scholarly journals A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study

Structure ◽  
2012 ◽  
Vol 20 (6) ◽  
pp. 1118-1126 ◽  
Author(s):  
Feng Zhao ◽  
Jinbo Xu
Keyword(s):  
Protein Structure ◽  
Functional Study ◽  
Statistical Potential ◽  
Specific Distance

scholarly journals Protein structure prediction based on statistical potential

1992 ◽  
Vol 62 (1) ◽  
pp. 104-106 ◽  
Author(s):  
S. Sun ◽  
N. Luo ◽  
R.L. Ornstein ◽  
R. Rein
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Statistical Potential

scholarly journals Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning

2017 ◽  
Author(s):  
Yujuan Gao ◽  
Sheng Wang ◽  
Minghua Deng ◽  
Jinbo Xu
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Conformational Space ◽  
Functional Study ◽  
Dihedral Angles ◽  
Prediction Errors ◽  
The Real ◽  
Novel Method

AbstractBackgroundProtein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging.MethodIn this study, we present a novel method to predict realvalued angles by combining clustering and deep learning. That is, we first generate certain clusters of angles (each assigned a label) and then apply a deep residual neural network to predict the label posterior probability. Finally, we output real-valued prediction by a mixture of the clusters with their predicted probabilities. At the same time, we also estimate the bound of the prediction errors at each residue from the predicted label probabilities.ResultIn this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds.ConclusionsOur study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

scholarly journals Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling

2019 ◽  
Author(s):  
Giacomo Janson ◽  
Alessandro Grottesi ◽  
Marco Pietrosanto ◽  
Gabriele Ausiello ◽  
Giulia Guarguaglini ◽  
...  
Keyword(s):  
Protein Structure ◽  
Homology Modeling ◽  
Protein Structure Prediction ◽  
3D Modeling ◽  
Structure Prediction ◽  
3D Model ◽  
Model Building ◽  
Statistical Potential ◽  
Target Template ◽  
Structural Divergence

AbstractThe most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program’s predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER’s objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance.Author summaryProteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most successful computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLER’s scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.

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