Difference between revisions of "Part:BBa K5477015"

Latest revision as of 22:29, 1 October 2024

LexA-ERRγ Chimeric activator with LexA DNA binding domain fused with LBD of ERRγ

The LexA-ERRγ (LexA-Estrogen-Related Receptor Gamma) combines the DNA-binding domain (DBD) of the LexA and the ligand-binding domain (LBD) of Estrogen-Related Receptor Gamma (ERRγ). ERRγ is classified as an orphan nuclear receptor (3).

ERRγ is ligand-independent and behaves as constitutive activator. 4-hydroxytamoxifen (4-OHT) has been proven to act as an inverse agonist and BPA has an antagonistic effect on this agonist meaning that BPA preserves the constitutive activity of ERRγ (3).

In the LexA-ERRγ fusion, the LexA DBD binds to LexA operator sequences (Lex6Op) in the reporter module, while the ERRγ LBD modulates transcriptional activation of NanoLuc BBa_K5477031.

The sequence for the LexA domain was from the paper of Zhou et al. 2022. This was fused with either the ligand-binding domain of the wild-type ERα, the mutant ERα or the Estrogen-Related Receptor gamma ERRγ. An alignment of the ligand-binding domains of the aforementioned was performed to determine the exact sequence to fuse with LexA to generate a chimeric activator that will bind to the Lex6Op.

Figure 1 - Multiple Sequence Alignment of Estrogen receptors - alpha and beta with Estrogen-related receptor gamma

Figure 2 - 3D Structure Prediction of Estrogen-related receptor gamma showing residue 217 found in the loop. The curved green line divides the structure and shows the ligand-binding domain of ERRγ. From residue 217 until the end of the amino acid sequence is fused downstream with LexA DNA binding domain to generate the chimeric activator.

Learnings from the Wetlab

This part was used as a receptor module intended to detect BPA (1) (2) (3) (4). The biosensor did not yield conclusive results. Attempts were made to suppress the receptor’s inherent constitutive activity using 4-OHT. However, the data remained inconsistent and irreproducible, with responses to the tested compounds either absent or irregular. As a result, no definitive conclusions could be drawn from the experiments.

Results from Drylab

Aim: The aim of this study is to investigate the structural and functional implications of ERRγ mutations, particularly their impact on ERRγ's interaction with HSP90 and ligand binding affinity to environmental and endogenous ligands (such as BPA and E2). Using computational approaches, including HADDOCK docking and molecular dynamics simulations, we aim to elucidate the mechanistic changes induced by specific mutations that modulate ERRγ's behavior, drawing comparisons to the glucocorticoid receptor (GR).

Objectives

1. Investigate the interaction between ERRγ(wild type and mutant) and HSP90 by using HADDOCK docking simulations to model the interaction of both the wild-type and mutant ERRγ proteins with the HSP90-p23 complex and identifying the structural changes induced by the "ATLPQLTPT" to "LVQPAKKPY" sequence substitution in ERRγ, and assess the degree of interaction with HSP90 compared to wild-type ERRγ and the GR.

2. Assess structural flexibility using Molecular Dynamics (MD) simulations by performing MD simulations for the wild-type ERRγ, mutant ERRγ, and glucocorticoid receptor (GR) to compare their structural flexibility, stability, and conformational changes over time and using RMSD data to evaluate the structural stability and dynamic behavior of each protein in solution.

3. Determine the impact of ERRγ mutations on ligand binding affinity by performing AutoDock Vina simulations to assess the binding affinity of ERRγ mutants (A272L and F450Y) and wild-type ERRγ for bisphenol A (BPA) and estradiol (E2) and analyze how mutations in ERRγ's ligand-binding domain affect the receptor’s selectivity and affinity for these ligands, providing insights into the structural changes driving differential binding preferences.

4. Analyze cluster and energy landscapes from docking results by using cluster analysis and energy decomposition (electrostatic, van der Waals, desolvation) from HADDOCK to identify which structural features and interactions contribute to optimal docking of ERRγ with HSP90 and ligands and correlate these findings with experimental binding affinities and MD results to understand the functional consequences of ERRγ mutations.

5. Draw comparisons between ERRγ mutants and the glucocorticoid receptor (GR) by comparing the structural, dynamic, and functional properties of ERRγ mutants to GR in terms of HSP90 interaction and structural flexibility and identify whether the ERRγ mutants mimic GR-like behavior, particularly in their dependence on HSP90 for stability.

For more details and better resolutions of the figures, visit MilkClear's Wiki: https://2024.igem.wiki/ucopenhagen/model

Analysis

HADDOCK Modeling

Figure XI: HADDOCK Models for Mutant ERRγ and Wild-type ERRγ

The models show docking simulations for both the mutant and wild-type ERRγ in complex with HSP90-p23. The docking suggests key differences between the wild type and mutant ERRγ in how they interact with HSP90:

Mutant ERRγ (upper lane): The mutant ERRγ model shows stronger reliance on HSP90, likely due to the sequence substitution of “ATLPQLTPT” (from GR) into ERRγ’s “LVQPAKKPY”. This substitution introduces features that mimic the interaction GR has with HSP90, possibly leading to a more stable or favorable docking pose with HSP90 and p23.

Wild-type ERRγ(lower lane): The wild-type ERRγ interacts less strongly with HSP90, which aligns with the hypothesis that the mutant version has adopted greater dependence on HSP90 for stability, akin to the glucocorticoid receptor (GR). The wild-type model may present fewer interactions or a less favorable docking pose, suggesting a weaker affinity for the HSP90-p23 complex.

