Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation |
| |
| Institution: | 1. State Key Laboratory of Cotton Biology, School of Life Sciences, School of Computer and Information Engineering, Henan University, Kaifeng 4750002, China;2. Kaifeng Academy of Agriculture and Forestry, Kaifeng 475000, China;1. Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China;2. School of Public Health, Southwest Medical University, Luzhou 646000, China;3. Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, China;4. Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, Southwest Medical University, Luzhou 646000, China;1. CAS Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;2. Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;3. Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China |
| |
| Abstract: | Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question). |
| |
| Keywords: | Antimicrobial peptides Host defense peptides Selectivity Structure-activity relationship QSAR Data science |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
