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Estimation of protein function using template-based alignment of enzyme active sites.

Brett Hanson, Charles Westin, Mario Rosa

  • 1Rochester Institute of Technology, School of Chemistry & Materials Science, 1 Lomb Memorial Drive, Rochester, NY 14623, USA. paul.craig@rit.edu.

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Summary

This study introduces ProMOL, a computational tool for predicting enzyme function using active site motifs. ProMOL aids in identifying functional residues and classifying enzymes, improving upon existing methods.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Enzymology

Background:

  • Protein structural data is accumulating faster than experimental characterization.
  • Computational prediction of enzyme function is crucial.
  • Current methods rely on detecting functional motifs in novel protein structures.

Purpose of the Study:

  • To present ProMOL, a novel computational method for enzyme function prediction.
  • To detect enzyme active site motifs based on distance restraints between catalytic residues.
  • To complement existing structure-based function prediction strategies.

Main Methods:

  • ProMOL plugin for PyMOL molecular graphics environment.
  • Creation of an active site motif library from Catalytic Site Atlas (CSA) definitions.
  • Translation of existing motif sets (JESS) and development of NMR motifs.
  • Evaluation of alignments using visual superposition, Levenshtein distance, and root-mean-square deviation (RMSD).

Main Results:

  • A library of 181 active site motifs was created.
  • ProMOL achieved >50% useful Enzyme Commission (EC) class suggestions for specific EC levels.
  • Additional motifs were incorporated from JESS and NMR datasets.
  • Alignment evaluations showed consistency with related search methods.

Conclusions:

  • ProMOL offers accessible template-based local alignments for enzyme active sites.
  • Recent enhancements improved ProMOL's usability, speed, and result presentation.
  • The tool aids in computational enzyme function prediction through motif detection.