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Efficient prediction of nucleic acid binding function from low-resolution protein structures.

András Szilágyi1, Jeffrey Skolnick

  • 1Center of Excellence in Bioinformatics, University at Buffalo, State University of New York, 901 Washington St, Buffalo, NY 14203, USA.

Journal of Molecular Biology
|March 23, 2006
PubMed
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We developed a fast method to predict DNA-binding proteins using only amino acid sequences and low-resolution models. This approach is accurate even with incomplete structural data, identifying novel DNA-binding proteins.

Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational biology

Background:

  • Structural genomics and ab initio methods yield protein structures lacking sequence or fold similarity to known proteins.
  • These low-resolution structures often only contain C-alpha atom positions, posing challenges for functional prediction.

Purpose of the Study:

  • To develop a rapid and efficient method for predicting DNA-binding proteins from limited structural information.
  • To enable functional annotation of proteins with novel structures or unknown functions.

Main Methods:

  • Utilized amino acid sequence composition and spatial distribution asymmetry.
  • Incorporated molecular dipole moment into a linear prediction formula.
  • Employed logistic regression for coefficient derivation on a training dataset.

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Main Results:

  • The method accurately predicts DNA-binding proteins using only C-alpha atom models and amino acid sequences.
  • Predictions remain robust despite inaccuracies in atomic coordinates and protein models.
  • Successfully identified proteins with novel binding site motifs and structures in an unbound state.

Conclusions:

  • This fast and efficient method provides accurate DNA-binding protein predictions from low-resolution models.
  • The approach is insensitive to structural inaccuracies, broadening its applicability.
  • It offers a valuable tool for functional annotation in structural genomics and beyond.