Cavity/Binding Site Prediction Approaches and Their Applications

2020 ◽  
pp. 49-69
Author(s):  
Himanshu Avashthi ◽  
Ambuj Srivastava ◽  
Dev Bukhsh Singh
2021 ◽  
Vol 19 (02) ◽  
pp. 2150006
Author(s):  
Fatemeh Nazem ◽  
Fahimeh Ghasemi ◽  
Afshin Fassihi ◽  
Alireza Mehri Dehnavi

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.


2012 ◽  
Vol 13 (7) ◽  
pp. 8752-8761 ◽  
Author(s):  
Jun Gao ◽  
Qi Liu ◽  
Hong Kang ◽  
Zhiwei Cao ◽  
Ruixin Zhu

2011 ◽  
Vol 12 (1) ◽  
pp. 225 ◽  
Author(s):  
Adam Amos-Binks ◽  
Catalin Patulea ◽  
Sylvain Pitre ◽  
Andrew Schoenrock ◽  
Yuan Gui ◽  
...  

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