We use theoretical and computational techniques to help solve biological and medical problems. The current research topics can be grouped into the following five categories:Gene discoveryWe analyze the mRNA and Expressed Sequence Tag (EST) DNA sequence databases to discover genes that are specifically expressed in a particular organ or tumor. The products of such genes can potentially be used as targets for delivery of antitumor agents, for anticancer vaccine development, and for tumor imaging. We also utilize these databases to discover novel fusion genes resulting from chromosomal rearrangements, which are frequently involved in carcinogenesis. This is a collaborative effort with Dr. Ira Pastan's Molecular Biology group, who experimentally verifies and characterizes these genes.Comparative analysis of genes and genomesComparison of human genes with their evolutionarily related homologs provides invaluable clues for the biological function of the proteins they encode. We collect, and attempt to construct the evolutionary history of, the homologs of the human genes identified by our Gene Discovery program. We also perform systematic search of human-specific mutations that occurred after the Homo-Pan divergence by comparison of the human and the chimpanzee genome sequences. The human-specific genetic alterations should be responsible for the generation of human-specific traits.ImmunotoxinImmunotoxins are man-made molecules constructed by joining an anticancer antibody and a suitable toxin, in our case, the pseudomonas exotoxin A. In all molecules under current active consideration, the antibody part is truncated to only the antigen-binding Fv portion of the molecule. The toxin part is modified to delete its own receptor-binding domain. Ideally, these molecules will bind only to the target cancer cells and kill them. Dr. Pastan's group has made many such molecules, each of which has a specific antibody for a particular cancer. Some of these have been or are being tested in phase I clinical trials. We study these molecules and attempt to find ways to improve their properties as an effective drug. This is also a collaborative work with Dr. Pastan's laboratory.Protein structure and modelingWe have a long-standing interest in surface areas, volumes, cavities, and stability of protein structures. We build models of the three-dimensional structure of protein molecules by homology modeling and by using both the in-house and publicly available fold recognition programs. A predicted structure often helps determine the function of the new genes that we find from the gene discovery program. We also develop tools for these operations. Currently, we are working on (1) improving our automatic protein structure alignment/search algorithm; (2) comparing the results of two very different such algorithms to the manually procured world-standard protein classification database, SCOP, in collaboration with Dr. Peter Munson's group at the Center for Information Technology (CIT) of NIH and with Drs. Jean Garnier and Jean-Francois Gibrat of the Institut National de la Recherche Agronomique, Jouy-en-Josas, France; (3) developing simple graphical means of displaying secondary structure and their interaction pattern; and (4) developing a protein modeling workbench that will enable one to perform various tasks in one convenient package, including construction, prediction, viewing, comparison, modification, and manipulation of protein structures. We also occasionally collaborate with other scientists on protein structure modeling projects.HydrophobicityWe study the phenomenon of hydrophobicity by means of statistical thermodynamics. The hydrophobic effect is believed to be one of the main forces that determine the structure, stability, and interaction of protein and other biologically important molecules. This research is done in collaboration with Prof. Giuseppe Graziano at Universit del Sannio, Benevento, Italy.

Agency
National Institute of Health (NIH)
Institute
Division of Basic Sciences - NCI (NCI)
Type
Intramural Research (Z01)
Project #
1Z01BC008759-14
Application #
7291935
Study Section
(LMB)
Project Start
Project End
Budget Start
Budget End
Support Year
14
Fiscal Year
2005
Total Cost
Indirect Cost
Name
Basic Sciences
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Das, Sudipto; Hahn, Yoonsoo; Walker, Dawn A et al. (2008) Topology of NGEP, a prostate-specific cell:cell junction protein widely expressed in many cancers of different grade level. Cancer Res 68:6306-12
Grigoryev, Dmitry N; Ma, Shwu-Fan; Shimoda, Larissa A et al. (2007) Exon-based mapping of microarray probes: recovering differential gene expression signal in underpowered hypoxia experiment. Mol Cell Probes 21:134-9
Hahn, Yoonsoo; Jeong, Sangkyun; Lee, Byungkook (2007) Inactivation of MOXD2 and S100A15A by exon deletion during human evolution. Mol Biol Evol 24:2203-12
Das, Sudipto; Hahn, Yoonsoo; Nagata, Satoshi et al. (2007) NGEP, a prostate-specific plasma membrane protein that promotes the association of LNCaP cells. Cancer Res 67:1594-601
Bera, Tapan K; Saint Fleur, Ashley; Lee, Yoomi et al. (2006) POTE paralogs are induced and differentially expressed in many cancers. Cancer Res 66:52-6
Hahn, Yoonsoo; Lee, Byungkook (2006) Human-specific nonsense mutations identified by genome sequence comparisons. Hum Genet 119:169-78
Sam, Vichetra; Tai, Chin-Hsien; Garnier, Jean et al. (2006) ROC and confusion analysis of structure comparison methods identify the main causes of divergence from manual protein classification. BMC Bioinformatics 7:206
Hahn, Yoonsoo; Bera, Tapan K; Pastan, Ira H et al. (2006) Duplication and extensive remodeling shaped POTE family genes encoding proteins containing ankyrin repeat and coiled coil domains. Gene 366:238-45
Egland, Kristi A; Liu, Xiu Fen; Squires, Stephen et al. (2006) High expression of a cytokeratin-associated protein in many cancers. Proc Natl Acad Sci U S A 103:5929-34
Hahn, Yoonsoo; Lee, Byungkook (2005) Identification of nine human-specific frameshift mutations by comparative analysis of the human and the chimpanzee genome sequences. Bioinformatics 21 Suppl 1:i186-94

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