AngularQA: Protein Model Quality Assessment with LSTM Networks

AbstractQuality Assessment (QA) plays an important role in protein structure prediction. Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure at each time-step, without using any database. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub:https://github.com/caorenzhi/AngularQA

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AngularQA: Protein Model Quality Assessment with LSTM Networks

Computational and Mathematical Biophysics ◽

10.1515/cmb-2019-0001 ◽

2019 ◽

Vol 7 (1) ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Matthew Conover ◽

Max Staples ◽

Dong Si ◽

Miao Sun ◽

Renzhi Cao

Keyword(s):

Protein Structure ◽

Quality Assessment ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Poor Quality ◽

Model Quality ◽

Time Step ◽

Testing Dataset ◽

Protein Model Quality Assessment ◽

Lstm Network

AbstractQuality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

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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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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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