Prediction of modulators of pyruvate kinase in smiles text using aprori methods

  • Authors:

  • Jason S. Caronna;Rojita Sharma;Jonathan D. Marra;Virginia L. Iuorno;Katherine G. Herbert;Jeffrey H. Toney

  • Affiliations:

  • Montclair State University;Montclair State University;Montclair State University;Montclair State University;Montclair State University;Montclair State University

  • Venue:
  • Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
  • Year:
  • 2007

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Abstract

Pyruvate kinase is an enzyme that catalyzes the formation of pyruvate from phosphenolpyruvate in glycolysis. There is a wealth of data on the activity of certain molecules and their effects on pyruvate kinase. This project aims to create an application that uses a pyruvate kinase dataset to determine the nature of unidentified molecules; whether or not they would be activators or inhibitors of this enzyme. This application uses an Apriori algorithm to identify or predict modulators of pyruvate kinase. This initial study focuses on simplified molecular input line entry specification (SMILES) text as target data to be mined. The three dimensional structure of pyruvate kinase is known and accessible though the Protein Data Bank (e.g., PDB code IA3W).