AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning
Lin, T.-T.; Yang, L.-Y.; Lu, I.-H.; Cheng, W.-C.; Hsu, Z.-R.; Chen, S.-H.; Lin, C.-Y.
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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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