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Structurally elucidating transition pathways between protein conformations gives deep mechanistic insight into protein behavior but is typically difficult. Unbiased molecular dynamics (MD) simulations provide one solution, but their computational expense is often prohibitive, motivating the development of enhanced sampling methods that accelerate conformational changes in a given direction, embodied in a collective variable. The accuracy of such methods is unclear for complex protein transitions, because obtaining unbiased MD data for comparison is difficult. Here, we use long-time scale, unbiased MD simulations of epidermal growth factor receptor kinase deactivation as a complex biological test case for two widely used methods-steered molecular dynamics (SMD) and the string method. We found that common collective variable choices, based on the root-mean-square deviation (RMSD) of the entire protein, prevented the methods from producing accurate paths, even in SMD simulations on the time scale of the unbiased transition. Using collective variables based on the RMSD of the region of the protein known to be important for the conformational change, however, enabled both methods to provide a more accurate description of the pathway in a fraction of the simulation time required to observe the unbiased transition.

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Abstract-Special-purpose computing hardware can provide significantly better performance and power efficiency for certain applications than general-purpose processors. Even within a single application area, however, a special-purpose machine can be far more valuable if it is capable of efficiently supporting a number of different computational methods that, taken together, expand the machine's functionality and range of applicability. We have previously described a massively parallel special-purpose supercomputer, called Anton, and have shown that it executes traditional molecular dynamics simulations orders of magnitude faster than the previous state of the art. Here, we describe how we extended Anton's software to support a more diverse set of methods, allowing scientists to simulate a broader class of biological phenomena at extremely high speeds. Key elements of our approach, which exploits Anton's tightly integrated hardwired pipelines and programmable cores, are applicable to the hardware and software design of various other specialized or heterogeneous parallel computing platforms.