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Fast protein fold recognition via sequence to structure alignment and contact capacity potentials

N N Alexandrov1, R Nussinov, R M Zimmer

  • 1Laboratory of Mathematical Biology, NCI-FCRF, Frederick, MD 21702-1201, USA. nicka@ncifcrf.gov

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 1, 1996
PubMed
Summary

We developed new scoring potentials and alignment methods to match protein sequences with their structures. This helps predict protein folds and improve sequence alignments for better modeling, even without obvious sequence similarity.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Accurate protein sequence-structure alignment is crucial for understanding protein function.
  • Existing methods may struggle with proteins lacking significant sequence similarity.

Purpose of the Study:

  • To introduce novel empirical scoring potentials and alignment procedures for protein sequence-structure matching.
  • To enable fold recognition for unknown protein sequences and enhance existing sequence alignments.

Main Methods:

  • Development of empirical "contact capacity" potentials derived from known protein structures.
  • Utilizing inverse Boltzmann law for normalization and conversion of frequencies to pseudoenergies.
  • Implementation of alignment procedures for sequence-structure matching.

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Main Results:

  • The new potentials effectively recognize plausible protein folds from a database, even with low sequence similarity.
  • The method provides a robust starting point for homology-based modeling by incorporating structural information.
  • The potentials are computationally efficient, allowing for fast optimization.

Conclusions:

  • The proposed empirical scoring potentials offer a powerful and efficient tool for protein sequence-structure alignment.
  • This approach advances protein fold recognition and improves the accuracy of structural modeling.
  • The method is valuable for analyzing proteins with limited sequence information.