A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models.

ABSTRACT: 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 datas

SUBMITTER: Takei Y 

PROVIDER: S-EPMC8945737 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature