ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction
By Jianyi Yang, Yan Wang, Yang Zhang. 2015
123 citations recorded by the source corpus.
Computer-based structure prediction becomes a major tool to provide large-scale structure models for annotating biological function of proteins. Information of residue-level accuracy and thermal mobility (or B-factor), which is critical to decide how biologists utilize the predicted models, is however missed in most structure prediction pipelines. We developed ResQ for unified residue-level model quality and B-factor estimations by combining local structure assembly variations with sequence- and structure-based profiling. ResQ was tested on 635 non-redundant proteins with structure models generated by I-TASSER, where the average difference between estimated and observed distance errors is 1.4 Å for the confidently modeled proteins. ResQ was further tested on structure decoys from CASP9-11 experiments, where the error of local structure quality prediction is consistently lower than or comparable to other state-of-the-art predictors. Finally, ResQ B-factor profile was used to assist molecular replacement, which resulted in successful solutions on several proteins that could not be solved from constant B-factor settings.
Subfields: Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Yang Zhang
- Liam J. McGuffin
- Robin Pearce
- Björn Wallner
- Arne Elofsson
- Jianlin Cheng
- Torsten Schwede
- Jianyi Yang
In Molecular Biology
- Yang Zhang
- Robin Pearce
- Liam J. McGuffin
- Jianlin Cheng
- Björn Wallner
- Arne Elofsson
- Torsten Schwede
- Chengxin Zhang
Citations in the corpus
- IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, positive): “Each IntFOLD5 model was considered individually using 6 pure-single model methods (CDA (20), SSA (20), ProQ2 (4), ProQ2D (39), ProQ3D (39) and VoroMQA (40)), and four alternative quasi-single model methods (DBA (20), MF5s (20), MFcQs (20) and ResQ (41)).”
- I-TASSER server: new development for protein structure and function predictions (2015, background): “This consistent reduction of distance estimation relative to the native structure is mainly due to the lower distance estimation for the residues of large modeling errors (1).”
- Estimation of model accuracy in CASP13 (2019, methodology): “5 Additionally, reference 3D-models sets were generated using the IntFOLD5 server 43 and these were used to score models using four previously described quasi-single model methods: DBA, 56 MF5s, 56 MFcQs 56 and ResQ.”
- Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, methodology): “The second noticeable component feature, newly introduced to the CASP12 structure modeling pipelines, is the application of the residue-level quality estimation method, ResQ, 27 which shows efficiency and robustness in predicting the quality of local structure of the predicted models.”
- Deep convolutional neural networks for predicting the quality of single protein structural models (2019, methodology): “No methods tried to predict both quality measurements at the same time, even though some methods derived the global score converted from predicted local quality score of residues 8,33 .”
- Deep-learning contact-map guided protein structure prediction in CASP13 (2019, methodology): “46 The ResQ algorithm extracts the following features for each model: Histograms that have a peak at a position of d ij 9 Å are converted into distance profiles for the residue pair.”