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DPANN: improved sequence to structure alignments following fold recognition.

Astrid Reinhardt1, David Eisenberg

  • 1Faint Signals Pattern Recognition, Los Angeles, California, USA.

Proteins
|July 2, 2004
PubMed
Summary
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Dynamic Programming meets Artificial Neural Networks (DPANN) improves protein fold recognition alignments. This method enhances accuracy for protein structure prediction and modeling by refining sequence-to-structure alignments.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Structure Prediction

Background:

  • Protein fold recognition (FR) assigns unknown protein sequences to known 3D folds.
  • Current FR methods often fail to accurately align sequences to identified structures, hindering protein model building.
  • Improved sequence-structure alignment is crucial for accurate protein structure prediction.

Purpose of the Study:

  • To enhance the accuracy of sequence-to-structure alignments in protein fold recognition.
  • To develop a novel method combining artificial neural networks (ANN) and dynamic programming (DP) for improved alignment.
  • To provide better templates for protein structure modeling.

Main Methods:

  • Developed a substitution matrix using artificial neural networks (ANN).

Related Experiment Videos

  • The matrix incorporates amino acid type and secondary structure state.
  • Implemented Dynamic Programming meets Artificial Neural Networks (DPANN) for sequence-structure alignment.

    • Main Results:

    • DPANN achieved accurate alignments for over 30% of sequences in a fold-recognition benchmark.
    • In over half of successful cases, DPANN alignments closely matched structural superpositions, significantly outperforming initial FR alignments.
    • Using actual secondary structures improved DPANN alignment success to over 50%.

    Conclusions:

    • DPANN substantially improves alignment accuracy after initial fold recognition.
    • Enhanced alignment quality provides more reliable templates for protein structure modeling.
    • The DPANN method offers a significant advancement in computational protein structure prediction.