Takei Yuma, Ishida Takashi
Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Koto-ku, Tokyo 135-0064, Japan.
Bioengineering (Basel). 2021 Mar 19;8(3):40. doi: 10.3390/bioengineering8030040.
Model quality assessment (MQA), which selects near-native structures from structure models, is an important process in protein tertiary structure prediction. The three-dimensional convolution neural network (3DCNN) was applied to the task, but the performance was comparable to existing methods because it used only atom-type features as the input. Thus, we added sequence profile-based features, which are also used in other methods, to improve the performance. We developed a single-model MQA method for protein structures based on 3DCNN using sequence profile-based features, namely, P3CMQA. Performance evaluation using a CASP13 dataset showed that profile-based features improved the assessment performance, and the proposed method was better than currently available single-model MQA methods, including the previous 3DCNN-based method. We also implemented a web-interface of the method to make it more user-friendly.
模型质量评估（MQA）是蛋白质三级结构预测中的一个重要过程，它从结构模型中选择接近天然的结构。三维卷积神经网络（3DCNN）被应用于这项任务，但由于其仅使用原子类型特征作为输入，其性能与现有方法相当。因此，我们添加了基于序列概况的特征（其他方法也会使用）来提高性能。我们基于3DCNN开发了一种使用基于序列概况特征的蛋白质结构单模型MQA方法，即P3CMQA。使用CASP13数据集进行的性能评估表明，基于概况的特征提高了评估性能，并且所提出的方法优于当前可用的单模型MQA方法，包括先前基于3DCNN的方法。我们还实现了该方法的网络界面，使其更便于用户使用。