The various boxplots and scatter plots you shared show how different clusters of docked complexes compare in terms of interaction energy types (e.g., electrostatic, van der Waals, desolvation) and RMSD:

1) Electrostatic Energy: In several clusters, the electrostatic energy is significantly negative, which suggests favorable interactions between ERRγ and its binding partners.

2) van der Waals Energy: This is also negative, indicating hydrophobic or steric contributions to the binding process. Lower van der Waals energy values are seen for certain clusters, which suggests that the mutant or wild-type ERRγ complexes might engage in hydrophobic packing.

3) Restraints Energy: This term refers to the energy contribution from any distance or angle constraints applied during the docking process. The plots indicate clusters with low restraints energy, suggesting a good fit of the ligand in the binding pocket. Some outliers with extremely high restraint energy suggest poor docking or large deviations from optimal geometry.

These energies and RMSD values help identify the best-docked structures and provide insights into how specific ERRγ variants interact with their ligands and HSP90.

Molecular Dynamics Simulations

Figure XII: Molecular Dynamics Simulation (ERR-g Mutant, Wild-type, and GR)

The graph shows the Root Mean Square Deviation (RMSD) of three proteins—ERRγ wild type, mutant ERRγ, and glucocorticoid receptor (GR)—over a 1 ns molecular dynamics simulation in water.

GR (blue line) displays the highest degree of flexibility and conformational changes during the simulation. Its RMSD fluctuates more than both forms of ERRγ, suggesting that GR undergoes larger structural changes over time. Mutant ERRγ(orange line) shows moderate fluctuations, with an increasing RMSD trend over the course of the simulation. This indicates that the mutant ERRγ also undergoes structural changes, although not as drastic as GR. This might reflect its partial adoption of GR-like behavior in complex with HSP90. Wild-type ERRγ(pink line) displays the lowest RMSD and the least fluctuation, indicating that it maintains a more stable conformation during the simulation. This suggests that the wild-type form has less structural flexibility, possibly correlating with a lower affinity for HSP90, unlike the mutant.

Overall, these dynamics suggest that the mutant ERRγ behaves more similarly to GR in terms of structural flexibility, which supports the idea that it has acquired GR-like interactions with HSP90. The wild-type form remains more stable, indicating its lesser dependence on HSP90.

Binding Affinity Data (ERRγ with BPA and E2) and Docking Results

A272L Mutation:This mutant shows the strongest binding affinity for BPA (-7.1 kcal/mol), indicating that the A272L mutation increases ERRγ's affinity for BPA significantly compared to the wild type. This mutation may create more favorable interactions with BPA, potentially due to steric or hydrophobic adjustments in the binding pocket. The binding affinity for E2 is significantly lower (9.2 kcal/mol), suggesting that this mutation is detrimental to E2 binding, which could be due to the loss of key interactions.

F450Y Mutation: This mutant shows a moderate decrease in BPA binding affinity (-5.4 kcal/mol), which is slightly better than the wild-type affinity for BPA. This suggests that the F450Y mutation might make minor improvements to the interaction with BPA. For E2, the binding affinity (1.5 kcal/mol) is poor, indicating that this mutation disrupts the ability to interact with E2. Similar to A272L, this suggests that the binding pocket no longer favors the native ligand.

Wild-type ERRγ: The wild-type ERRγ has moderate binding affinity for BPA (-5.2 kcal/mol), suggesting some baseline interaction between ERRγ and BPA. However, the mutants A272L and F450Y both show alterations that improve or slightly adjust this interaction. The wild type has a better binding affinity for E2 (-4.8 kcal/mol) compared to the mutants, suggesting that the wild-type form is optimized for interacting with this natural ligand. The mutations disrupt these favorable interactions, indicating that they may induce structural changes that diminish the receptor's ligand binding.

Conclusions

HSP90 Interaction: Mutant ERRγ seems to mimic GR’s dependence on HSP90, as supported by both the HADDOCK docking results and molecular dynamics simulations.

Binding Affinity: The A272L mutant exhibits the highest binding affinity for BPA, suggesting that mutations in ERRγ can enhance its ability to bind non-endogenous ligands like BPA. However, these mutations generally decrease the receptor's affinity for its natural ligand E2, indicating a trade-off between binding different ligands.

Cluster and Energy Analysis: The docking results indicate that certain clusters have more favorable interactions (as measured by electrostatic and van der Waals energy), which correlate with better binding affinity and lower RMSD values.

Sequence and Features

References

1. Ayami Matsushima, Takamasa Teramoto, Yoshimitsu Kakuta, Crystal structure of endocrine-disrupting chemical bisphenol A and estrogen-related receptor γ, The Journal of Biochemistry, Volume 171, Issue 1, January 2022, Pages 23–25, https://doi.org/10.1093/jb/mvab145

2. Çiftçi S, Yalçın SS, Samur G. Bisphenol A Exposure in Exclusively Breastfed Infants and Lactating Women: An Observational Cross-sectional Study. J Clin Res Pediatr Endocrinol. 2021 Nov 25;13(4):375-383. doi: 10.4274/jcrpe.galenos.2020.2021.0305. Epub 2021 Mar 22. PMID: 33749218; PMCID: PMC8638632.

3. Takayanagi S, Tokunaga T, Liu X, Okada H, Matsushima A, Shimohigashi Y. Endocrine disruptor bisphenol A strongly binds to human estrogen-related receptor gamma (ERRgamma) with high constitutive activity. Toxicol Lett. 2006;167(2):95-105. doi:10.1016/j.toxlet.2006.08.012

4. Zhou, T., Liang, Z. & Marchisio, M.A. Engineering a two-gene system to operate as a highly sensitive biosensor or a sharp switch upon induction with β-estradiol. Sci Rep 12, 21791 (2022). https://doi.org/10.1038/s41598-022-26195-x