Protein Identification by Peptide Mass Fingerprinting using MALDI-TOF Mass Spectrometry

AbstractProteomic approaches based on two-dimensional gel electrophoresis, mass spectrometry and database search are widely used to address questions about the development, physiology and quality of seeds. Identification of proteins is of great importance in proteomic analyses. For seed crops without full genome information, cross-species protein identification by mass spectrometry-driven sequence similarity search can be used. However, this approach is risky due to protein polymorphism between different species. Species-specific expressed sequence tag (EST) databases are an invaluable resource, which complements mass spectrometry data analysis for protein identification. Here, we illustrate a modified method of protein identification and characterization using species-specific EST databases and peptide mass fingerprinting with an example of protein identification. This method is reliable, supplements the existing methods, and improves the efficiency and accuracy of protein identification for seed crops for which complete genome information is not available.

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A novel computational approach applicable to human microbiome studies – urinary tract microbiome exaple

10.14293/p2199-8442.1.sop-life.pyc2l3.v1 ◽

2015 ◽

Author(s):

Antonio Starcevic ◽

Janko Diminic ◽

Mario Cindric ◽

Kristina Perica ◽

Marina Ceprnja ◽

...

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Protein Identification ◽

Rapid Development ◽

Peptide Mass Fingerprinting ◽

Mass Spectrometry Data ◽

Clinical Proteomics ◽

Tandem Mass ◽

Peptide Mass ◽

Speed Accuracy

Peptide mass fingerprinting is a term which describes technique which utilizes ESI or MALDI MS followed by tandem mass spectrometry sequencing. This technique has become a cornerstone for protein identification. Today, applications using peptide mass fingerprinting in biomedical analyses are a major driving force behind its rapid development. However, efficient and accurate analyses of generally big protein tandem mass spectrometry data sets require robust software. In terms of final goal, which is data interpretation, the role of software and underlying algortihms is at least equally important as the technique itself, a fact which is often neglected. High-throughput mass spectrometry instruments can readily generate hundreds of thousands of spectra. This fact combined with the ever growing size of genomic databases imposes tremendous demands for potential successful softvare solutions. In fact, it is the process of comparing large-scale mass spectrometry data with large databases that remains the toughest bottleneck in proteomics. Here we present a completely novel approach based on natural language processing which is not just another improvement of existing approaches, but represents a paradigm shift. It doesn't rely on peak intesity for database peptide matching and it uses newly developed concept of microbial proteome fingerptints for strain/species identification. Since this new algorithm doesn't rely on sequence alignment but instead utilizes a concept of singular proteome fingerprints rather than sets of unrelated peptides, it proposes an elegant solution for this most troubling step in proteome analyses. Abandoning BLAST and other alignment based methods. results in far superior processing speed, accuracy and sensitivity. The above mentioned algortithm can be used to analyse not only proteomes but also metaproteomes coming from mixed microbe communities as in the case presented – human urine samples taken from a hospital. The method itself is completely generic, not developed with any specific platform in mind, which makes it highly versatile, able to turn any existing device into highly efficient metaproteome analyzer without siginificant costs related to purchase of new equipment. This work was funded by HRZZ (Croatian Science Foundation) research project “Clinical proteomics of microorganisms”.

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Identification of newly developed advanced schistosomiasis with MALDI-TOF mass spectrometry and ClinProTools analysis

Parasite ◽

10.1051/parasite/2019032 ◽

2019 ◽

Vol 26 ◽

pp. 33 ◽

Cited By ~ 3

Author(s):

Yuzheng Huang ◽

Yongliang Xu ◽

Yi Huang ◽

Fang Sun ◽

Haisong Tian ◽

...

Keyword(s):

Mass Spectrometry ◽

Magnetic Beads ◽

Clinical Symptoms ◽

Healthy Controls ◽

Early Screening ◽

Double Blind ◽

Maldi Tof Mass Spectrometry ◽

Maldi Tof ◽

Advanced Schistosomiasis ◽

Peptide Mass

Cases of newly developed advanced schistosomiasis (NDAS) have occurred in areas where schistosomiasis transmission has been blocked for more than 25 years. The causes and pathogenesis of NDAS are still unknown. Diagnosis of NDAS relies on historical investigation and clinical symptoms, such as liver fibrosis, hepatic ascites and abnormal biochemical indexes in serum. It is important but difficult at this stage to develop a new tool for early screening and rapid diagnosis. In this study, serum peptides from thirty patients with NDAS and thirty healthy controls were captured with weak cation exchange magnetic beads, and subjected to MALDI-TOF mass spectrometry and ClinProTools analysis. Eleven peaks with m/z 924, 2661, 2953, 2991, 3241, 3884, 5337, 5905, 5943, 7766 and 9289 were decreased and three peaks with m/z 1945, 2082 and 4282 were increased in the NDAS group. The proteomic detection pattern (PDP) was established with 14 different peptide peaks, and its sensitivity and specificity were investigated with a blind test. The peptide mass fingerprints of sera from 50 NDAS patients and 100 healthy controls were double-blind subjected to the PDP method, and 50 patients and 92 healthy controls were classified as NDAS and healthy separately, which showed 100% sensitivity and 92% specificity. Our results showed that the PDP could be a new and useful method to detect NDAS.

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