The DAPLDS or Dynamically Adaptive Protein-Ligand Docking System project involves collaboration among the University of Texas, El Paso, The Scripps Research Institute (TSRI), and the University of California, Berkeley. This project, through implementation and use of a cybertool, DAPLDS, that enables adaptive multi-scale modeling in a global computing environment (i.e., distributed, heterogeneous computing environment using "volunteer" PC computers), will further knowledge of the atomic details of protein-ligand interactions and, by doing so, will accelerate the discovery of novel pharmaceuticals. The goals of the project are: (1) to explore the multi-scale nature of algorithmic adaptations in protein-ligand docking and (2) to develop cyber infrastructures based on computational methods and models that efficiently accommodate these adaptations.
The intellectual merit of the project derives from small molecules, called ligands, which play an essential role in turning protein functions on or off, or in providing substrates for chemical reactions catalyzed by enzymes. Knowledge of the atomic level details of the protein-ligand docking is a valuable resource in the development of novel pharmaceuticals. The docking process depends on the characteristics of the protein-ligand complex involved and given a certain complex, the characterization and modeling of the docking process can affect both solution accuracy and model execution time. Based on characteristics of the protein-ligand conformations and the availability and reliability of computational resources, DAPLDS adapts, when appropriate, the model and/or the computational system to optimize model accuracy and time to solution. The multi-scale modeling adaptation in DAPLDS comprises at least three spanning scales: (1) protein-ligand representation spanning scale from rigid to flexible representation of protein-ligand interactions, (2) solvent representation spanning scale from less accurate to more accurate modeling of solvent treatment, and (3) sampling strategy spanning scale from fixed to adaptive sampling of the protein-ligand docking space.
Broader Impact: DAPLDS applies multi-scale modeling to the search for putative drugs and drug leads. Our project changes the way in which grand challenges are approached by implementing an adaptive cybertool that scales beyond the protein-ligand docking application, e.g., this tool can be adapted and used for protein folding and protein structure prediction. Moreover, the use of public computing resources promotes and disseminates science research and science knowledge among the users of PCs involved in this effort.
