Date of Award

Fall 2002

Document Type

Thesis

Degree Name

Master of Science in Computational Biology - (M.S.)

Department

Federated Department of Biological Sciences

First Advisor

Michael Recce

Second Advisor

Barry Cohen

Third Advisor

Alex Elbrecht

Abstract

Computational methods for identifying and screening the most promising drug receptor candidates in the human genome are of great interest to drug discovery researchers. Successful methods will accurately identify and narrow the field of potential drug receptor candidates. This study details one such method.

The method described here begins with the assumption that novel drug receptors have high sequence similarity to established drug receptors. The similarity search program FASTA3 aligns translated sequences of the human genome to known drug receptor sequences and ranks these alignments by measuring their statistical significance. Query results returned by FASTA3 are assembled into "in-silico proteins" or artificially generated homologs of known drug receptors. A second similarity search program, BLASTP, aligns in-silico proteins with a protein database, and also ranks alignments based on statistical significance. A potentially valuable in-silico protein identifies its generating drug receptor as the top-ranking result returned from the BLASTP search, and may represent a new family member of a particular group of drug receptors.

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