Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery
Shigeharu Ishida, Hideaki Umeyama, Mitsuo Iwadate and Y-h. TaguchiAffiliation:
Department of Physics, Chuo University, Tokyo 112-8551, Japan, and Department of Biological Sciences, Chuo University, Tokyo 112-8551, Japan.
AbstractAutoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed.
Autoimmune disease, drug discovery, FAMS, principal component analysis, promoter methylation.
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