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

AbstractMotivationAccurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.ResultsWe present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before.AvailabilityDatasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely [email protected] informationNo supplementary data

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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

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NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213013600063 ◽

2013 ◽

Vol 22 (05) ◽

pp. 1360006 ◽

Cited By ~ 2

Author(s):

QINGGUO WANG ◽

CHARLES SHANG ◽

DONG XU ◽

YI SHANG

Keyword(s):

Multidimensional Scaling ◽

Quality Assessment ◽

Structure Prediction ◽

Tertiary Structure ◽

Selection Problem ◽

Model Quality ◽

Model Quality Assessment ◽

Protein Model ◽

Protein Model Quality Assessment ◽

Consensus Score

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.

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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.

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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.

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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.

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