As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%–50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.

作为重要的蛋白质酰化修饰，赖氨酸琥珀酰化（Ksucc）参与各种生物过程，并参与人类肿瘤发生。在这里，我们从13种物种中收集了26,243个非冗余的已知Ksucc站点作为基准数据集，结合了10种类型的信息功能，并通过整合深度学习和常规机器学习来实现混合学习架构将算法整合到一个框架中。我们构建了一个名为HybridSucc的新工具，该工具的Ksucc部位的一般预测值和人为特异性预测的曲线下面积（AUC）值分别为0.885和0.952。相比之下，HybridSucc的准确性比其他现有工具高17.84％–50.62％。使用HybridSucc，我们进行了蛋白质组范围内的预测，并确定了370个癌症突变的优先级，这些突变改变了218种重要蛋白质（包括PKM2，SHMT2和IDH2）的Ksucc状态。我们不仅开发了用于预测Ksucc站点的引人注目的工具，而且还为进一步的实验考虑生成了有用的候选对象。可以从http://hybridsucc.biocuckoo.org/免费访问HybridSucc的在线服务以进行学术研究。