An in silico approach to identification, categorization and prediction of nucleic acid binding proteins
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins
复制标题
核酸结合蛋白的鉴定、分类和预测的计算机方法
DOI：
10.1093/bib/bbaa171
复制
发表时间：
2021-05-01
影响因子：
9.5
通讯作者：
Zou, Quan
中科院分区：
文献类型：
--
作者：
Xu, Lei;Jiang, Shanshan;Zou, Quan
蛋白质与核酸之间的相互作用在转录、翻译和DNA修复等过程中起着重要的作用。相关生物事件的机制可以通过探索蛋白质在这些相互作用中的功能来理解。近年来，已知蛋白质序列的数量迅速增加，但用于描述蛋白质结构和功能的数据库却发展缓慢。因此，改进这些数据库对于预测蛋白质-核酸相互作用是有意义的。此外，相关生物事件的机制，如病毒感染或设计新的药物靶点，可以通过了解蛋白质在这些相互作用中的功能来进一步理解。收集并鉴定了每个序列的信息，包括其功能和相互作用位点，并建立了PNIDB数据库。PNIDB中的蛋白质被分为27类，如转录，免疫系统和结构蛋白等，然后使用机器学习方法预测每个蛋白质的功能。使用我们的方法，预测器在标记序列上训练，然后基于训练好的分类器预测蛋白质的功能。经10次交叉验证，预测准确率达到77.43%。
The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein-nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation.