Event Title

Session 6: Healthcare - Analyzing Ligand-binding Proteins Using Their Structural Information

Location

University Student Union: Pheasant Room 253 A/B

Start Date

12-2-2018 2:00 PM

End Date

12-2-2018 3:00 PM

Description

It is known that a protein’s biological function is in some way related to its physical structure. Many researchers have studied this relationship both for the entire backbone structures of proteins as well as their binding sites, which are where binding activity occurs. However, despite this research, it remains an open challenge to predict a protein’s function from its structure. There are many useful applications from protein function predictions, such as effective drug discovery with fewer side effects, development of structure-based drug designs, disease diagnosis, and many more. This presentation will discuss how this ligand-binding protein prediction problem is approached by taking a higher level object-oriented approach, which is named as Covariances of Distances to Principal Axis (CDPA) that summarizes the description of the binding site so that it reduces the amount of information lost compared to most of the other approaches. Thereby, a model-based method is considered, where the nonparametric model is implemented by using the features of the binding sites for a given ligand group for understanding and classification purposes. Then the results obtained using the model-based approach are compared to the alignmentbased method used by Ellingson and Zhang (2012) and Hoffmann et al. (2010).

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Feb 12th, 2:00 PM Feb 12th, 3:00 PM

Session 6: Healthcare - Analyzing Ligand-binding Proteins Using Their Structural Information

University Student Union: Pheasant Room 253 A/B

It is known that a protein’s biological function is in some way related to its physical structure. Many researchers have studied this relationship both for the entire backbone structures of proteins as well as their binding sites, which are where binding activity occurs. However, despite this research, it remains an open challenge to predict a protein’s function from its structure. There are many useful applications from protein function predictions, such as effective drug discovery with fewer side effects, development of structure-based drug designs, disease diagnosis, and many more. This presentation will discuss how this ligand-binding protein prediction problem is approached by taking a higher level object-oriented approach, which is named as Covariances of Distances to Principal Axis (CDPA) that summarizes the description of the binding site so that it reduces the amount of information lost compared to most of the other approaches. Thereby, a model-based method is considered, where the nonparametric model is implemented by using the features of the binding sites for a given ligand group for understanding and classification purposes. Then the results obtained using the model-based approach are compared to the alignmentbased method used by Ellingson and Zhang (2012) and Hoffmann et al. (2010).