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- W3137895877 abstract "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." @default.
- W3137895877 created "2021-03-29" @default.
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- W3137895877 date "2021-03-19" @default.
- W3137895877 modified "2023-09-26" @default.
- W3137895877 title "P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features" @default.
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- W3137895877 doi "https://doi.org/10.3390/bioengineering8030040" @default.
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