Kyle Boone, Natalia Tjokro, Kalea N. Chu, Casey Chen, Malcolm L. Snead, C. Tamerler
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Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.","PeriodicalId":502488,"journal":{"name":"Frontiers in Dental Medicine","volume":"12 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression\",\"authors\":\"Kyle Boone, Natalia Tjokro, Kalea N. Chu, Casey Chen, Malcolm L. Snead, C. Tamerler\",\"doi\":\"10.3389/fdmed.2024.1372534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. 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引用次数: 0
摘要
种植体周围炎是一种复杂的感染性疾病,表现为种植体周围牙槽骨的逐渐丧失以及与微生物菌群失调相关的炎症反应。由于抗生素耐药性的威胁、对微生物群落中病原体和共生菌的非选择性抑制以及潜在的严重全身性后遗症,使用抗生素治疗种植体周围炎备受争议。因此,种植体周围炎的传统治疗方法包括通过非手术或手术方法进行机械清创,并辅以局部杀菌剂。因此,目前的治疗方案可能无法防止复发,因为病原体要么不受影响,要么在治疗后迅速重新出现。要想成功缓解种植体周围炎的病情发展,需要一种特定的治疗模式,这种模式能够针对关键病原体,并恢复细菌群落平衡,使其向共生菌种发展。抗菌肽(AMP)具有细菌特异性和靶向抑制活性,有望成为替代疗法。然而,肽序列空间表现出复杂的关系,如序列的稀疏向量编码,包括描述肽抗菌活性的组合和离散函数。在本文中,我们生成了一个透明的机器学习(ML)模型,该模型基于粗糙集理论,利用对 AMPs 水理特征的简单总结来识别序列-功能关系。根据肽对不同种类细菌的不同活性来比较肽的水理特征,可以提高抗菌靶向的可预测性。通过遗传算法丰富序列多样性,我们生成了大量用于选择性靶向病原体的候选 AMPs,并利用分类粗糙集预测了它们的活性。经验性生长抑制数据会反复反馈到我们的 ML 训练中,以生成新的多肽,从而形成越来越严格的规则,确定哪些多肽符合特定细菌菌株的目标抑制水平。随后,我们对得分最高的候选肽进行了经验测试,以确定它们对种植体周围炎的主要病原体和辅助病原体以及一种口腔共生细菌的抑制作用。结果表明,一种名为 VL-13 的新型多肽对关键病原体具有选择性抑制作用。考虑到每年口腔种植体的数量不断增加以及疾病进展的复杂性,种植体周围疾病的发病率持续上升。我们的方法为开发基于抗菌肽的疗法提供了透明的、由 ML 支持的途径,这些疗法以微生物群落的变化为目标,可对疾病进展产生有益影响。
Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression
Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.