to encapsulate bioactive compounds, deliver targeted drugs, and prepare gels and edible functional films. The digestive behavior of OBs emulsions is similar to that of protein-stabilized emulsions, which can increase the satiety, effectively help reduce calorie intake and improve the bioavailability of functional factors. It has also promoted the development of simulated dairy, spices and meat products.

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Data Mining in Protein Identification by Tandem Mass Spectrometry

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch074 ◽

2011 ◽

pp. 472-478

Author(s):

Haipeng Wang

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Tandem Mass Spectrometry ◽

Large Scale ◽

Protein Identification ◽

Peptide Fragmentation ◽

Tandem Mass ◽

Fragmentation Pattern ◽

Post Translational Modification ◽

Challenges And Opportunities

Protein identification (sequencing) by tandem mass spectrometry is a fundamental technique for proteomics which studies structures and functions of proteins in large scale and acts as a complement to genomics. Analysis and interpretation of vast amounts of spectral data generated in proteomics experiments present unprecedented challenges and opportunities for data mining in areas such as data preprocessing, peptide-spectrum matching, results validation, peptide fragmentation pattern discovery and modeling, and post-translational modification (PTM) analysis. This article introduces the basic concepts and terms of protein identification and briefly reviews the state-of-the-art relevant data mining applications. It also outlines challenges and future potential hot spots in this field.

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Parallel Channels-Multidimensional Protein Identification Technology

Journal of the American Society for Mass Spectrometry ◽

10.1021/jasms.0c00055 ◽

2020 ◽

Vol 31 (7) ◽

pp. 1440-1447

Author(s):

Nan Zhang ◽

Xiaojing Liu ◽

Shuaixin Gao ◽

Catherine Chiulan Wong

Keyword(s):

Protein Identification ◽

Multidimensional Protein Identification Technology ◽

Parallel Channels

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Proteomics: current techniques and potential applications to lung disease

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00301.2003 ◽

2004 ◽

Vol 287 (1) ◽

pp. L1-L23 ◽

Cited By ~ 70

Author(s):

Jan Hirsch ◽

Kirk C. Hansen ◽

Alma L. Burlingame ◽

Michael A. Matthay

Keyword(s):

Posttranslational Modifications ◽

Protein Separation ◽

Protein Identification ◽

Cell Types ◽

Protein Profiling ◽

Complex Samples ◽

Potential Value ◽

Potential Applications ◽

Tissue Specimens ◽

Different Cell Types

Proteomics aims to study the whole protein content of a biological sample in one set of experiments. Such an approach has the potential value to acquire an understanding of the complex responses of an organism to a stimulus. The large vascular and air space surface area of the lung expose it to a multitude of stimuli that can trigger a variety of responses by many different cell types. This complexity makes the lung a promising, but also challenging, target for proteomics. Important steps made in the last decade have increased the potential value of the results of proteomics studies for the clinical scientist. Advances in protein separation and staining techniques have improved protein identification to include the least abundant proteins. The evolution in mass spectrometry has led to the identification of a large part of the proteins of interest rather than just describing changes in patterns of protein spots. Protein profiling techniques allow the rapid comparison of complex samples and the direct investigation of tissue specimens. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. These methodologies have made the application of proteomics on the study of specific diseases or biological processes under clinically relevant conditions possible. The quantity of data that is acquired with these new techniques places new challenges on data processing and analysis. This article provides a brief review of the most promising proteomics methods and some of their applications to pulmonary research.

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