HIV-1 Protease Cleavage Site Prediction Based on Two-Stage Feature Selection Method
| Autor: | Xiao-Cheng Yuan, Juan Ding, Qiang Su, Bing Niu, Jing-Yuan Yin, Haipeng Li, Wencong Lu, Preston Roeper, Chunrong Peng |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
medicine.medical_treatment
Human immunodeficiency virus (HIV) HIV Infections Feature selection Computational biology Cleavage (embryo) medicine.disease_cause Bioinformatics Biochemistry Structure-Activity Relationship HIV Protease HIV-1 protease Structural Biology Jackknife test medicine Humans Amino Acid Sequence AdaBoost Binding Sites Protease biology HIV General Medicine Models Chemical biology.protein Selection method Algorithms |
| Zdroj: | Protein and Peptide Letters. 20:290-298 |
| ISSN: | 0929-8665 |
| DOI: | 10.2174/092986613804910707 |
| Popis: | Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and ef- fective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in pro- teins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty impor- tant biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By us- ing the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jack- knife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, re- spectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease. |
| Databáze: | OpenAIRE |
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