  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. The AC of small molecules has been extensively
investigated but limited knowledge is accumulated about the AC phenomenon in
peptides with canonical amino acids. This study introduces a quantitative
definition and benchmarking framework AMPCliff for the AC phenomenon in
antimicrobial peptides (AMPs) composed by canonical amino acids. 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. 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. Our analysis reveals that these models are capable of detecting AMP AC
events and the pre-trained protein language ESM2 model demonstrates superior
performance across the evaluations. The predictive performance of AMP activity
cliffs remains to be further improved, considering that ESM2 with 33 layers
only achieves the Spearman correlation coefficient=0.50 for the regression task
of the MIC values on the benchmark dataset. Source code and additional
resources are available at https://www.healthinformaticslab.org/supp/ or
https://github.com/Kewei2023/AMPCliff-generation.
