TY - JOUR
T1 - Alchemical binding free energy calculations in AMBER20
T2 - Advances and best practices for drug discovery
AU - Lee, Tai Sung
AU - Allen, Bryce K.
AU - Giese, Timothy J.
AU - Guo, Zhenyu
AU - Li, Pengfei
AU - Lin, Charles
AU - Dwight McGee, T.
AU - Pearlman, David A.
AU - Radak, Brian K.
AU - Tao, Yujun
AU - Tsai, Hsu Chun
AU - Xu, Huafeng
AU - Sherman, Woody
AU - York, Darrin M.
N1 - Funding Information:
The authors are grateful for financial support provided by the National Institutes of Health (Grant GM107485 to D.M.Y.). Computational resources were provided by the Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey (specifically, the Amarel cluster and associated research computing resources), the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant ACI-1548562 (specifically, the resources COMET and COMET GPU at SDSC through allocation TG-CHE190067), and the Texas Advanced Computing Center (TACC) at the University of Texas at Austin (specifically, the Frontera Supercomputer through allocation CHE20002). We also gratefully acknowledge the support of the NVIDIA Corporation with the donation of several Pascal, Volta, and Turing GPUs for testing.
Funding Information:
The authors are grateful for financial support provided by the National Institutes of Health (Grant GM107485 to D.M.Y.). Computational resources were provided by the Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey (specifically, the Amarel cluster and associated research computing resources), the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant ACI-1548562294 (specifically, the resources COMET and COMET GPU at SDSC through allocation TGCHE190067), and the Texas Advanced Computing Center (TACC) at the University of Texas at Austin (specifically, the Frontera Supercomputer through allocation CHE20002). We also gratefully acknowledge the support of the NVIDIA Corporation with the donation of several Pascal, Volta, and Turing GPUs for testing.
Publisher Copyright:
© 2020 American Chemical Society
PY - 2020/11/23
Y1 - 2020/11/23
N2 - Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/ unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
AB - Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/ unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
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U2 - 10.1021/acs.jcim.0c00613
DO - 10.1021/acs.jcim.0c00613
M3 - Article
C2 - 32936637
AN - SCOPUS:85092052470
SN - 1549-9596
VL - 60
SP - 5595
EP - 5623
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 11
ER -