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Inferring function using patterns of native disorder in proteins.

Anna Lobley1, Mark B Swindells, Christine A Orengo

  • 1Bioinformatics Unit, Department of Computer Science, University College London, London, United Kingdom.

Plos Computational Biology
|August 29, 2007
PubMed
Summary
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Protein disorder significantly enhances the prediction of protein function, particularly for signaling and molecular recognition categories. This study demonstrates the value of incorporating intrinsically disordered regions into functional annotation for unannotated proteins.

Area of Science:

  • Molecular Biology
  • Bioinformatics

Background:

  • Eukaryotic proteomes commonly feature natively unstructured regions, with 30-60% of proteins predicted to contain intrinsically disordered regions (IDRs).
  • These disordered regions are experimentally confirmed and play essential roles in protein function.

Purpose of the Study:

  • To evaluate the contribution of protein disorder to predicting protein function using Gene Ontology (GO) categories.
  • To analyze the occurrence and patterns of protein disorder in the human proteome and identify enriched GO categories.

Main Methods:

  • Analysis of protein disorder occurrence and distribution patterns in human proteomes.
  • Encoding length- and position-dependent disorder features into Support Vector Machine (SVM) classifiers.
  • Assessing prediction accuracy improvements for 26 GO categories related to signaling and molecular recognition.

Related Experiment Videos

Main Results:

  • Protein disorder patterns are both length- and position-dependent, influencing protein function.
  • Incorporating disorder features improved prediction accuracies for 26 GO categories, notably for kinase, phosphorylation, growth factor, and helicase functions.
  • Predicted GO term assignments were generated for unannotated and orphan human proteins.

Conclusions:

  • Protein disorder information is crucial for accurate protein function prediction.
  • The developed GO category classifiers offer reliable predictions and insights into orphan and unannotated proteins' functions.