bioRxiv · 10.1101/2020.12.17.423359
AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning
Abstract
MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. ResultsOur results show that our prediction model is superior to the existing AMP predictors. AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contactcylin@iis.sinica.edu.tw
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Lin, T.-T., Yang, L.-Y., Lu, I.-H., Cheng, W.-C., Hsu, Z.-R., Chen, S.-H., Lin, C.-Y.. 2020-12-18. AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning. https://doi.org/10.1101/2020.12.17.423359
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