PDF(Pubmed)
Abstract：
The use of anti-inflammatory peptides (AIPs) as an alternative therapeutic approach for inflammatory diseases holds great research significance. Due to the high cost and difficulty in identifying AIPs with experimental methods, the discovery and design of peptides by computational methods before the experimental stage have become promising technology. In this study, we present BertAIP, a bidirectional encoder representation from transformers (BERT)-based method for predicting AIPs directly from their amino acid sequence without using any other information. BertAIP implements a BERT model to extract features of a protein, and uses a fully connected feed-forward network for AIP classification. It was constructed and evaluated using the AIP datasets that were reconstructed from the latest Immune Epitope Database. The experimental results showed that BertAIP achieved an accuracy of 0.751 and a Matthews correlation coefficient of 0.451, which were higher than other commonly used methods. The results of the independent test suggested that BertAIP outperformed the existing AIP predictors. In addition, to enhance the interpretability of BertAIP, we explored and visualized the amino acids that the model considered important for AIP prediction. We believe that the BertAIP proposed herein will be a useful tool for large-scale screening and identifying novel AIPs for drug development and therapeutic research related to inflammatory diseases.
摘要：
使用抗炎肽（AIPs）作为炎症性疾病的替代治疗方法具有重要的研究意义。由于用实验方法识别AIP的成本高，难度大，在实验阶段之前通过计算方法发现和设计肽已成为有前途的技术。在这项研究中,我们介绍BertAIP,一种基于转换器（BERT）的双向编码器表示方法，用于直接从氨基酸序列预测AIP，而无需使用任何其他信息。BertAIP实现BERT模型来提取蛋白质的特征，并使用完全连接的前馈网络进行AIP分类。它是使用从最新的免疫表位数据库重建的AIP数据集构建和评估的。实验结果表明，BertAIP的准确率为0.751，马修斯相关系数为0.451，高于其他常用方法。独立测试的结果表明，BertAIP优于现有的AIP预测因子。此外,为了增强BertAIP的可解释性，我们探索并可视化了模型认为对AIP预测重要的氨基酸。我们相信本文提出的BertAIP将是用于大规模筛选和鉴定新型AIP的有用工具，用于与炎性疾病相关的药物开发和治疗研究。
