Simulating the Long-timescale Structural Behavior of Bacterial and Influenza Neuraminidases with Different HPC Resources
Understanding the conformational dynamics which affects ligand binding by Neuraminidases is needed to improve the in silico selection of novel drug candidates targeting these pathogenicity factors and to adequately estimate the efficacy of potential drugs. Conventional molecular dynamics (MD) is a powerful tool to study conformational sampling, drug-target recognition and binding, but requires significant computational effort to reach timescales relevant for biology. In this work the advances in a computer power and specialized architectures were evaluated at simulating long MD trajectories of the structural behavior of Neuraminidases. We conclude that modern GPU accelerators enable calculations at the timescales that would previously have been intractable, providing routine access to microsecond-long trajectories in a daily laboratory practice. This opens an opportunity to move away from the "static" affinity-driven strategies in drug design towards a deeper understanding of ligand-specific conformational adaptation of target sites in protein structures, leading to a better selection of efficient drug candidates in silico. However, the performance of modern GPUs is yet far behind the deeply-specialized supercomputers co-designed for MD. Further development of affordable specialized architectures is needed to move towards the much-desired millisecond timescale to simulate large proteins at a daily routine.
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