Datasets are provided here for the purpose of reproducibility and future method testing. If you are looking for information about AMP sequences, you are recommended to visit other AMP databases such as ADP, CAMP, LAMP, etc. Our datasets here were collected from these databases and filtered out sequences with non-natural amino acids.
Data used for constructing the AmPEP prediction model.
Benchmark datasets from Xiao et al. (iAMP-2L) for methods comparison can be downloaded from here.
Reference: Pratiti Bhadra, Jielu Yan, Jinyan Li, Simon Fong, and Shirley W. I. Siu.* AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Scientific Reports, 1697 (2018).
Short Anti-Microbial Peptides
Data is filtered from our AmPEP dataset, include sequences only with 5-30 AA in length. This dataset is used for constructing the Deep-AmPEP30 and RF-AmPEP30 prediction models. An independent dataset was constructed as benchmark to compare model performances with other existing methods.
|Train Dataset||Test Dataset (Benchmark)|
|1529 positives and 1273 negatives *||94 positives and 94 negatives|
|*The original dataset was later found with duplicated sequences, the cleaned dataset is provided here.|
Reference: Jielu Yan, Pratiti Bhadra, Ang Li, Pooja Sethiya, Longguang Qin, Hio Kuan Tai, Koon Ho Wong, and Shirley W. I. Siu* Deep-AmPEP30: Improve short antimicrobial peptides prediction with deep learning. Molecular Therapy – Nucleic Acids, Volume 20, Pages 882-894.