Division of Molecular Medicine, Department of Medicine, Los Angeles County Harbor-University of California, Los Angeles, Medical Center, Torrance, CA 90509.
Division of Infectious Diseases, Department of Medicine, Los Angeles County Harbor-University of California, Los Angeles, Medical Center, Torrance, CA 90509.
Proc Natl Acad Sci U S A. 2019 Apr 2;116(14):6944-6953. doi: 10.1073/pnas.1819250116. Epub 2019 Mar 15.
Diversity of α-helical host defense peptides (αHDPs) contributes to immunity against a broad spectrum of pathogens via multiple functions. Thus, resolving common structure-function relationships among αHDPs is inherently difficult, even for artificial-intelligence-based methods that seek multifactorial trends rather than foundational principles. Here, bioinformatic and pattern recognition methods were applied to identify a unifying signature of eukaryotic αHDPs derived from amino acid sequence, biochemical, and three-dimensional properties of known αHDPs. The signature formula contains a helical domain of 12 residues with a mean hydrophobic moment of 0.50 and favoring aliphatic over aromatic hydrophobes in 18-aa windows of peptides or proteins matching its semantic definition. The holistic α-core signature subsumes existing physicochemical properties of αHDPs, and converged strongly with predictions of an independent machine-learning-based classifier recognizing sequences inducing negative Gaussian curvature in target membranes. Queries using the α-core formula identified 93% of all annotated αHDPs in proteomic databases and retrieved all major αHDP families. Synthesis and antimicrobial assays confirmed efficacies of predicted sequences having no previously known antimicrobial activity. The unifying α-core signature establishes a foundational framework for discovering and understanding αHDPs encompassing diverse structural and mechanistic variations, and affords possibilities for deterministic design of antiinfectives.
α-螺旋宿主防御肽（αHDPs）的多样性通过多种功能有助于抵御广谱病原体。因此，即使是基于人工智能的方法，也很难确定 αHDPs 之间常见的结构-功能关系，这些方法旨在寻找多因素趋势，而不是基础原理。在这里，生物信息学和模式识别方法被应用于从氨基酸序列、生化和三维特性中识别出真核 αHDPs 的统一特征。该特征公式包含一个 12 个残基的螺旋结构域，平均疏水性矩为 0.50，在匹配其语义定义的肽或蛋白质的 18 个氨基酸窗口中，优先选择脂肪族而非芳香族疏水性。整体的 α 核心特征包含了 αHDPs 的现有物理化学性质，并与独立的基于机器学习的分类器的预测强烈收敛，该分类器识别在靶膜中诱导负高斯曲率的序列。使用 α 核心公式进行查询可在蛋白质组学数据库中识别出 93%的所有注释的 αHDPs，并检索到所有主要的 αHDP 家族。合成和抗菌测定证实了具有先前未知抗菌活性的预测序列的功效。统一的 α 核心特征为发现和理解涵盖多种结构和机制变化的 αHDPs 建立了一个基础框架，并为抗感染药物的确定性设计提供了可能性。
