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.
Keywords: Autoimmune disease, drug discovery, FAMS, principal component analysis, promoter methylation.
Protein & Peptide Letters
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.
Export Options
About this article
Cite this article as:
Ishida Shigeharu, Umeyama Hideaki, Iwadate Mitsuo and Taguchi Y-h., Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery, Protein & Peptide Letters 2014; 21 (8) . https://dx.doi.org/10.2174/09298665113209990052
DOI https://dx.doi.org/10.2174/09298665113209990052 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Oral Agents in Multiple Sclerosis
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry Biologics as Treatment for Systemic Lupus: Great Efforts, Sobering Results, New Challenges
Current Drug Discovery Technologies Polymeric Drug Delivery Approaches for Colon Targeting: A Review
Drug Delivery Letters Targeting Cytosolic Phospholipase A2α for Novel Anti-Inflammatory Agents
Current Medicinal Chemistry The Potential of Selected Prostanoid Receptors as Targets in a New Therapeutic Strategy for Allergy and Immune Diseases
Current Drug Safety APO2L/TRAIL: New Insights in the Treatment of Autoimmune Disorders
Recent Patents on Inflammation & Allergy Drug Discovery Inhibitors Targeting the LFA-1/ICAM-1 Cell-Adhesion Interaction: Design and Mechanism of Action
Current Pharmaceutical Design Role of Renin-Angiotensin System in Inflammation, Immunity and Aging
Current Pharmaceutical Design Anti-Inflammatory Approaches that Target the Chemokine Network
Recent Patents on Inflammation & Allergy Drug Discovery Small Molecule Inhibitors of Phosphoinositide 3-Kinase (PI3K) δ and γ
Current Topics in Medicinal Chemistry VEGFR1 Signaling Regulates IL-4-Mediated Arginase 1 Expression in Macrophages
Current Molecular Medicine Current and Future Therapies Targeting the Immune System in Multiple Sclerosis
Current Pharmaceutical Biotechnology Monocyte Dependent Regulation of Autoimmune Inflammation
Current Molecular Medicine MICA Molecules in Disease and Transplantation, a Double-Edged Sword?
Current Immunology Reviews (Discontinued) Central Self - Tolerance by Thymic Presentation of Self - Antigens and Autoimmunity
Current Medicinal Chemistry - Immunology, Endocrine & Metabolic Agents Sexual Dimorphism in Autoimmune Disease
Current Molecular Medicine Advances in the Treatment of Autoimmune Diseases; Cellular Activity, Type-1/Type-2 Cytokine Secretion Patterns and their Modulation by Therapeutic Peptides
Current Medicinal Chemistry Methotrexate in the Treatment of Psoriasis and Rheumatoid Arthritis: Mechanistic Insights, Current Issues and Novel Delivery Approaches
Current Pharmaceutical Design Gluten-dependent Intestinal Autoimmune Response
Current Pharmaceutical Design A Quantitative Structure-Activity Relationship Study on Some Series of Potassium Channel Blockers
Medicinal Chemistry