Integration of 3D Structural and Sequence Features to Predict GPCR Ligand Binding
Abstract
G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. Such predictions are cost-efficient and can be important aides for planning wet lab experiments to help elucidate signaling pathways and expedite drug discovery. There are existing computational tools for GPCR ligand binding prediction using various sequence and structural derived features. However, these methods have been typically tested on specific families of GPCRs and none has focused on features that characterize binding of a single ligand to multiple GPCR families. In this work, we have established that there are ligands that bind across two or more distinct GPCR families. In many cases the involved GPCRs share a conserved sequence motif and structural similarities. These results suggest possibilities for predicting GPCR ligand binding through the integration of sequence and 3D structural information. The prediction process can be guided by features that characterize binding of one ligand to multiple GPCRs of the same or different families. For my PhD dissertation research, I propose to further explore the combination of such features to predict GPCR ligand binding. Our computational approach, which involves integrating GPCR classification, structure predictions, and molecular docking, will be based on statistical and machine learning as well as energy optimization techniques to predict what ligands will bind to a given GPCR. The resulting algorithm will be implemented in Python and R programming packages and incorporated into the publicly accessible GPCR-PEn webserver (gpcr.utep.edu) for distribution to the scientific community.
Subject Area
Bioinformatics|Artificial intelligence
Recommended Citation
Dankwah, Kwabena Owusu, "Integration of 3D Structural and Sequence Features to Predict GPCR Ligand Binding" (2020). ETD Collection for University of Texas, El Paso. AAI28262055.
https://scholarworks.utep.edu/dissertations/AAI28262055