Professor, Baylor College of Medicine
Co-Director, Structural and Computational Biology & Molecular Biophysics Graduate Program
B.S., McGill University, Montreal, Canada, 1980
Ph.D., Stanford University, CA, 1987
M.D., Stanford University, CA, 1990
Postdoc, University of California at San Francisco, 1994-97
Structural biology, bioinformatics, and computational genomics
The primary goal of our bioinformatics laboratory is to understand how protein functional surfaces control critical events, such as binding, catalysis and active complex assembly. To address this problem typically requires exhaustive and expensive mutational analysis in the wetlab. Here instead, we analyze the mutational “experiments” already performed during evolution and recorded in sequence databases.
Specifically, we have developed a method of sequence analysis that identifies, among divergently related proteins, patterns of sequence variations that correlate with functional divergence. This evolutionary trace method (ET) ranks amino acids in a protein by their evolutionary (and presumably functional) importance. As a consequence of this ranking, it becomes possible to locate functional surfaces on a structure, probe the molecular details of active site function and specificity, and recognize cryptic functional commonalties in distantly related proteins.
We are using this new approach to probe G protein-mediated signaling, and transcriptional regulation by intracellular hormone receptors. Our focus in those systems is 1) to model and understand the mechanisms of G protein-coupled receptors; 2) to characterize interactions between these receptors and the G proteins; and 3) to decipher the origin of recognition specificity between transcriptional factors and their response elements. In turn, these systems are test beds for computational tools that can be used broadly to study helical transmembrane receptors, protein-protein interactions and protein-DNA interactions.
Most generally, we note that genome projects, growing protein structure databases and DNA chip technologies are now bringing to bear unprecedented amounts of data to fundamental problems in structural biology (protein structure prediction) and in genomics (gene function prediction). At the same time, these massive data overwhelm conventional means of analysis. For these reasons, our broad goal is to develop a new generation of bioinformatics methods, such as the evolutionary trace, that integrate sequence-structure-function data and turn them into new insights in gene expression and protein function.
Lisewski AM, Lichtarge O (2006) Rapid detection of similarity in protein structure and function through contact metric distances. Nucleic Acids Research 34:e152.
Ribes-Zamora A, Mihalek I, Lichtarge O, Bertuch AA (2007) Distinct faces of the Ku heterodimer mediate DNA repair and telomeric functions. Nature Structural & Molecular Biology 14:301-307.
Mihalek I, Res I, Lichtarge O (2007) On itinerant water molecules and detectability of protein-protein interfaces through comparative analysis of homologues. Journal of Molecular Biology 369:584-595.
Ward RM, Venner E, Daines B, Murray S, Erdin S, Kristensen DM, Lichtarge O (2009) Evolutionary Trace Annotation Server: automated enzyme function prediction in protein structures using 3D templates. Bioinformatics 25:1426-1427.
Erdin S, Ward RM, Venner E, Lichtarge O (2010) Evolutionary trace annotation of protein function in the structural proteome. Journal of Molecular Biology 396:1451-1473.
Lua RC, Lichtarge O (2010) PyETV: a PyMOL evolutionary trace viewer to analyze functional site predictions in protein complexes. Bioinformatics 26:2981-2982.
Rodriguez GJ, Yao R, Lichtarge O, Wensel TG (2010) Evolution-guided discovery and recoding of allosteric pathway specificity determinants in psychoactive bioamine receptors. Proceedings of the National Academy of Sciences USA 107:7787-7792.
Wilkins AD, Lua R, Erdin S, Ward RM, Lichtarge O (2010) Sequence and structure continuity of evolutionary importance improves protein functional site discovery and annotation. Protein Science 19:1296-1311.
Adikesavan AK, Katsonis P, Marciano DC, Lua R, Herman C, Lichtarge O (2011) Separation of recombination and SOS response in Escherichia coli RecA suggests LexA interaction sites. PLoS Genetics 7:e1002244.
Wilkins AD, Bachman BJ, Erdin S, Lichtarge O (2012) The use of evolutionary patterns in protein annotation. Current Opinion in Structural Biology 22:316-325.
Olivier Lichtarge, M.D., Ph.D.
Department of Molecular and Human Genetics
Baylor College of Medicine
One Baylor Plaza T921
Houston, Texas 77030, U.S.A.
Tel: (713) 798-5646
Fax: (713) 798-5386