A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein in HIV-infected patients

作者：Mohammad Fili, Guiping Hu, Changze Han, Alexa Kort, John Trettin, Hillel Haim · 发表于：Algorithms for Molecular Biology · 年份：2023 · DOI：10.1186/s13015-023-00228-0 · 被引用次数：3 · 研究领域：Machine Learning in Bioinformatics、Genomics and Rare Diseases、vaccines and immunoinformatics approaches

BACKGROUND: Therapeutics against the envelope (Env) proteins of human immunodeficiency virus type 1 (HIV-1) effectively reduce viral loads in patients. However, due to mutations, new therapy-resistant Env variants frequently emerge. The sites of mutations on Env that appear in each patient are considered random and unpredictable. Here we developed an algorithm to estimate for each patient the mutational state of each position based on the mutational state of adjacent positions on the three-dimensional structure of the protein. METHODS: We developed a dynamic ensemble selection algorithm designated k-best classifiers. It identifies the best classifiers within the neighborhood of a new observation and applies them to predict the variability state of each observation. To evaluate the algorithm, we applied amino acid sequences of Envs from 300 HIV-1-infected individuals (at least six sequences per patient). For each patient, amino acid variability values at all Env positions were mapped onto the three-dimensional structure of the protein. Then, the variability state of each position was estimated by the variability at adjacent positions of the protein. RESULTS: The proposed algorithm showed higher performance than the base learner and a panel of classification algorithms. The mutational state of positions in the high-mannose patch and CD4-binding site of Env, which are targeted by multiple therapeutics, was predicted well. Importantly, the algorithm outperformed other classificat...