P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features

• Main Authors: Yuma Takei, Takashi Ishida
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Bioengineering
• Subjects: model quality assessment (MQA); estimation of model accuracy (EMA); protein structure prediction; machine learning; deep learning; 3DCNN
• Online Access: https://www.mdpi.com/2306-5354/8/3/40

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.