Supporting Data for manuscript entitled "sAMP-VGG16: Force-field assisted image-based deep neural network 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 relies 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）已成为极具前景的抗生素替代疗法。抗菌肽的设计路径涵盖从实验试错法到合成杂合肽库的各类方法。鉴于开发有效抗菌肽的过程成本极高且耗时良久，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，肽序列的编码特征化工作依托四类方法：(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，该模型采用基于德鲁德（Drude）极化力场原子类型的特征编码方式，相较传统特征向量可更高效地捕捉肽的相关性质。所提出的预测模型在抗菌肽预测任务中展现出优异的准确性与效率，可作为新一代筛选方法用于新型抗菌肽的预测。