NeuRiPP: Neural network identification of RiPP precursor peptides
NeuRiPP: Neural network identification of RiPP precursor peptides
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DOI：
10.1038/s41598-019-49764-z
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发表时间：
2019-09-16
影响因子：
4.6
通讯作者：
de los Santos, Emmanuel L. C.
中科院分区：
文献类型：
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作者：
de los Santos, Emmanuel L. C.
在过去的几年里，已经取得了重大进展的生物合成基因簇（BGC）编码核糖体合成和后修饰肽（RIPPs）的计算识别。这通过鉴定RiPP剪裁酶（RTE）和RiPP前体肽（PP）来完成。然而，PP的鉴定，特别是新型RiPP类别的鉴定仍然具有挑战性。为了解决这个问题，机器学习已经被用来准确地识别PP序列。目前的机器学习工具具有局限性，因为它们特定于它们所训练的RiPP类，并且依赖于上下文，需要关于推定PP序列周围遗传环境的信息。NeuRiPP克服了这些限制。它通过利用现有程序的高置信度推定PP序列的丰富数据集，沿着来自RiPP数据库的实验验证PP来实现这一点。NeuRiPP使用适合于肽分类的神经网络架构，其权重在PP数据集上训练。它能够识别已知的PP序列和可能是PP的序列。当在现有的RiPP BGC数据集上进行测试时，NeuRiPP能够在比当前工具明显更多的推定RiPP簇中识别PP序列，同时保持相同的HMM命中精度。最后，NeuRiPP能够成功地从最近通过实验表征的新型RiPP类别中识别PP序列，突出了其在补充现有生物信息学工具方面的实用性。
Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. Current machine learning tools have limitations, since they are specific to the RiPPclass they are trained for and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network archictectures that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP was able to identify PP sequences in significantly more putative RiPP clusters than current tools while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that were recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools.