TITLE:

STRUCTURAL-BASED ANALYSIS OF DIHYDROFOLATE REDUCTASE EVOLUTION

DATE:

Friday, April 22nd, 2011

TIME:

3:30 PM

LOCATION:

GMCS 214

SPEAKER:

David Hecht, Professor of Chemistry at Southwestern College

ABSTRACT:

The evolution of dihydrofolate reductase (DHFR) was studied through a comprehensive structure-based analysis. An amino acid sequence alignment was generated from a superposition of experimentally determined x-ray crystal structures of wild-type (wt) DHFR from the Protein Data Bank (PDB). Using this structure-based alignment of DHFR, a metric was generated for the degree of conservation at each alignment site – not only in terms of amino acid residue, but also secondary structure, and residue class. A phylogenetic tree was generated using the alignment that compared favorably with the canonical phylogeny. This structure-based alignment was used to confirm that the degree of conservation of active-site residues in terms of both sequence as well as structure was significantly greater than non-active site residues. These results can be used in helping to understand the likely future evolution of DHFR in response to novel therapies.

David Hecht is currently a professor of chemistry at Southwestern College in Chula Vista, California. Dr. Hecht holds several additional academic appointments including: visiting researcher, U.C. Irvine EECS and adjunct faculty member, San Diego State University Dept. of Chemistry. He is an associate editor of the journal Biosystems and a senior member of the IEEE. He received the B.S. degree in biochemistry from Rutgers University, the M.S. degree in chemistry from U.C. Berkeley and the Ph.D. in macromolecular structural biology and chemistry from The Scripps Research Institute in La Jolla, CA. He has worked 10+ years in various biotech and pharmaceutical companies performing systems integration as well as data management with the goal of accelerating lead discovery and optimization processes for preclinical drug discovery and development. His current research interests include: structural bio- & chemi-informatics analyses of protein-ligand interactions as well as development of computational approaches to accelerate drug discovery and development. These include developing novel applications of semantic programming, machine learning and neural nets, evolutionary algorithms, and in-silico screening (e.g. docking algorithms).

HOST:

Jose E. Castillo

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