scholarly journals The Fragment-based Consistency Score in Model Quality Assessment for De Novo Prediction of Protein Structures

2011 ◽  
Vol 11 ◽  
pp. 63-81
Author(s):  
Hikmet Cetin ◽  
Takeshi N Sasaki ◽  
Masaki Sasai
Keyword(s):  
Quality Assessment ◽  
De Novo ◽  
Protein Structures ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Consistency Score

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

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.

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

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.

scholarly journals Protein structure quality assessment based on the distance profiles of consecutive backbone Cα atoms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 211 ◽  
Author(s):  
Sandeep Chakraborty ◽  
Ravindra Venkatramani ◽  
Basuthkar J. Rao ◽  
Bjarni Asgeirsson ◽  
Abhaya M. Dandekar
Keyword(s):  
Quality Assessment ◽  
De Novo ◽  
Protein Structures ◽  
Three Dimensional ◽  
Grand Challenge ◽  
State Structure ◽  
Native State ◽  
Model Quality ◽  
Link Type ◽  
High Quality Protein

Predicting the three dimensional native state structure of a protein from its primary sequence is an unsolved grand challenge in molecular biology. Two main computational approaches have evolved to obtain the structure from the protein sequence - ab initio/de novo methods and template-based modeling - both of which typically generate multiple possible native state structures. Model quality assessment programs (MQAP) validate these predicted structures in order to identify the correct native state structure. Here, we propose a MQAP for assessing the quality of protein structures based on the distances of consecutive Cα atoms. We hypothesize that the root-mean-square deviation of the distance of consecutive Cα (RDCC) atoms from the ideal value of 3.8 Å, derived from a statistical analysis of high quality protein structures (top100H database), is minimized in native structures. Based on tests with the top100H set, we propose a RDCC cutoff value of 0.012 Å, above which a structure can be filtered out as a non-native structure. We applied the RDCC discriminator on decoy sets from the Decoys 'R' Us database to show that the native structures in all decoy sets tested have RDCC below the 0.012 Å cutoff. While most decoy sets were either indistinguishable using this discriminator or had very few violations, all the decoy structures in the fisa decoy set were discriminated by applying the RDCC criterion. This highlights the physical non-viability of the fisa decoy set, and possible issues in benchmarking other methods using this set. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

QMEAN: A comprehensive scoring function for model quality assessment

2008 ◽  
Vol 71 (1) ◽  
pp. 261-277 ◽  
Author(s):  
Pascal Benkert ◽  
Silvio C. E. Tosatto ◽  
Dietmar Schomburg
Keyword(s):  
Quality Assessment ◽  
Scoring Function ◽  
Model Quality ◽  
Model Quality Assessment
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