Arican Ozgur Can, Gumus Ozgur
Department of Health Bioinformatics, Ege University, 35100, Izmir, Turkey.
Department of Computer Engineering, Ege University, 35100, Izmir, Turkey.
Comput Biol Med. 2023 Sep;164:107317. doi: 10.1016/j.compbiomed.2023.107317. Epub 2023 Aug 7.
Proteins interact with many molecules in order to maintain the vital activities in cells. Proteins that interact with DNA are called DNA-binding proteins (DBP), and proteins that interact with RNA are called RNA-binding proteins (RBP). Since DBPs and RBPs are involved in critical biological processes, their classification is quite important. Although the convolutional neural network and bidirectional long-short-term memory hybrid model (CNN-BiLSTM) is very popular in DBP and RBP classification, it has problems such as requirement of high processing power and long training time. Therefore, a multilayer perceptron (MLP) based predictor, PredDRBP-MLP (Predictor of DNA-Binding Proteins and RNA-Binding Proteins - Multilayer Perceptron) was developed in this study. PredDRBP-MLP is an artificial learning model that performs multi-class classification of DBPs, RBPs and non-nucleic acid-binding proteins (NNABP). PredDRBP-MLP achieved quite successful results on the independent dataset, specifically in the NNABP class, compared to the existing predictors, in addition to requiring lower processing power and being able to train quicker compared to CNN-BiLSTM based predictors. In NNABP class, PredDRBP-MLP predictor achieved 0.578 precision, 0.522 recall and 0.549 F1-score, while other multi-class predictor achieved 0.486 precision, 0.183 recall and 0.266 F1-score. A desktop application was developed for PredDRBP-MLP. The application is freely accessible at https://sourceforge.net/projects/preddrbp-mlp.
蛋白质与许多分子相互作用以维持细胞中的生命活动。与DNA相互作用的蛋白质称为DNA结合蛋白（DBP），与RNA相互作用的蛋白质称为RNA结合蛋白（RBP）。由于DBP和RBP参与关键的生物学过程，它们的分类非常重要。尽管卷积神经网络和双向长短期记忆混合模型（CNN-BiLSTM）在DBP和RBP分类中非常流行，但它存在诸如需要高处理能力和长训练时间等问题。因此，本研究开发了一种基于多层感知器（MLP）的预测器PredDRBP-MLP（DNA结合蛋白和RNA结合蛋白预测器 - 多层感知器）。PredDRBP-MLP是一种人工学习模型，可对DBP、RBP和非核酸结合蛋白（NNABP）进行多类分类。与现有预测器相比，PredDRBP-MLP在独立数据集上取得了相当成功的结果，特别是在NNABP类别中，此外，与基于CNN-BiLSTM的预测器相比，它需要更低的处理能力并且能够更快地训练。在NNABP类别中，PredDRBP-MLP预测器的精确率为0.578，召回率为0.522，F1分数为0.549，而其他多类预测器的精确率为0.486，召回率为0.183，F1分数为0.266。为PredDRBP-MLP开发了一个桌面应用程序。该应用程序可在https://sourceforge.net/projects/preddrbp-mlp上免费访问。
