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Related Experiment Videos

The relationship between n-gram patterns and protein secondary structure.

John K Vries1, Xiong Liu, Ivet Bahar

  • 1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. vries@ccbb.pitt.edu

Proteins
|May 25, 2007
PubMed
Summary
This summary is machine-generated.

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N-gram patterns (NP{n,m}) in protein sequences reveal evolutionary links and predict secondary structure. New methods differentiate evolutionary and local propensity information for improved protein classification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • N-gram patterns (NP{n,m}) represent sets of residues and wildcards within protein sequences.
  • Shared NP{n,m} patterns indicate evolutionary relationships, forming the basis for protein classification algorithms.
  • NP{4,2} patterns theoretically capture secondary structure propensities due to their window size and combinatorial nature.

Purpose of the Study:

  • To differentiate the evolutionary and local propensity information within NP{4,2} patterns.
  • To develop and validate a secondary structure prediction algorithm based on NP{4,2} pattern Z-values.
  • To assess the relative contributions of evolutionary and local propensity to protein classification.

Main Methods:

  • Calculated probabilities of alpha-, beta-, and coil components for each NP{4,2} pattern across Protein Data Bank (PDB) chains.

Related Experiment Videos

  • Developed a prediction algorithm using Z-values of these probability distributions.
  • Grouped PDB chains by sequence identity to isolate evolutionary and local propensity contributions.

    • Main Results:

    • The NP{4,2}-based algorithm accurately predicted 71-76% of alpha-helical segments and 62-67% of beta-sheets in jackknife tests.
    • Demonstrated a strong correlation between NP{4,2} patterns and protein secondary structure.
    • Found evolutionary information to be more critical for beta-sheet prediction than alpha-helix prediction.

    Conclusions:

    • NP{4,2} patterns effectively capture secondary structure information, supporting their use in protein classification.
    • The developed method successfully distinguishes between evolutionary and local propensity signals in protein sequences.
    • Understanding the interplay between evolutionary and local factors enhances the accuracy of protein structure and function predictions.