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Related Experiment Videos

Sequence-dependent conformational sampling using a database of phi(i)+1 and psi(i) angles for predicting polypeptide

S Sudarsanam1, S Srinivasan

  • 1Department of Protein Chemistry, Immunex Corporation, Seattle, WA 98101, USA.

Protein Engineering
|March 6, 1998
PubMed
Summary

A novel method predicts polypeptide backbone conformations using sequence-dependent sampling of amino acid dimer angles. This approach accurately models local interactions, aiding in predicting near-native structures for entire protein sequences.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Predicting polypeptide backbone conformations is crucial for understanding protein structure-function relationships.
  • Existing methods often struggle to accurately capture sequence-dependent conformational preferences.

Purpose of the Study:

  • To develop and validate a novel computational method for predicting polypeptide backbone conformations.
  • To assess the method's ability to model sequence-dependent local interactions.

Main Methods:

  • A sequence-dependent sampling method utilizing a database of amino acid dimer angles (phi(i)+1 and psi(i)).
  • Dynamic division of angle pools based on amino acid homology.
  • Generation of 10,000 backbone conformations per hexamer via random angle assignment.

Related Experiment Videos

  • Conformation evaluation using superposition onto pre-selected hexameric templates (helical, extended, turn) with a root mean square deviation cutoff of 1 Å.

    • Main Results:

    • The method successfully predicted backbone conformations for polypeptides with known helical, extended, turn, and mixed alpha-beta structures.
    • High populations of compatible models indicated the most probable hexamer conformation.
    • Assembly of overlapping hexamer models yielded near-native structures for entire sequences.

    Conclusions:

    • The developed method effectively predicts polypeptide backbone conformations based on local sequence-dependent interactions.
    • This approach demonstrates significant potential for accurately modeling protein structures.
    • The strategy of assembling hexamer models offers a pathway to near-native full-sequence structure prediction.