Feeding 9 Billion: Innovations in Agricultural Modeling
One of the most important stages in the drug discovery process is the ability to determine binding sites on proteins for potential ligands.
When attempting to find the correct pose of a protein-ligand complex, there are an infinite number of possible solutions. The position, geometry and conformation of a ligand are all continuous variables – this leads to the “Docking Problem”. To explore all possible docking modes, an enormous amount of calculation must be done – the complexity, of this task may be reduced by adopting a multi-scale approach. In this approach the accuracy is not sacrificed.
There are currently several docking algorithms, which address the “Docking Problem”, two of which are:
· GOLD
· OXDOCK
However, traditional methods are limited because:
· The lack of accuracy, when estimating the docking pose, results in an inability to accurately estimate the interaction of a ligand and protein.
· They lack the speed that is necessary to investigate many possible solutions in a reasonable amount of time.
Researchers at the University of Oxford have developed a molecular docking program, which addresses these problems. The key features and advantages of this docking algorithm are:
· A multi-scale approach to simplify and speed up the initial stages of molecular docking
The geometry of the entire ligand is fixed after three points are defined and thus after this stage, the entire molecule is docked to make the calculations more accurate.
· Evolutionary programming, which employs the concept of natural selection to find the optimum solution.
The advantage of an evolutionary approach is that it provides a method to find optimal solutions to complex problems without resorting to an exhaustive search.
The Oxford Invention provides a program that will optimise ligand molecules and a scoring function that accurately describes the binding of ligands to proteins. These are developed in tandem, yielding an algorithm that is accurate, precise, specific, and a distinct improvement, in many ways, on traditional methods..
