AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

作者：Kewei Li, Yuqian Wu, Yinheng Li, Yutong Guo, Yanwen Kong, Yan Wang, Yiyang Liang, Yusi Fan, Lan Huang, Ruochi Zhang, Fengfeng Zhou · 发表于：Journal of Advanced Research · 年份：2025 · DOI：10.1016/j.jare.2025.04.046 · 被引用次数：6 · 研究领域：Antimicrobial Peptides and Activities、Machine Learning in Bioinformatics、vaccines and immunoinformatics approaches

INTRODUCTION: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. OBJECTIVES: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. METHODS: This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. RESULTS: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ...