摘要： 构建基于云计算的蛋白质折叠空间结构预测框架，通过数据云存储设备获取蛋白质序列原始数据，采用HDFS（Hadoop Distributed File System）分布式存储方式保存于云端。资源和队列管理器RQM（Resource Queue Management）开启云端虚拟机后，以之作为扫描节点（Sensor Node， SN），SN基于二维AB非格点模型建立最小蛋白质分子能量优化函数，采用局部搜索机制改进的量子遗传算法对其作优化求解。利用云端GPU设备处理模型训练数据，即可实现蛋白质折叠空间结构的自动化预测。实验结果表明：蛋白质序列能量势函数计算结果更小、执行效率更高、GDT-TS（Geothermal Development and Testing Tool Suite）评价指标值更大。

中图分类号：TP393.4 文献标志码：A DOI: 10.16157/j.issn.0258-7998.244973
中文引用格式： 徐胜超，杨波，王宏杰，等. 基于云计算的蛋白质折叠空间结构预测[J]. 电子技术应用，2024，50(8)：10-16.
英文引用格式： Xu Shengchao，Yang Bo，Wang Hongjie，et al. Cloud computing based spatial structure prediction of protein folding[J]. Application of Electronic Technique，2024，50(8)：10-16.

Cloud computing based spatial structure prediction of protein folding

Abstract： A prediction framework for the spatial structure of protein folding based on cloud computing is proposed and implemented. The original data of protein sequence is obtained through the data cloud storage unit and stored in the cloud using the HDFS distributed storage mode. After the resource and queue manager RQM (Requirements Quality Management) starts the cloud virtual machine, it is used as the Sensor Node which establishes the minimum protein molecular energy optimization function based on two-dimensional AB non-lattice model. The quantum genetic algorithm is adopted for local search mechanism to optimize its solution. The cloud GPU equipment is used to process the model training data to complete the automatic prediction of the spatial structure of protein folding. The experimental results show that the proposed approach can achieve the smaller calculation result of protein sequence energy potential function, the higher execution efficiency, and the higher GDT-TS (Geothermal Development and Testing Tool Suite) evaluation index value.

Key words : cloud computing；protein folding；spatial structure prediction；HDFS distributed storage；local search mechanism；quantum genetic algorithm