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PCAS--a precomputed proteome annotation database resource.

Yong Zhang1, Yanbin Yin, Yunjia Chen

  • 1College of Life Sciences, National Laboratory of Genetic Engineering and Protein Engineering, Center of Bioinformatics, Peking University, Beijing 100871, China. zhangy@mail.cbi.pku.edu.cn

BMC Genomics
|November 5, 2003
PubMed
Summary
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The ProteinCentric Annotation System (PCAS) offers comprehensive proteome annotation using diverse algorithms. This online resource enhances functional classification of proteins and serves as a valuable discovery platform.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Proteomics

Background:

  • Publicly available model proteomes require computational annotation for functional classification.
  • Motif and domain recognition algorithms are crucial for protein function prediction.
  • Existing resources can be complemented by integrated proteome annotation data.

Purpose of the Study:

  • To develop an online proteome annotation system (PCAS) integrating diverse computational algorithms.
  • To provide pre-computed annotation data for model proteomes.
  • To enhance functional classification and serve as a discovery platform.

Main Methods:

  • Applied multiple motif/domain recognition algorithms (HMMs, PSSMs, patterns) from Pfam, SMART, TIGRFAM, CDD, PROSITE, and SUPERFAMILY.

Related Experiment Videos

  • Integrated databases via InterPro mapping.
  • Utilized SignalP and TMHMM for signal peptide and transmembrane region prediction.

    • Main Results:

    • Developed PCAS, an online resource with pre-computed proteome annotation data.
    • Included human, mouse, rat, Arabidopsis thaliana, C. elegans, D. melanogaster, S. cerevisiae, and S. pombe proteomes.
    • Provided graphical and tabular summaries of protein motifs and domains.

    Conclusions:

    • PCAS offers improved annotation coverage for model proteomes.
    • The system facilitates inspection of both confident and boundary annotation data.
    • PCAS functions as a discovery platform, with planned biannual updates and algorithm upgrades.