Automated tertiary structure prediction with accurate local model quality assessment using the intfold-ts method

In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.

Download Full-text

P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features

Bioengineering ◽

10.3390/bioengineering8030040 ◽

2021 ◽

Vol 8 (3) ◽

pp. 40

Author(s):

Yuma Takei ◽

Takashi Ishida

Keyword(s):

Quality Assessment ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Three Dimensional ◽

Sequence Profile ◽

Single Model ◽

Model Quality ◽

Model Quality Assessment ◽

Assessment Performance

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.

Download Full-text

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

10.1101/2021.01.28.428706 ◽

2021 ◽

Author(s):

Jian Liu ◽

Tianqi Wu ◽

Zhiye Guo ◽

Jie Hou ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Tertiary Structure ◽

Model Quality ◽

Tertiary Structure Prediction ◽

Model Quality Assessment ◽

Template Free ◽

Distance Prediction ◽

Protein Tertiary Structure Prediction

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in the three main aspects: (1) a new deep-learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in protein tertiary structure prediction and ranked 3rd out of 136 predictors in inter-domain structure prediction. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance predictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.

Download Full-text

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

10.22541/au.161733097.71126646/v1 ◽

2021 ◽

Author(s):

Jian Liu ◽

Tianqi Wu ◽

Zhiye Guo ◽

Jie Hou ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Tertiary Structure ◽

Model Quality ◽

Tertiary Structure Prediction ◽

Model Quality Assessment ◽

Template Free ◽

Distance Prediction ◽

Protein Tertiary Structure Prediction

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in three main aspects: (1) a new deep learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in protein tertiary structure prediction and ranked 3rd out of 136 predictors in inter-domain structure predic-tion. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance pre-dictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.

Download Full-text

PconsD: ultra rapid, accurate model quality assessment for protein structure prediction

Bioinformatics ◽

10.1093/bioinformatics/btt272 ◽

2013 ◽

Vol 29 (14) ◽

pp. 1817-1818 ◽

Cited By ~ 26

Author(s):

Marcin J. Skwark ◽

Arne Elofsson

Keyword(s):

Protein Structure ◽

Quality Assessment ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Model Quality ◽

Accurate Model ◽

Model Quality Assessment

Download Full-text

Large-scale structure prediction by improved contact predictions and model quality assessment

Bioinformatics ◽

10.1093/bioinformatics/btx239 ◽

2017 ◽

Vol 33 (14) ◽

pp. i23-i29 ◽

Cited By ~ 23

Author(s):

Mirco Michel ◽

David Menéndez Hurtado ◽

Karolis Uziela ◽

Arne Elofsson

Keyword(s):

Quality Assessment ◽

Structure Prediction ◽

Large Scale ◽

Large Scale Structure ◽

Scale Structure ◽

Model Quality ◽

Model Quality Assessment ◽

Contact Predictions

Download Full-text

De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning

10.1101/2022.01.11.475831 ◽

2022 ◽

Author(s):

Jun Liu ◽

Guangxing He ◽

Kailong Zhao ◽

Guijun Zhang

Keyword(s):

Protein Structure ◽

Quality Assessment ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Feedback Mechanism ◽

Geometric Constraints ◽

Model Quality ◽

Model Quality Assessment ◽

Loop Feedback

Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templates, is helpful for unraveling the folding mechanism. Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. Results: In this study, we propose a de novo protein structure prediction method called RocketX. In RocketX, a feedback mechanism is constructed through the geometric constraint prediction network GeomNet, the structural simulation module, and the model quality evaluation network EmaNet. In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. The structure model is folded based on the predicted geometric constraints. In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. RocketX is tested on 483 benchmark proteins and 20 FM targets of CASP14. Experimental results show that the closed-loop feedback mechanism significantly contributes to the performance of RocketX, and the prediction accuracy of RocketX outperforms that of the state-of-the-art methods trRosetta (without templates) and RaptorX. In addition, the blind test results on CAMEO show that although no template is used, the prediction accuracy of RocketX on medium and hard targets is comparable to the advanced methods that integrate templates.

Download Full-text

A single-model quality assessment method for poor quality protein structure

10.21203/rs.3.rs-17080/v1 ◽

2020 ◽

Author(s):

Jianquan Ouyang ◽

Ningqiao Huang ◽

Yunqi Jiang

Keyword(s):

Protein Structure ◽

Quality Assessment ◽

Structure Prediction ◽

Assessment Method ◽

Poor Quality ◽

Single Model ◽

Model Quality ◽

Model Quality Assessment ◽

Quality Assessment Method

Abstract Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool. In this work, we introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

Download Full-text