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Lichtarge Computational Biology Lab

Houston, Texas

Lichtarge Computational Biology Lab
Lichtarge Computational Biology Lab
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Evolution-Directed Studies of Protein Functional Surfaces

    For more rotating images of functional sites, visit our sample traces page .

    The interactions of proteins amongst themselves and with other macromolecules control cellular networks and govern life. In order to decipher these interactions and to identify specific and appropriate targets for drugs, our work aims to identify the functional sites that mediate protein binding and to predict how their component amino acids contribute to functional specificity. Areas of special focus are the G protein pathway, transcriptional regulation, and phosphorylation, in which we seek to understand the molecular basis of signaling by analyzing the interfaces of G Protein-Coupled Receptors, of nuclear receptors, and of kinases.

    Our approach is based on the Evolutionary Trace (ET, Lichtarge 1996, Mihalek 2004). This method entails ranking the relative functional importance of amino acids in a protein sequence by correlating their variations during evolution with divergences in the phylogenetic tree of that sequence family. We have shown that the best-ranked residues typically cluster spatially in the protein structure (Madabushi 2002) and thereby reveal the location of functional sites (Yao 2003). This approach is similar to laboratory-based mutational scanning, but it exploits the vast number of mutations and assays that were already tested through evolution, and that are increasingly retrievable from sequence and structure databases.

    The first line of research is to map the molecular determinants of interaction in proteins of pharmaceutical interest. For example, with respect to G protein signaling, a G protein site predicted to interface with the GPCR was later confirmed by mutational scanning. Then, in collaboration with the Wensel Lab, we predicted that a novel site in RGS would bind the visual signaling effector PDE (Sowa 2000), and confirmed this was the case experimentally (Sowa 2001). An interesting finding were two RGS residues shown to control the rate of GTP hydrolysis by G from a distance. They form an evolutionarily conserved allosteric switch that is in part responsible for turning off G protein signaling. We recently extended this strategy to propose a set of residues thought to mediate signal transduction across all GPCR from Class A (Madabushi et al. 2004). In Nuclear receptors we identified novel modes of dimerization (Gu et al 2005). These studies demonstrate the use of evolution-directed engineering of protein and peptides to explore the molecular basis of protein function and interaction.

    A second line of research is to generalize evolution-directed analysis of functional surfaces in order to characterize protein functional sites on a proteomic scale. Recent studies showed that functional sites can be predicted reliably and accurately in proteins with diverse functions, structures, and evolutionary histories (Madabushi 2002). We are now further testing the extent to which this strategy can be extended to nearly all proteins. In practice, this will be useful for drug targeting and design, for protein modeling and engineering, and for elucidating the function of new genes and of new protein structures (Lichtarge and Sowa, 2002). More fundamentally, we hope to gain an unprecedented view into the sequence determinants of protein structure and function from the perspective of evolution.

    Selected References:

    Lichtarge and Wilkins (2010) Current Op. Struct. Biol.
    Rodriguez et al (2010) PNAS 107:7787.
    Erdin and Ward et al (2010) J. Mol. Biol. 396:1451.
    Ward et al (2009) Bioinformatics 25:1426.
    Kristensen and Ward et al (2008) BMC Bioinformatics 9:17.
    Ribes-Zamora et al (2007) Nat. Struct. Mol. Biol. 14(4):301-7.
    Shenoy et al (2006) J. Biol. Chem. 281(2):1261-73.
    Raviscioni et al (2005) J. Mol. Bio. 350: 402-15.
    Madabushi et al (2004) J. Biol. Chem. 279:8126-32.
    Mihalek et al (2004) J. Mol. Biol. 336:1265-82.
    Yao and Kristensen et al (2003) J. Mol. Bio. 326:255-61.
    Madabushi et al (2002) J. Mol. Biol. 316:139-53.
    Lichtarge and Sowa (2002) Current Op Struct Biol.12:21-27.
    Sowa et al (2001) Nature Struc. Biol. 8(3):234-7.
    Lichtarge et al (1996) J. Mol. Biol. 257(2):342-58.