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.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

“…We found that the covid-19 S protein shows the expected high correlation between the occurrence of mutations and site flexibility (Figure 3) when we compare %DFI (DFI ranked by percentile) to the average number of variants per position found within a given %DFI bin. Previous studies have indicated that rigid residues are critical for functional dynamics, thus more likely to impact function if mutated and, generally, can lead to a loss of function and thus more conserved (Kim, H. et al 2015; Butler et al 2018; Modi, Risso, et al 2021; Modi, Campitelli, et al 2021; Kazan et al 2022; Ose, Butler, et al 2022; Stevens et al 2022; Campitelli et al 2020a; Kumar, A., Glembo, T.J., Ozkan, S.B. 2015b).…”

“…Previous studies have indicated that rigid residues are critical for functional dynamics, thus more likely to impact function if mutated and, generally, can lead to a loss of function and thus more conserved (Kim, H. et al 2015;Butler et al 2018;Modi, Risso, et al 2021;Modi, Campitelli, et al 2021;Kazan et al 2022;Ose, Butler, et al 2022;Stevens et al 2022;Campitelli et al 2020a;Kumar, A., Glembo, T.J., Ozkan, S.B. 2015b).…”

Allosteric regulatory control in dihydrofolate reductase is revealed by dynamic asymmetry

“…Our DFI metric calculates the relative flexibility/rigidity of individual residues in an protein (Campitelli et al, 2020; Kumar et al, 2015; Larrimore et al, 2017; Modi, Risso, et al, 2021; Stevens et al, 2022). The DFI algorithm, which is developed using linear response theory and perturbation response scanning, calculates the average response of a residue as a result of a perturbation on every other residue in a protein (Gerek & Ozkan, 2011).…”

Engineering gain‐of‐function mutants of a WW domain by dynamics and structural analysis

“…Dynamic flexibility index (Butler et al, 2018; Gerek & Ozkan, 2011; Kazan et al, 2022; Kumar et al, 2015; Larrimore et al, 2017; Modi, Risso, et al, 2021; Ose et al, 2022; Stevens et al, 2022) uses the PRS technique that combines Elastic Network Model (ENM) and Linear Response Theory (LRT) (Nevin Gerek et al, 2013). In PRS, Brownian‐like unit forces are applied sequentially to each residue as perturbations (Atilgan et al, 2010; Kumar et al, 2015).…”

“…We prepared the time series %DFI by averaging 3 adjacent time windows, respectively (0.5 μs-1 μs, 1 μs-1.5 μs, and 1.5 μs-2 μs). As our earlier works (Butler et al, 2015;Kazan et al, 2022;Larrimore et al, 2017;Modi, Campitelli, et al, 2021) highlight that the DFI profiles capture the related function (Butler et al, 2018;Campitelli et al, 2021;Kolbabakartchner et al, 2021;Modi, Risso, et al, 2021;Ose et al, 2022;Stevens et al, 2022;Zou et al, 2015), we cluster these time series of the DFI values of each variant using PCA (see Section 3.4) to compare their dynamics profiles. The first two principal components are responsible for most of the variance in the mutant DFI profiles.…”