ArticleBioengineering (Basel, Switzerland)2021
P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features.
Article in Bioengineering (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
- Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction.Nature communications · 2022Article
- Article
- A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models.Bioengineering (Basel, Switzerland) · 2022Article
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2 authors.
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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.
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