Institute of Protein Biochemistry, National Research Council, Naples, Italy.
Dompé Farmaceutici SpA, L'Aquila.
Bioinformatics. 2018 Aug 1;34(15):2566-2574. doi: 10.1093/bioinformatics/bty159.
ADP-ribosylation is a post-translational modification (PTM) implicated in several crucial cellular processes, ranging from regulation of DNA repair and chromatin structure to cell metabolism and stress responses. To date, a complete understanding of ADP-ribosylation targets and their modification sites in different tissues and disease states is still lacking. Identification of ADP-ribosylation sites is required to discern the molecular mechanisms regulated by this modification. This motivated us to develop a computational tool for the prediction of ADP-ribosylated sites.
Here, we present ADPredict, the first dedicated computational tool for the prediction of ADP-ribosylated aspartic and glutamic acids. This predictive algorithm is based on (i) physicochemical properties, (ii) in-house designed secondary structure-related descriptors and (iii) three-dimensional features of a set of human ADP-ribosylated proteins that have been reported in the literature. ADPredict was developed using principal component analysis and machine learning techniques; its performance was evaluated both internally via intensive bootstrapping and in predicting two external experimental datasets. It outperformed the only other available ADP-ribosylation prediction tool, ModPred. Moreover, a novel secondary structure descriptor, HM-ratio, was introduced and successfully contributed to the model development, thus representing a promising tool for bioinformatics studies, such as PTM prediction.
ADPredict is freely available at www.ADPredict.net.
Supplementary data are available at Bioinformatics online.
ADP-糖基化是一种翻译后修饰（PTM），涉及多种关键的细胞过程，从 DNA 修复和染色质结构的调节到细胞代谢和应激反应。迄今为止，对于不同组织和疾病状态下 ADP-糖基化靶标及其修饰位点的全面了解仍然缺乏。鉴定 ADP-糖基化位点是辨别该修饰调控的分子机制所必需的。这促使我们开发了一种用于预测 ADP-糖基化位点的计算工具。
在这里，我们提出了 ADPredict，这是第一个专门用于预测 ADP-糖基化天冬氨酸和谷氨酸的计算工具。该预测算法基于（i）物理化学性质、（ii）内部设计的二级结构相关描述符和（iii）文献中报道的一组人类 ADP-糖基化蛋白的三维特征。ADPredict 是使用主成分分析和机器学习技术开发的；通过内部密集的自举和预测两个外部实验数据集来评估其性能。它的性能优于唯一可用的其他 ADP-糖基化预测工具 ModPred。此外，引入了一个新的二级结构描述符 HM-ratio，并成功地为模型开发做出了贡献，因此它代表了生物信息学研究（如 PTM 预测）的一种有前途的工具。
ADPredict 可在 www.ADPredict.net 上免费获得。
补充数据可在“Bioinformatics”在线获取。
