Dataset Information

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database.

ABSTRACT: We designed a metaproteomic analysis method (ComPIL) to accommodate the ever-increasing number of sequences against which experimental shotgun proteomics spectra could be accurately and rapidly queried. Our objective was to create these large databases for the analysis of complex metasamples with unknown composition, including those derived from human, animal, and environmental microbiomes. The amount of high-throughput sequencing data has substantially increased since our original database was assembled in 2014. Here, we present a rebuild of the ComPIL libraries comprised of updated publicly disseminated sequence data as well as a modified version of the search engine ProLuCID-ComPIL optimized for querying experimental spectra. ComPIL 2.0 consists of 113 million protein records and roughly 4.8 billion unique tryptic peptide sequences and is 2.3 times the size of our original version. We searched a data set collected on a healthy human gut microbiome proteomic sample and compared the results to demonstrate that ComPIL 2.0 showed a substantial increase in the number of unique identified peptides and proteins compared to the first ComPIL version. The high confidence of protein identification and accuracy demonstrated by the use of ComPIL 2.0 may encourage the method's application for large-scale proteomic annotation of complex protein systems.

SUBMITTER: Park SKR

PROVIDER: S-EPMC7767584 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database.

Park Sung Kyu Robin SKR Jung Titus T Thuy-Boun Peter S PS Wang Ana Y AY Yates John R JR Wolan Dennis W DW

Journal of proteome research 20190114 2

We designed a metaproteomic analysis method (ComPIL) to accommodate the ever-increasing number of sequences against which experimental shotgun proteomics spectra could be accurately and rapidly queried. Our objective was to create these large databases for the analysis of complex metasamples with unknown composition, including those derived from human, animal, and environmental microbiomes. The amount of high-throughput sequencing data has substantially increased since our original database was ...[more]

PMID: 30525664

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Project description:BackgroundMass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations.ResultsOur approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed "Blazmass") to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples.ConclusionsThe combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

Project description:Disulphide bonds are stabilizing crosslinks in proteins and serve to enhance their thermal stability. In proteins that are small and rich in disulphide bonds, they could be the major determining factor for the choice of conformational state since their constraints on appropriate backbone conformation can be substantial. Such crosslinks and their positional conservation could itself enable protein family and functional association. Despite the importance of the field, there is no comprehensive database on disulphide crosslinks that is available to the public. Herein we provide information on disulphides in DSDBASE2.0, an updated and significantly expanded database that is freely available, fully annotated and manually curated database on native and modelled disulphides. The web interface also provides several useful computational tools that have been specifically developed for proteins containing disulphide crosslinks. The modelling of disulphide crosslinks is performed using stereochemical criteria, coded within our Modelling of Disulphides in Proteins (MODIP) algorithm. The inclusion of modelled disulphides potentially enhances the loop database substantially, thereby permitting the recognition of compatible polypeptide segments that could serve as templates for immediate modelling. The DSDBASE2.0 database has been updated to include 153,944 PDB entries, 216,096 native and 20,153,850 modelled disulphide bond segments from PDB January 2021 release. The current database also provides a resource to user-friendly search for multiple disulphide bond containing loops, along with annotation of their function using GO and subcellular localization of the query. Furthermore, it is possible to obtain the three-dimensional models of disulphide-rich small proteins using an independent algorithm, RANMOD, that generates and examines random, but allowed backbone conformations of the polypeptide. DSDBASE2.0 still remains the largest open-access repository that organizes all disulphide bonds of proteins on a single platform. The database can be accessed from http://caps.ncbs.res.in/dsdbase2.

Dataset Information

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database.

Publications

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database.

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