Background As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. Methods In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. Results Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. Conclusion A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

中文翻译：

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使用阳性未标记学习的赖氨酸戊二酰化位点的计算识别

背景作为一种新型的蛋白质酰化修饰，赖氨酸戊二酰化已被发现在代谢过程和线粒体功能中起着至关重要的作用。为了进一步探索戊二酰化的生物学机制和功能，预测潜在的戊二酰化位点具有重要意义。在现有的戊二酰化位点预测因子中，将经过实验验证的戊二酰化位点作为阳性样本，将未经验证的赖氨酸位点作为阴性样本来训练预测因子。然而，未经验证的赖氨酸位点可能包含一些尚未通过实验鉴定的戊二酰化位点。方法 在本研究中，将实验验证的戊二酰化位点作为阳性样本，其余未经验证的赖氨酸位点作为未标记样本。开发了一种名为 PUL-GLU 的生物信息学工具，以使用阳性未标记学习算法识别戊二酰化位点。结果 实验结果表明，PUL-GLU 显着优于当前的戊二酰化位点预测因子。因此，PUL-GLU 可以成为准确识别蛋白质戊二酰化位点的有力工具。结论 在 http://bioinform.cn/pul_glu/ 上提供了一个用户友好的 PUL-GLU 网络服务器。