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SPD--a web-based secreted protein database.

Yunjia Chen1, Yong Zhang, Yanbin Yin

  • 1Centre of Bioinformatics, College of Life Sciences, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.

Nucleic Acids Research
|December 21, 2004
PubMed
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Researchers identified 18,152 secreted proteins in humans, mice, and rats using advanced prediction methods and diverse data. This comprehensive dataset, the Secreted Protein Database (SPD), aids in understanding protein function and localization.

Area of Science:

  • Proteomics
  • Bioinformatics
  • Genomics

Background:

  • Secreted proteins play crucial roles in cellular communication and extracellular matrix organization.
  • Accurate identification and annotation of secreted proteins are essential for understanding biological processes and disease mechanisms.
  • Existing databases often lack comprehensiveness and standardized annotation for secreted proteins across multiple species.

Purpose of the Study:

  • To construct a comprehensive and high-confidence dataset of secreted proteins for human, mouse, and rat.
  • To develop and apply novel pipelines for secreted protein prediction, annotation, and classification.
  • To establish a publicly accessible web-based database (SPD) for exploring and downloading secreted protein data.

Main Methods:

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  • Integrated multiple large-scale protein sequence databases (Swiss-Prot, TrEMBL, RefSeq, Ensembl, CBI-Gene).
  • Employed an improved secreted protein prediction approach and developed custom annotation and classification pipelines.
  • Utilized TribeMCL for protein clustering and integrated nine reference datasets, including Gene Ontology Annotation (GOA).

    • Main Results:

    • Successfully identified and curated a total of 18,152 secreted proteins from human, mouse, and rat secretomes.
    • Ranked all predicted secreted proteins by prediction confidence and classified them into functional categories.
    • Developed a user-friendly web-based database (http://spd.cbi.pku.edu.cn) with browsing, searching, and download functionalities.

    Conclusions:

    • The constructed Secreted Protein Database (SPD) provides a valuable, comprehensive resource for the research community.
    • The developed prediction and annotation pipelines enhance the accuracy and scope of secreted protein identification.
    • SPD facilitates further research into the functions and roles of secreted proteins in various biological contexts.