Massively parallel simulation counters antibiotic resistance
A solution to the problem of antibiotic-resistant bacteria may be closer, as a result of a computer science project to investigate ways of making legacy software run efficiently on heterogeneous systems, the Nvidia GPU Technology Conference was told on 16 May.
Simon McIntosh-Smith, of the University of Bristol in the UK, presented results from a project that ported a very large molecular modelling program to systems with a mix of conventional CPUs and also GPUs. One early result has been the identification of around ten small molecules that could block the biological pathway that creates antibiotic resistance in bacteria. So confident are the researchers in their predictions that the candidate drug molecules are now being synthesised in the laboratory.
Typically, biomolecules such as proteins can be made up from between a thousand and two thousand atoms, whereas the docking molecules, the ligands, will be an order of magnitude smaller – about 50 to 100 atoms. The software is BUDE – the Bristol University Docking Engine – is used to predict the structure of small molecules that can bind tightly to the active sites in large biological molecules. It processes tens of millions of candidate ligands and uses a genetic algorithm-like methodology to select those that bind most tightly, using energy minimisation calculations. It is, said McIntosh-Smith, a very large piece of code, in the region of hundreds of thousands of lines of Fortran. However, only a few thousand lines needed to be ported across to GPU processors.
Because the ten million or so ligands all come in slightly different 'flavours' – they have flexible side chains, for example – the project represents 'an embarrassment of parallelism,' he continued. 'When we get down to one molecule, we want to test it in many different positions and rotations, so we have even more parallelism.'
The group has run 53 comparisons between the predictions from the computer code and actual measurements from X-ray crystallography and other experimental data. Only two results are outside the desirable bounds, so 'we are now getting very accurate simulation results,' McIntosh-Smith said.By porting the code across to a heterogeneous CPU+GPU system, he reported a factor of 20 increase in the speed of computation and a factor of 10 improvement in energy efficiency.
The simulation to try to find ligands that would block recently emerged antibiotic-resistance in a bacterium found in Asia took four days and surveyed more than 8 million candidate molecules. Once, he pointed out, it would have taken the biggest and fastest supercomputers in the world to do the calculation, which can now be done by a university research group.