琥珀酰化
深度学习
计算机科学
人工智能
序列(生物学)
卷积神经网络
计算生物学
神经科学
模式识别(心理学)
生物
赖氨酸
生物化学
氨基酸
作者
Huiqing Wang,Hong Sheng Zhao,Jing Zhang,Jiale Han,Zhihao Liu
摘要
Lysine succinylation (Ksucc) regulates various metabolic processes, participates in vital life processes, and is involved in the occurrence and development of numerous diseases. Accurate recognition of succinylation sites can reveal underlying functional mechanisms and pathogenesis. However, most remain undetected. Moreover, a deep learning architecture focusing on generic and species-specific predictions is still lacking. Thus, we proposed a deep learning-based framework named Deep-Ksucc, combining a dense convolutional network and ordered-neuron long short-term memory in parallel, which took the cascading characteristics of sequence information and physicochemical properties as the input. The results of the generic and species-specific predictions indicated that Deep-Ksucc can identify sequence patterns of different organisms and recognize plenty of succinylation sites. The case study showed that Deep-Ksucc can serve as a reliable tool for biology verification and computer-aided recognition of succinylation sites.
科研通智能强力驱动
Strongly Powered by AbleSci AI