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Department of Biochemistry and Molecular Biology

Houston, Texas

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Verna and Marrs McLean Department of Biochemistry and Molecular Biology
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Olivier Lichtarge, M.D., Ph.D.

Photograph of Dr. Lichtarge

Professor, Molecular & Human Genetics,
Biochemistry & Molecular Biology;
Programs in Developmental Biology, Cell & Molecular Biology, and Structural & Computational Biology, and Molecular Biophysics

Director, Center of Computational and Integrative Biomedical Research

E-mail: lichtarge@bcm.edu

Lab Website

Education and Awards

  • B.S., McGill University, Canada, 1980
  • Ph.D., Stanford University, 1987
  • M.D., Stanford University, 1990
  • Resident in Internal Medicine, University of California at San Francisco, 1996
  • Fellow in Endocrinology, University of California at San Francisco, 1996
  • Postdoc, University of California at San Francisco, 1997

Our lab marries computation with experiments to study three areas of protein structure-function: the molecular basis of protein catalysis and interaction, the design of peptides and proteins, and the annotation of protein sequence and structure. In each case, our long-term goals are to engineer proteins or peptides to probe and then rationally disrupt protein pathways.

To guide experiments, we rely on an integrated computational analysis of the evolution of protein sequences, structures, and functions. This phylogenomic strategy is called the Evolutionary Trace (ET) and, most simply, it assigns to each sequence residue a relative score of “functional importance”. From this we can formulate hypotheses on the molecular determinants of activity and specificity, and rationally target experiments to the most relevant sites of a protein.

In the G protein signaling pathway, one example of this approach is a G protein-coupled receptor engineered to signal through the ERK/MAPK pathway but not through the typical G protein mechanism (Shenoy et al., 2006). Another example is an RGS7 mutant designed to shut off G protein signaling as if it were and RGS9 (Sowa et al., 2001). Likewise in nuclear hormone receptors and bHLH transcription factors, targeted mutations enabled us to swap DNA binding (Raviscioni et al., 2005) and in vivo proneural development (Quan et al., 2004), respectively. Finally, Evolutionary Trace-based peptides let us mimic a protein-protein interaction surface and disrupt normal binding in Germ Cell Nuclear Transcription Factor (GCNF4).

Thus while our computational aims are to improve the general annotation of function on a proteomic scale, our experimental projects aim to characterize molecular mechanisms in protein families of intense pharmaceutical interest. The hope is that computation and design, together, can lead to novel drug targets and to novel approaches for the development of therapeutics.

Selected Publications:

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