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）已成为极具前景的抗生素替代疗法。抗菌肽的设计方法涵盖从实验试错法到合成杂合肽库的多种路径。为解决有效抗菌肽设计过程中成本高昂、耗时漫长的痛点，近年来涌现出诸多用于抗菌肽预测的计算与机器学习工具。总体而言，这类工具在对肽序列进行编码时，其特征化流程主要基于四类方法：(a) 氨基酸组成、(b) 理化性质、(c) 序列相似性，以及 (d) 结构性质。本研究提出了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极化力场原子类型的特征编码方式，相较于传统特征向量，能够更高效地捕捉肽链的相关性质。所提出的预测模型在抗菌肽识别任务中展现出优异的准确率与效率，可作为筛选新型抗菌肽的下一代筛查方法。