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A Random Forest Model for Peptide Classification Based on Virtual Docking Data.
Feng, Hua; Wang, Fangyu; Li, Ning; Xu, Qian; Zheng, Guanming; Sun, Xuefeng; Hu, Man; Xing, Guangxu; Zhang, Gaiping.
Afiliación
  • Feng H; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Wang F; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Li N; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Xu Q; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Zheng G; Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou 450046, China.
  • Sun X; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Hu M; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Xing G; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
  • Zhang G; Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China.
Int J Mol Sci ; 24(14)2023 Jul 13.
Article en En | MEDLINE | ID: mdl-37511165
The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Bosques Aleatorios Tipo de estudio: Clinical_trials / Prognostic_studies Idioma: En Revista: Int J Mol Sci Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Bosques Aleatorios Tipo de estudio: Clinical_trials / Prognostic_studies Idioma: En Revista: Int J Mol Sci Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza