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PIRSitePredict for protein functional site prediction using position-specific rules.

Chuming Chen1,2, Qinghua Wang1,2, Hongzhan Huang1,2

  • 1Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.

Database : the Journal of Biological Databases and Curation
|February 27, 2019
PubMed
Summary
This summary is machine-generated.

We developed PIRSitePredict, a new tool for predicting protein functional sites. This method aids in annotating uncharacterized proteins, crucial for understanding genetic variation and advancing biological research.

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Area of Science:

  • * Bioinformatics
  • * Computational Biology
  • * Genomics

Background:

  • * Global protein annotation methods are mature, but local functional site prediction is advancing.
  • * Genetic variant data accumulation necessitates fine-grained protein site annotation.
  • * Existing methods require improvement for predicting functional sites at the amino acid level.

Purpose of the Study:

  • * To develop and share a computational method for predicting protein functional sites.
  • * To introduce PIRSitePredict, a Java software package for functional site prediction.
  • * To demonstrate the application of PIRSitePredict in annotating uncharacterized proteins from cartilaginous fishes.

Main Methods:

  • * Developed position-specific conditional template annotation rules (PIR Site Rules or PIRSRs).
  • * Curated rules using protein structural and experimental data.
  • * Created a stand-alone Java software package, PIRSitePredict, for workflow streamlining.

Main Results:

  • * Successfully annotated uncharacterized proteins from de novo assembled transcriptomes.
  • * Applied PIRSitePredict to embryonic transcriptomes of three cartilaginous fishes.
  • * Generated approximately 1200 lines of functional site annotations per species.

Conclusions:

  • * PIRSitePredict provides a valuable tool for functional site prediction.
  • * The method enhances the annotation of uncharacterized proteins, particularly from genomic/transcriptomic data.
  • * Facilitates broader scientific community access to advanced protein functional site prediction.