DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction
DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction
复制标题
DOI：
10.1186/s12859-020-3342-z
复制
发表时间：
2020-04-23
影响因子：
3
通讯作者：
KC, Dukka B.
中科院分区：
文献类型：
--
作者：
Thapa, Niraj;Chaudhari, Meenal;KC, Dukka B.
蛋白质琥珀酰化是最近出现的一种重要且常见的翻译后修饰（PTM），发生在赖氨酸残基上。琥珀酰化在其大小（例如，在100 Da时，它是较大的化学PTMs之一）和其在生理ph下将修饰赖氨酸残基的净电荷从+ 1修饰为- 1的能力上都是值得注意的。琥珀酰化后蛋白质中发生的总体局部变化已被证明与基因活性的变化相对应，并受到柠檬酸循环缺陷的干扰。这些观察结果，再加上琥珀酸盐是在细胞呼吸过程中作为代谢中间体产生的这一事实，导致了蛋白质琥珀酰化可能在细胞代谢和重要细胞功能之间的相互作用中发挥作用的建议。例如，琥珀酰化可能代表了基因组调节和修复的一个重要方面，并可能在许多疾病状态的病因学中产生重要影响。在这项研究中，我们开发了DeepSuccinylSite，这是一种新型的预测工具，它使用深度学习方法和嵌入来根据蛋白质的初级结构识别琥珀酰化位点。结果采用实验鉴定的琥珀酰化位点独立测试集，该方法的灵敏度、特异度和MCC效率分别为79%、68.7%和0.48分，ROC曲线下面积为0.8。在与先前描述的琥珀酰化预测因子的并排比较中，DeepSuccinylSite代表了琥珀酰化位点预测的总体准确性的显着提高。总之，这些结果表明，我们的方法代表了一种强大的和互补的技术，用于蛋白质琥珀酰化的高级探索。
Background Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to - 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.