Abstract
Multicanonical molecular dynamics (McMD)-based dynamic docking is a powerful tool to not only predict the native binding configuration between two flexible molecules, but it can also be used to accurately simulate the binding/unbinding pathway. Furthermore, it can also predict alternative binding sites, including allosteric ones, by employing an exhaustive sampling approach. Since McMD-based dynamic docking accurately samples binding/unbinding events, it can thus be used to determine the molecular mechanism of binding between two molecules. We developed the McMD-based dynamic docking methodology based on the powerful, but woefully underutilized McMD algorithm, combined with a toolset to perform the docking and to analyze the results. Here, we showcase three of our recent works, where we have applied McMD-based dynamic docking to advance the field of computational drug design. In the first case, we applied our method to perform an exhaustive search between Hsp90 and one of its inhibitors to successfully predict the native binding configuration in its binding site, as we refined our analysis methods. For our second case, we performed an exhaustive search of two medium-sized ligands and Bcl-xL, which has a cryptic binding site that differs greatly between the apo and holo structures. Finally, we performed a dynamic docking simulation between a membrane-embedded GPCR molecule and a high affinity ligand that binds deep within its receptor’s pocket. These advanced simulations showcase the power that the McMD-based dynamic docking method has, and provide a glimpse of the potential our methodology has to unravel and solve the medical and biophysical issues in the modern world.
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Data availability
The representative structures of our McMD-based dynamic docking simulations have been submitted to the Biological Structure Model Archive (https://bsma.pdbj.org) under BSMIDs BSM-00002, BSM-00007, BSM-00008, BSM-00010, BSM-00021, BSM-00024, and BSM-00029 (Bekker et al. 2020c).
The McMD-enhanced version of GROMACS is available from https://gitlab.com/gjbekker/gromacs, along with analysis tools.
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Acknowledgements
We are especially grateful to Prof. Haruki Nakamura for his advice and ideas regarding the development of our dynamic docking methodology.
Funding
This work was supported by Japan Agency for Medical Research and Development (AMED) to N.K., and by the Grand-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JP20H03229). It was performed in part under the Cooperative Research Program of the Institute for Protein Research, Osaka University, CR-21–05 and CR-22–05. Computational resources of the TSUBAME3.0 system, Tokyo Institute of Technology, were provided by the HPCI Research Project (hp190018, hp190021, hp190027, hp200011, hp200025, hp200063, hp210002, hp210005, hp210048, hp220002, hp220015, and hp220026).
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Bekker, GJ., Kamiya, N. Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking. Biophys Rev 14, 1349–1358 (2022). https://doi.org/10.1007/s12551-022-01010-z
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DOI: https://doi.org/10.1007/s12551-022-01010-z