Corrigendum to “3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design” [Biochem. Biophys. Res. Commun. 355 (2007) 513–519]

2007 ◽  
Vol 357 (1) ◽  
pp. 330 ◽  
Author(s):  
Jing-Fang Wang ◽  
Dong-Qing Wei ◽  
Lin Li ◽  
Si-Yuan Zheng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Drug Design ◽  
3D Structure ◽  
Structure Modeling ◽  
Cytochrome P450 2C19 ◽  
3D Structure Modeling

3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design

2007 ◽  
Vol 355 (2) ◽  
pp. 513-519 ◽  
Author(s):  
Jing-Fang Wang ◽  
Dong-Qing Wei ◽  
Lin Li ◽  
Si-Yuan Zheng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Drug Design ◽  
3D Structure ◽  
Structure Modeling ◽  
Cytochrome P450 2C19 ◽  
3D Structure Modeling

scholarly journals RNA and protein 3D structure modeling: similarities and differences

2011 ◽  
Vol 17 (9) ◽  
pp. 2325-2336 ◽  
Author(s):  
Kristian Rother ◽  
Magdalena Rother ◽  
Michał Boniecki ◽  
Tomasz Puton ◽  
Janusz M. Bujnicki
Keyword(s):  
3D Structure ◽  
Structure Modeling ◽  
Protein 3D Structure ◽  
3D Structure Modeling ◽  
Similarities And Differences

scholarly journals 3D Structure Modeling of Catalase Enzyme from Aspergillus fumigatus

2016 ◽  
Vol 1 (1) ◽  
pp. 008-012
Author(s):  
D Vivek Dhar ◽  
B Shiv ◽  
AC Kaushik ◽  
M Sarad Kumar
Keyword(s):  
Aspergillus Fumigatus ◽  
3D Structure ◽  
Structure Modeling ◽  
3D Structure Modeling

scholarly journals Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning

2020 ◽  
Author(s):  
Jin Li ◽  
Jinbo Xu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Prediction Method ◽  
Structure Modeling ◽  
Contact Prediction ◽  
Real Value ◽  
3D Structure Modeling ◽  
Distance Prediction

AbstractInter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then employ a novel method to fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.

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