Towards high-throughput, high-performance computational estimation of binding affinities for patient specific HIV-1 protease sequences

The rapid acquisition of mutations conferring resistance to particular drugs remains a significant cause of anti-HIV treatment failure. Informatics based techniques give resistance scores to individual mutations which can be combined additively to assess the resistance levels of complete sequences. It is likely however, that the full picture is more complicated, with non-linear epistatic effects between combinations of mutations playing an important role in determining the level of viral resistance [1, 2]. Molecular dynamics is one simulation technique which offers the ability to derive quantitative (as well as qualitative) insight into the interplay of resistance-causing mutations. The dynamics of sequence specific models can be simulated and the free energy change associated with the drug binding calculated. The free energy change (or binding affinity) is the thermodynamic quantity which determines how tightly a drug will bind to its target. Hence, comparing values for mutant and wildtype systems allows the level of resistance of a particular sequence to be estimated. Validating such an approach is computationally demanding and the management of large numbers of simulations is a considerable administrative challenge. Here we present the use of tools based on the Simple API for Grid Applications (SAGA) to tackle this computational problem. This allows us to utilise high-end infrastructure, such as TeraGrid/XD, to provide extreme scales of throughput for highperformance simulations. This paper presents initial results and experience of using the TeraGrid in conjunction with DEISA, the European analogue of the US TeraGrid. High-throughput (high-performance) calculations are one of the few classes of computational problems that can easily exploit the computational power of widely distributed computing resources, thereby amassing more computational power than any of the individual resources could offer. In addition to the resource utilization rationale the need for cross-Grid capabilities in this case arises from shared and complementary scientific and technical skills found in this intercontinental collaboration.