Dataset Information

Use of composite protein database including search result sequences for mass spectrometric analysis of cell secretome.

ABSTRACT: Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.

SUBMITTER: Shin J

PROVIDER: S-EPMC4378925 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Use of composite protein database including search result sequences for mass spectrometric analysis of cell secretome.

Shin Jihye J Kim Gamin G Kabir Mohammad Humayun MH Park Seong Jun SJ Lee Seoung Taek ST Lee Cheolju C

PloS one 20150330 3

Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable r ...[more]

PMID: 25822838

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Project description:Sinorhizobium fredii is a dominant rhizobium on alkaline-saline land that can induce nitrogen-fixing symbiotic root nodules in soybean. Two S. fredii strains, CCBAU25509 and CCBAU45436, were used in this study to facilitate in-depth analyses of this species and its interactions with soybean. We have previously completed the full assembly of the genomes and detailed transcriptomic analyses for these two S. fredii strains, CCBAU25509 and CCBAU45436, that exhibit differential compatibility toward some soybean hosts. In this work, we performed high-throughput Orbitrap analyses of the whole proteomes and secretomes of CCBAU25509 and CCBAU45436 at different growth stages. Our proteomic data cover coding sequences in the chromosome, chromid, symbiotic plasmid, and other accessory plasmids. In general, we found higher levels of protein expression by genes in the chromosomal genome, whereas proteins encoded by the symbiotic plasmid were differentially accumulated in bacteroids. We identified secreted proteins from the extracellular medium, including seven and eight Nodulation Outer Proteins (Nops) encoded by the symbiotic plasmid of CCBAU25509 and CCBAU45436, respectively. Differential host restriction of CCBAU25509 and CCBAU45436 is regulated by the allelic type of the soybean Rj2(Rfg1) protein. Using sequencing data from this work and available in public databases, our analysis confirmed that the soybean Rj2(Rfg1) protein has three major allelic types (Rj2/rfg1, rj2/Rfg1, rj2/rfg1) that determine the host restriction of some Bradyrhizobium diazoefficiens and S. fredii strains. A mutant defective in the type 3 protein secretion system (T3SS) in CCBAU25509 allowed this strain to nodulate otherwise-incompatible soybeans carrying the rj2/Rfg1 allelic type, probably by disrupting Nops secretion. The allelic forms of NopP and NopI in S. fredii might be associated with the restriction imposed by Rfg1. By swapping the NopP between CCBAU25509 and CCBAU45436, we found that only the strains carrying NopP from CCBAU45436 could nodulate soybeans carrying the rj2/Rfg1 allelic type. However, no direct interaction between either forms of NopP and Rfg1 could be observed.

Project description:BackgroundThe amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed.ResultsIn order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application.ConclusionWe present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.

Dataset Information

Use of composite protein database including search result sequences for mass spectrometric analysis of cell secretome.

Publications

Use of composite protein database including search result sequences for mass spectrometric analysis of cell secretome.

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