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A Protocol for Computer-Based Protein Structure and Function Prediction
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Improving protein fold recognition with hybrid profiles combining sequence and structure evolution.

Yassine Ghouzam1, Guillaume Postic1, Alexandre G de Brevern1

  • 1Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

ORION, a novel fold recognition method, enhances protein 3D structure prediction by integrating sequence and structural information. This approach improves the detection of evolutionary relationships, outperforming existing tools.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Template-based modeling is crucial for protein 3D structure prediction.
  • Detecting distant evolutionary relationships is key for homology detection.
  • Current methods primarily use evolutionary information from amino acid sequences.

Purpose of the Study:

  • To introduce ORION, a new fold recognition method.
  • To improve the detection of remote homology in protein sequences.
  • To enhance protein structure prediction by incorporating structural information.

Main Methods:

  • ORION utilizes pairwise comparison of hybrid profiles.
  • Hybrid profiles combine evolutionary information from protein sequence and structure.
  • The method employs the 16-state structural alphabet, Protein Blocks, for local conformation description.

Main Results:

  • ORION outperforms PSI-BLAST and HHsearch on multiple benchmarks.
  • The method demonstrates superior performance on CASP8, 9, and 10 target sequences.
  • ORION detects approximately 10% more templates at fold and superfamily SCOP levels.

Conclusions:

  • ORION represents a significant advancement in fold recognition.
  • Integrating sequence and structural information improves homology detection.
  • The method enhances the accuracy of protein 3D structure prediction.