Abstract
Information of protein quaternary structure can help to understand the biological functions of proteins. Because wet-lab experiments are both time-consuming and costly, we adopt a novel computational approach to assign proteins into 10 kinds of quaternary structures. By coding each protein using its biochemical and physicochemical properties, feature selection was carried out using Incremental Feature Selection (IFS) method. The thus obtained optimal feature set consisted of 97 features, with which the prediction model was built. As a result, the overall prediction success rate is 74.90% evaluated by Jackknife test, much higher than the overall correct rate of a random guess 10% (1/10). The further feature analysis indicates that protein secondary structure is the most contributed feature in the prediction of protein quaternary structure.
Keywords: Biochemical properties, Incremental Feature Selection;, Maximum Relevance, Minimum Redundancy, Physicochemical properties, Protein quaternary structure, Jackknife test, SVM, FDOD, NNA, mRMR, MaxRel feature, Predator, PredAcc, Peng's study
Protein & Peptide Letters
Title: Prediction of Protein Quaternary Structure with Feature Selection and Analysis Based on Protein Biological Features
Volume: 19 Issue: 1
Author(s): Le-Le Hu, Kai-Yan Feng, Lei Gu and Xiao-Jun Liu
Affiliation:
Keywords: Biochemical properties, Incremental Feature Selection;, Maximum Relevance, Minimum Redundancy, Physicochemical properties, Protein quaternary structure, Jackknife test, SVM, FDOD, NNA, mRMR, MaxRel feature, Predator, PredAcc, Peng's study
Abstract: Information of protein quaternary structure can help to understand the biological functions of proteins. Because wet-lab experiments are both time-consuming and costly, we adopt a novel computational approach to assign proteins into 10 kinds of quaternary structures. By coding each protein using its biochemical and physicochemical properties, feature selection was carried out using Incremental Feature Selection (IFS) method. The thus obtained optimal feature set consisted of 97 features, with which the prediction model was built. As a result, the overall prediction success rate is 74.90% evaluated by Jackknife test, much higher than the overall correct rate of a random guess 10% (1/10). The further feature analysis indicates that protein secondary structure is the most contributed feature in the prediction of protein quaternary structure.
Export Options
About this article
Cite this article as:
Hu Le-Le, Feng Kai-Yan, Gu Lei and Liu Xiao-Jun, Prediction of Protein Quaternary Structure with Feature Selection and Analysis Based on Protein Biological Features, Protein & Peptide Letters 2012; 19 (1) . https://dx.doi.org/10.2174/092986612798472866
DOI https://dx.doi.org/10.2174/092986612798472866 |
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
-
Circulating MicroRNAs as a New Class of Biomarkers of Physiological
Reactions of the Organism to the Intake of Dietary Supplements and
Drugs
MicroRNA Caffeine: Cognitive and Physical Performance Enhancer or Psychoactive Drug?
Current Neuropharmacology Oxidative Stress and Cellular Senescence: The Key Tumor-promoting Factors in Colon Cancer and Beneficial Effects of Polyphenols in Colon Cancer Prevention
Current Cancer Therapy Reviews Recent Developments of Platinum-based Anticancer Drugs- Detection and Analysis in Biological Samples
Current Organic Chemistry Detoxification of Aflatoxin M1 (AFM1) in Dairy Base Beverages (Acidophilus Milk) by Using Different Types of Lactic Acid Bacteria-Mini Review
Current Nutrition & Food Science A Review of Eugenol-based Nanomedicine: Recent Advancements
Current Bioactive Compounds Cutoff Values of D-Dimer and FDP in Plasma for the Diagnosis of Thrombosis
Vascular Disease Prevention (Discontinued) Investigation of the Antimycobacterial Activity of 8-hydroxyquinolones
Medicinal Chemistry Electrochemical Detection of Polyphenolic Compounds in Foods and Beverages
Current Analytical Chemistry Anal Cancer: Focus on HIV-Positive Patients in the HAART Era
Current HIV Research Physicochemical and Antioxidant Characteristics of Safflower Seed Oil
Current Nutrition & Food Science The Pharmacology of Cyclic Nucleotide-Gated Channels: Emerging from the Darkness
Current Pharmaceutical Design Facile Microwave-assisted Synthesis of Various C5-modified Pyrimidine Pyranonucleosides as Potential Cytotoxic Antitumor Agents
Current Microwave Chemistry A Survey on Machine Learning Based Medical Assistive Systems in Current Oncological Sciences
Current Medical Imaging Mining PeptideAtlas for Biomarkers and Therapeutics in Human Disease
Current Pharmaceutical Design Synthesis of Some 1-(Flavon-7-yl)-4,5-dihydro-1,2,4-triazin-6(1H)-ones and Related Congeners
Letters in Organic Chemistry Breast Cancer Image Classification: A Review
Current Medical Imaging Curcumin Prevents Brain Damage and Cognitive Dysfunction During Ischemic-reperfusion Through the Regulation of miR-7-5p
Current Neurovascular Research Biological Active Ingredients of Traditional Chinese Herb Astragalus membranaceus on Treatment of Diabetes: A Systematic Review
Mini-Reviews in Medicinal Chemistry Green Synthesis, Biological Activity Evaluation, and Molecular Docking Studies of Aryl Alkylidene 2, 4-thiazolidinedione and Rhodanine Derivatives as Antimicrobial Agents
Combinatorial Chemistry & High Throughput Screening