College of Information Engineering, Zhejiang University of Technology, Hangzhou310023, China.
School of Systems and Technology, Department of Informatics and Systems, University of Management and Technology, Lahore54770, Pakistan.
J Chem Inf Model. 2023 Feb 13;63(3):1044-1057. doi: 10.1021/acs.jcim.2c00943. Epub 2023 Jan 31.
Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew's correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.
鉴定 DNA 结合蛋白（DBP）有助于挖掘 DNA-蛋白质相互作用中嵌入的信息，这对于理解 DNA 复制、转录和修复的机制具有重要意义。尽管现有的基于蛋白质序列预测 DBP 的计算方法已经取得了巨大的成功，但由于这些方法没有充分挖掘序列顺序信息，仍有改进的空间。在这项研究中，开发了一种新的三部分序列顺序特征提取（称为 TPSO）策略，用于从蛋白质序列中提取更具判别力的信息，以预测 DBP。对于每个查询蛋白质，TPSO 首先将其一级序列特征分为 N 端和 C 端片段，然后分别提取全长序列和这两个片段的三个部分的数值伪特征。基于 TPSO，提出了一种新的基于深度学习的方法，称为 TPSO-DBP，它采用基于序列的单视图特征、双向长短期记忆（BiLSTM）和全连接（FC）神经网络来学习 DBP 预测模型。实验结果表明，TPSO-DBP 可以达到 87.01%的准确率，涵盖了 85.30%的所有 DBP，同时实现了显著高于大多数现有最先进的 DBP 预测方法的马修相关系数值（0.741）。详细的数据分析表明，TPSO-DBP 的优势在于 TPSO 的利用，它有助于提取更隐蔽的突出模式，以及由 BiLSTM 和 FC 组成的深度学习神经网络框架，它学习输入特征与 DBP 之间的非线性关系。TPSO-DBP 的独立包和网络服务器可在 https://jun-csbio.github.io/TPSO-DBP/ 免费获取。
