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Lichtarge Computational Biology Lab

Houston, Texas

Lichtarge Computational Biology Lab
Lichtarge Computational Biology Lab
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Lichtarge Computational Biology Lab

Our lab in the Department of Molecular and Human Genetics 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.

Current computational projects focus on the refinement and automation of the Evolutionary Trace, on the computational annotation of function, and on data integration on a proteomic scale. Our experimental focus is on the molecular mechanisms of G protein signaling, nuclear receptors, and kinases, through collaborations, and on interactions among essential bacterial gene products, in our own wetlab. Since all these proteins are of pharmaceutical interest, we hope that computation and design can together lead to novel drug targets and, eventually, to novel approaches for the development of therapeutics.

For further information, see this detailed description of our work on computational functional site prediction. Also, we have made tools available to access our Evolutionary Trace Server: the Evolutionary Trace Viewer and the Evolutionary Trace report_maker.


Evolutionary Action
Evolutionary Trace
Evolutionary Trace Annotator
GPCR Difference ET

In the news:

Exploring gene function and parasite–host protein interactions

Nature Methods

Nature Reviews Genetics


Hypothesis Generation Based on Mining the Scientific Literature

Washington Post CBSNYTimesThe Economist

ReutersBloombergNew ScientistTIME Magazine

Albany Buissiness ReviewEconomic TimesLive ScienceFast Company

IT News

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