Name
Abstract | ||
|---|---|---|
Motivation: Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. Results: based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. Supplementary information: Supplementary data are available at Bioinformatics online. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bioinformatics/btab632 | BIOINFORMATICS |
DocType | Volume | Issue |
Conference | 37 | 21 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lisha Ye | 1 | 0 | 0.34 |
| Peikun Wu | 2 | 0 | 0.68 |
| Zhenling Peng | 3 | 4 | 2.14 |
| Jianzhao Gao | 4 | 8 | 1.89 |
| Jian Liu | 5 | 0 | 0.68 |
| Jianyi Yang | 6 | 16 | 5.07 |