Supporting Data for manuscript entitled "sAMP-VGG16: A Drude Polarizable Force Field assisted Deep Transfer Learning based Prediction Model for Short Antimicrobial Peptides"

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization in these rely on approaches based on (a) amino acid composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies AMPs with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs.

近三十年来，抗菌肽（antimicrobial peptides, AMPs）已成为极具潜力的抗生素替代治疗方案。抗菌肽的设计路径覆盖从实验试错法到合成杂合肽库的多种手段。由于开发有效抗菌肽的传统流程成本高昂且耗时漫长，近年来诸多用于抗菌肽预测的计算与机器学习工具应运而生。通常而言，这类工具在对肽序列进行特征编码时，均采用以下四类方法：(a) 氨基酸组成、(b) 理化性质、(c) 序列相似性，以及(d) 结构性质。本研究提出一种基于图像的深度神经网络模型用于抗菌肽预测，其采用基于德鲁德极化力场原子类型的特征编码方案，相较传统特征向量可更高效地捕捉肽的相关特性。所提出的预测模型具备优异的预测精度与效率，可作为下一代筛选方法用于新型抗菌肽的预测与发掘。