Title:Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery
Volume: 21
Issue: 8
Author(s): Shigeharu Ishida, Hideaki Umeyama, Mitsuo Iwadate and Y-h. Taguchi
Affiliation:
Keywords:
Autoimmune disease, drug discovery, FAMS, principal component analysis, promoter methylation.
Abstract: Autoimmune 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.
