@article{68033c9f66af4dddaae25722666dc453,
title = "Investigating the allosteric response of the PICK1 PDZ domain to different ligands with all-atom simulations",
abstract = "The PDZ family is comprised of small modular domains that play critical roles in the allosteric modulation of many cellular signaling processes by binding to the C-terminal tail of different proteins. As dominant modular proteins that interact with a diverse set of peptides, it is of particular interest to explore how different binding partners induce different allosteric effects on the same PDZ domain. Because the PICK1 PDZ domain can bind different types of ligands, it is an ideal test case to answer this question and explore the network of interactions that give rise to dynamic allostery. Here, we use all-atom molecular dynamics simulations to explore dynamic allostery in the PICK1 PDZ domain by modeling two PICK1 PDZ systems: PICK1 PDZ-DAT and PICK1 PDZ-GluR2. Our results suggest that ligand binding to the PICK1 PDZ domain induces dynamic allostery at the αA helix that is similar to what has been observed in other PDZ domains. We found that the PICK1 PDZ-ligand distance is directly correlated with both dynamic changes of the αA helix and the distance between the αA helix and βB strand. Furthermore, our work identifies a hydrophobic core between DAT/GluR2 and I35 as a key interaction in inducing such dynamic allostery. Finally, the unique interaction patterns between different binding partners and the PICK1 PDZ domain can induce unique dynamic changes to the PICK1 PDZ domain. We suspect that unique allosteric coupling patterns with different ligands may play a critical role in how PICK1 performs its biological functions in various signaling networks.",
keywords = "PDZ, all-atom simulation, allosteric effect, dynamic coupling index, dynamic flexibility index, ligand",
author = "Stevens, {Amy O.} and Kazan, {I. Can} and Banu Ozkan and Yi He",
note = "Funding Information: This research was funded by the National Science Foundation Graduate Research Fellowship Program (Grant No. DGE-1939267), the National Science Foundation (Grant No. 2137558), the National Science Foundation (Grant No. 1901709), the Leverhulme Trust (RPG-2017-222), and the Gordon and Betty Moore Foundation (Award: 1715591). This work was also supported by the Substance Use Disorders Grand Challenge Pilot Research Award, the Research Allocations Committee (RAC) Award, and the University of New Mexico Office of the Vice President for Research WeR1 Faculty Success Program. We also acknowledge the Centre of Informatics—Tricity Academic Supercomputer & network (CI TASK) in Gdansk, Poland, for the availability of high-performance computing resources. Funding Information: This research was funded by the National Science Foundation Graduate Research Fellowship Program (Grant No. DGE‐1939267), the National Science Foundation (Grant No. 2137558), the National Science Foundation (Grant No. 1901709), the Leverhulme Trust (RPG‐2017‐222), and the Gordon and Betty Moore Foundation (Award: 1715591). This work was also supported by the Substance Use Disorders Grand Challenge Pilot Research Award, the Research Allocations Committee (RAC) Award, and the University of New Mexico Office of the Vice President for Research WeR1 Faculty Success Program. We also acknowledge the Centre of Informatics—Tricity Academic Supercomputer & network (CI TASK) in Gdansk, Poland, for the availability of high‐performance computing resources. Publisher Copyright: {\textcopyright} 2022 The Protein Society.",
year = "2022",
month = dec,
doi = "10.1002/pro.4474",
language = "English (US)",
volume = "31",
journal = "Protein Science",
issn = "0961-8368",
publisher = "Cold Spring Harbor Laboratory Press",
number = "12",
}