Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
Sci Rep. 2021 Mar 16;11(1):6074. doi: 10.1038/s41598-021-85629-0.
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .
适体是短的寡核苷酸（DNA/RNA）或肽分子，能够与它们的特定目标具有高度特异性和亲和力选择性地结合。作为一种强大的新型氨基酸配体，适体在生物传感、治疗和诊断领域具有很高的潜力。在这里，我们提出了 AptaNet-一种新的深度学习神经网络-通过整合来自适体和靶蛋白的特征来预测适体-蛋白相互作用对。适体是通过使用两种不同的策略进行编码的，包括 k-mer 和反向互补 k-mer 频率。氨基酸组成（AAC）和伪氨基酸组成（PseAAC）被应用于使用蛋白质的 24 种物理化学和构象特性来表示靶信息。为了解决数据中的不平衡问题，我们应用了一种邻域清理算法。该预测器是基于深度神经网络构建的，并且使用随机森林算法选择了最优特征。结果，训练数据集的准确率达到 99.79%，测试数据集的准确率达到 91.38%。AptaNet 在我们构建的适体-蛋白基准数据集上取得了很高的性能。结果表明，AptaNet 可以帮助识别新的适体-蛋白相互作用对，并深入了解适体与蛋白质之间的关系。我们的基准数据集和 AptaNet 的源代码可在以下网址获得：https://github.com/nedaemami/AptaNet。
