NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network
NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network
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DOI：
10.1038/s41598-019-54405-6
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发表时间：
2019-11-29
影响因子：
4.6
通讯作者：
Kihara, Daisuke
中科院分区：
文献类型：
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作者：
Jain, Aashish;Kihara, Daisuke
随着合成生物学的进步，设计和合成定制基因产品所需的成本和时间一直在稳步下降。学术界和工业界的许多研究实验室通常都会制造基因工程蛋白质作为其研究活动的一部分。然而，蛋白质序列的操纵可能会导致无意中产生有毒蛋白质。因此，能够在合成前识别蛋白质的毒性将降低潜在危害的风险。现有方法过于具体，限制了其应用。在这里，我们扩展了用于预测蛋白质毒性的通用功能预测方法。蛋白质功能预测方法在生物信息学界得到了积极的研究，并在过去十年中取得了显着的进步。我们之前开发了成功的功能预测方法，这些方法在社区范围的功能注释实验 CAFA 中被证明是表现最好的方法之一。基于我们的功能预测方法，我们开发了一个名为 NNTox 的神经网络模型，该模型使用目标蛋白的预测 GO 术语来进一步预测该蛋白有毒的可能性。我们还开发了一个多标签模型，可以预测查询序列的特定毒性类型。这项工作共同分析了 GO 术语与蛋白质毒性之间的关系，并建立了蛋白质毒性的预测模型。
With advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decreasing. Many research laboratories in academia as well as industry routinely create genetically engineered proteins as a part of their research activities. However, manipulation of protein sequences could result in unintentional production of toxic proteins. Therefore, being able to identify the toxicity of a protein before the synthesis would reduce the risk of potential hazards. Existing methods are too specific, which limits their application. Here, we extended general function prediction methods for predicting the toxicity of proteins. Protein function prediction methods have been actively studied in the bioinformatics community and have shown significant improvement over the last decade. We have previously developed successful function prediction methods, which were shown to be among top-performing methods in the community-wide functional annotation experiment, CAFA. Based on our function prediction method, we developed a neural network model, named NNTox, which uses predicted GO terms for a target protein to further predict the possibility of the protein being toxic. We have also developed a multi-label model, which can predict the specific toxicity type of the query sequence. Together, this work analyses the relationship between GO terms and protein toxicity and builds predictor models of protein toxicity.