中文摘要：抗生素的过度使用导致了抗生素耐药性的出现，因此，抗菌肽（ABPs）受到了广泛关注。实验室鉴定自然资源中有效的ABP成本高且耗时，因此需要开发能够识别蛋白质序列中的新ABP的电子模型，用于化学合成和测试。本研究提出了一种深度学习分类器Deep-ABPpred，它可以识别蛋白质序列中的ABP。利用基于word2vec的氨基酸水平特征的双向长短期记忆算法开发了Deep-ABPpred。研究表明，Deep-ABPpred在测试数据集和独立数据集上都优于其他先进的ABP分类器，精度分别达到约97和94%。Deep-ABPpred的高精度表明它在提出新的ABP合成和实验方面具有一定的适用性。利用Deep-ABPpred，鉴定了链球菌噬菌体尾部蛋白序列ABP，并在实验室化学合成后对其体外活性进行了测试。这些ABP对选定的革兰氏阳性和革兰氏阴性细菌显示出强大的抗菌活性，证实了该模型在鉴定蛋白质序列中新ABP的能力。在此基础上，还开发了在线预测服务器，可在https://abppred.anvil.app/上自由访问。该web服务器将蛋白质序列作为输入，输出高概率（>0.95）的ABP。
外文摘要：The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.
外文关键词：AMR；antibacterial peptides；deep learning；BiLSTM；Streptococcus；bacteriophage
作者：Sharma, R；Shrivastava, S；Singh, SK；Kumar, A；Saxena, S；Singh, RK
作者单位：IIT BHU；IVR；Indian Vet Res Inst
期刊名称：BRIEFINGS IN BIOINFORMATICS
期刊影响因子：8.99
出版年份：2021
出版刊次：5
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