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P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features
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 MQAdoi:10.3390/bioengineering8030040 pmid:33808604 fatcat:nuyw2bk72vcwrjtr4kiqbm4yb4