背景:ADP-核糖基化是涉及调节多种细胞过程的关键修饰,包括染色质结构调节,RNA转录,细胞死亡。细菌ADP-核糖基转移酶毒素(bARTT)可作为有效的毒力因子,协调宿主细胞功能的操纵以促进细菌发病机理。尽管发挥了关键作用,由于有限的验证数据和bARTT成员之间固有的序列多样性,新型bARTT的生物信息学鉴定提出了巨大的挑战.
结果:我们提出了一种基于深度学习的模型,ARTNet,专门设计用于从细菌基因组中预测bARTT。最初,我们引入了一种有效的数据增强方法来解决训练ARTNet中的数据稀缺问题。随后,我们采用了一种数据优化策略,即利用ART相关的域子序列而不是主全序列,从而显著提高ARTNet的性能。ARTNet在重复的独立测试数据集上实现了0.9351的马修相关系数(MCC)和0.9666的F1分数(宏),在时间效率和准确性方面优于其他三种深度学习模型和六种传统机器学习模型。此外,我们通过经验证明了ARTNet在没有序列相似性的情况下预测跨域超家族的新型bARTT的能力。我们预计ARTNet将极大地促进从细菌基因组中筛选和鉴定新型bARTT。
背景:ARTNet可以在http://www上公开访问。MGC.AC.cn/ARTNet/.ARTNet的源代码可在https://github.com/zhengdd0422/ARTNet/上免费获得。
背景:补充数据可在Bioinformatics在线获得。
BACKGROUND: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.
RESULTS: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew\'s correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes.
METHODS: ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.