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.
Among the methods for protein structure prediction, which is important in biological research, AlphaFold2 has demonstrated astonishing accuracy in the 14th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP14). The accuracy is close to the level of experimental structure determination. Furthermore, AlphaFold2 predicts three-dimensional structures and estimates the accuracy of the predicted structures. AlphaFold2 outputs two model accuracy estimation scores, pLDDT, and pTM, enabling the user to judge the reliability of the predicted structures. Original research of AlphaFold2 showed that those scores had good correlations to actual prediction accuracy. However, it was unclear whether we could select a structure close to the native structure when multiple structures are predicted for a single protein. In this study, we generated several hundred structures with different combinations of parameters for 500 proteins and verified the performance of the accuracy estimation scores of AlphaFold2. In addition, we compared those scores with existing accuracy estimation methods. As a result, pLDDT and pTM showed better performance than the existing accuracy estimation methods for AlphaFold2 structures. However, the estimation performance of relative accuracy of the scores was still insufficient, and the improvement would be needed for further utilization of AlphaFold2.
Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.
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