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Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods

Mai, Huanghao and Zimmer, Matthew H. and Miller, Thomas F., III (2023) Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods. Journal of Physical Chemistry B, 127 (2). pp. 446-455. ISSN 1520-6106. PMCID PMC9869335. doi:10.1021/acs.jpcb.2c05795.

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Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC–E3 binding and PROTAC–target binding principally through protein–protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target–PROTAC–E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parametrization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current force field, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity.

Item Type:Article
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URLURL TypeDescription CentralArticle
Mai, Huanghao0000-0003-2278-0768
Zimmer, Matthew H.0000-0002-1437-2636
Miller, Thomas F., III0000-0002-1882-5380
Additional Information:© 2023 The Authors. Published by American Chemical Society. Attribution 4.0 International (CC BY 4.0) H.M. thanks William M. Clemons, Jr., Daniel Jacobson, Tomislav Begušić, Xuecheng Tao, Marta Gonzalvo, and Lixue Cheng for comments on the manuscript, and Zhen-Gang Wang and Christopher J. Balzer for technical discussions. We gratefully acknowledge support from the National Institutes of Health (NIH) R01GM138845 (8877_CIT, subaward), Amgen Chem-Bio-Engineering Award (CBEA), and DeLogi Trust Science and Technology Grant. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges computer at the Pittsburgh Supercomputing Center through allocation MCB160013. (67) XSEDE is supported by National Science Foundation grant number ACI-1548562. This work also used computational resources from the Resnick High Performance Computing Center, a facility supported by Resnick Sustainability Institute at the California Institute of Technology. The authors declare no competing financial interest.
Group:Resnick Sustainability Institute
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Caltech DeLogi FundUNSPECIFIED
Resnick Sustainability InstituteUNSPECIFIED
Issue or Number:2
PubMed Central ID:PMC9869335
Record Number:CaltechAUTHORS:20230209-988069100.19
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:119182
Deposited By: Research Services Depository
Deposited On:17 Mar 2023 15:23
Last Modified:17 Mar 2023 15:23

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