The human body contains over 20,000 different types of proteins, which interact with each other to enable life as we know it. Currently, Protein docking models have been Developed to estimate how two proteins will interact, yet it is challenging to score whether or not the predicted docking estimate is correct. The Purdue researchers Developed a new computational Method to address this challenge.
DOVE, short for Docking decoy selection with Voxel-based Deep neural nEtwork, first scans protein-protein interfaces of a proposed Protein docking configuration using a 3D voxel, while considering the atomic interactions and energetic contributions. These 3D features are the input of the Deep Learning model, which is trained to identify near-native models from a larger group of generated models.
DOVE, created by Purdue researchers, captures structural and energetic features of the interface of a Protein docking model with a 3D box and judges if the model is more likely to be correct or incorrect using 3D convolutional neural network.“To understand molecular mechanisms of functions of proteincomplexes, biologists have been using experimental methods such as X-rays andmicroscopes, but they are time- and resource-intensive efforts,†said DaisukeKihara, a professor of biological sciences and computer science in Purdue’sCollege of Science, who leads the Research team. “Bioinformatics researchers inour lab and other institutions have been developing computational methods formodeling Protein complexes. One big challenge is that a computational methodusually generates thousands of models, and choosing the correct one or rankingthe models can be difficult.â€
“Our work represents a major advancement in the field of bioinformatics,†said Xiao Wang, a graduate student and member of the Research team. “This may be the first time researchers have successfully used Deep Learning and 3D features to quickly understand the effectiveness of certain Protein models. Then, this information can be used in the creation of targeted drugs to block certain protein-protein interactions.â€
01-13-2020, 09:07 PM
Deep Learning Method Developed to Aid Protein Docking, Speed Drug Research
DOVE, created by Purdue researchers, captures structural and energetic features of the interface of a Protein docking model with a 3D box and judges if the model is more likely to be correct or incorrect using 3D convolutional neural network.“To understand molecular mechanisms of functions of proteincomplexes, biologists have been using experimental methods such as X-rays andmicroscopes, but they are time- and resource-intensive efforts,†said DaisukeKihara, a professor of biological sciences and computer science in Purdue’sCollege of Science, who leads the Research team. “Bioinformatics researchers inour lab and other institutions have been developing computational methods formodeling Protein complexes. One big challenge is that a computational methodusually generates thousands of models, and choosing the correct one or rankingthe models can be difficult.â€
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