Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

AbstractThe inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). During the 2020 CASP14 experiment, we developed and tested several EMA predictors that used deep learning with the new features based on inter-residue distance/contact predictions as well as the existing model quality features. The average global distance test (GDT-TS) score loss of ranking CASP14 structural models by three multi-model MULTICOM EMA predictors (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) is 0.073, 0.079, and 0.081, respectively, which are ranked first, second, and third places out of 68 CASP14 EMA predictors. The single-model EMA predictor (MULTICOM-DEEP) is ranked 10th place among all the single-model EMA methods in terms of GDT_TS score loss. The results show that deep learning and contact/distance predictions are useful in ranking and selecting protein structural models.

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Protein Model Accuracy Estimation Empowered by Deep Learning and Inter-residue Distance Prediction in CASP14

10.21203/rs.3.rs-228012/v1 ◽

2021 ◽

Author(s):

Xiao Chen ◽

Jian Liu ◽

Zhiye Guo ◽

Tianqi Wu ◽

Jie Hou ◽

...

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Structural Models ◽

Single Model ◽

Model Accuracy ◽

Model Quality ◽

Residue Contact ◽

Contact Distance ◽

Protein Model ◽

Contact Predictions

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (CASP13) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). During the 2020 CASP14 experiment, we developed and tested several EMA predictors that used deep learning with the new features based on inter-residue distance/contact predictions as well as the existing model quality features. The average global distance test (GDT-TS) score loss of ranking CASP14 structural models by three multi-model MULTICOM EMA predictors (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) is 0.073, 0.079, and 0.081, respectively, which are ranked first, second, and third places out of 68 CASP14 EMA predictors. The single-model EMA predictor (MULTICOM-DEEP) is ranked 10th place among all the single-model EMA methods in terms of GDT-TS score loss. The results show that deep learning and contact/distance predictions are useful in ranking and selecting protein structural models.

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Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Scientific Reports ◽

10.1038/s41598-021-90303-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiao Chen ◽

Jian Liu ◽

Zhiye Guo ◽

Tianqi Wu ◽

Jie Hou ◽

...

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Structural Models ◽

Single Model ◽

Model Accuracy ◽

Model Quality ◽

Distance Information ◽

Evaluation Of Performance ◽

Protein Model

AbstractThe inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

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DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

BMC Bioinformatics ◽

10.1186/s12859-021-03960-9 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 1

Author(s):

Tianqi Wu ◽

Zhiye Guo ◽

Jie Hou ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Tertiary Structure ◽

Convolutional Network ◽

Distance Map ◽

Contact Distance ◽

Real Value ◽

Contact Predictions ◽

Distance Prediction ◽

Better Than

Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

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DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

10.1101/2021.03.29.437573 ◽

2021 ◽

Author(s):

Xiao Chen ◽

Jianling Cheng

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Structural Models ◽

Model Quality ◽

Distance Map ◽

Protein Model ◽

Single Protein ◽

Ranking Loss

AbstractBackgroundEstimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model.ResultWe developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. The work demonstrates that using raw inter-residue distance information alone with deep learning can predict the quality of protein structural models reasonably well.

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Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

10.1101/552422 ◽

2019 ◽

Cited By ~ 7

Author(s):

Jie Hou ◽

Tianqi Wu ◽

Renzhi Cao ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Tertiary Structure ◽

Structure Modeling ◽

Contact Distance ◽

Protein Model ◽

Template Free ◽

Distance Prediction

AbstractPrediction of residue-residue distance relationships (e.g. contacts) has become the key direction to advance protein tertiary structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based protein structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve inter-residue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving protein structure prediction problem. However, there are still major challenges in accurately predicting protein contact distance when there are few homologous sequences to generate co-evolutionary signals, folding proteins from noisy contact distances, and ranking models of hard targets.

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DeepUMQA: Ultrafast Shape Recognition-based Protein Model Quality Assessment using Deep Learning

10.1101/2021.10.02.462491 ◽

2021 ◽

Author(s):

Sai-Sai Guo ◽

Jun Liu ◽

Xiao-Gen Zhou ◽

Gui-Jun Zhang

Keyword(s):

Deep Learning ◽

Quality Assessment ◽

Shape Recognition ◽

Residue Level ◽

Model Assessment ◽

Single Model ◽

Model Quality ◽

Model Quality Assessment ◽

Protein Model ◽

Protein Model Quality Assessment

AbstractMotivationProtein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for describing residue-level topological information. Design features that can further reflect residue-level topology when combined with deep learning methods are therefore crucial to improve the performance of model quality assessment.ResultsWe developed a deep-learning method, DeepUMQA, based on Ultrafast Shape Recognition (USR) for the residue-level single-model quality assessment. In the framework of the deep residual neural network, the residue-level USR feature was introduced to describe the topological relationship between the residue and overall structure by calculating the first moment of a set of residue distance sets and then combined with 1D, 2D, and voxelization features to assess the quality of the model. Experimental results on test datasets of CASP13, CASP14, and CAMEO show that USR could complement the voxelization feature to comprehensively characterize residue structure information and significantly improve the model assessment accuracy. DeepUMQA outperformed the state-of-the-art single-model quality assessment methods, including ProQ2, ProQ3, ProQ3D, Ornate, VoroMQA, and DeepAccNet.AvailabilityThe source code and executable are freely available at https://github.com/iobio-zjut/[email protected]

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

10.1101/128231 ◽

2017 ◽

Cited By ~ 2

Author(s):

Mirco Michel ◽

David Menéndez Hurtado ◽

Karolis Uziela ◽

Arne Elofsson

Keyword(s):

Structure Prediction ◽

Large Scale ◽

Supplementary Information ◽

Model Quality ◽

Contact Maps ◽

Folding Algorithm ◽

Unknown Structure ◽

Supplementary Material ◽

Direct Coupling Analysis ◽

Contact Predictions

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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The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction

Methods in Molecular Biology - Protein Structure Prediction ◽

10.1007/978-1-0716-0708-4_2 ◽

2020 ◽

pp. 13-26 ◽

Cited By ~ 2

Author(s):

Jie Hou ◽

Tianqi Wu ◽

Zhiye Guo ◽

Farhan Quadir ◽

Jianlin Cheng

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Contact Distance ◽

Prediction Server ◽

Distance Prediction

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Improved estimation of model quality using predicted inter-residue distance

Bioinformatics ◽

10.1093/bioinformatics/btab632 ◽

2021 ◽

Author(s):

Lisha Ye ◽

Peikun Wu ◽

Zhenling Peng ◽

Jianzhao Gao ◽

Jian Liu ◽

...

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Superior Performance ◽

Supplementary Information ◽

Prediction Algorithm ◽

Structure Model ◽

Single Model ◽

Model Quality ◽

Reference Models

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-model 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 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. Availability and Implementation http://yanglab.nankai.edu.cn/QDistance Supplementary information Supplementary data are available at Bioinformatics online.

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