Conformational Changes of Thyroid Receptors in Response to Antagonists

Fischer, André; Frehner, Gabriela; Lill, Markus A.; Smieško, Martin

doi:10.1021/acs.jcim.0c01403

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…In general, experimental methods can only provide limited insight into such ligand translocation processes [8] as, for example, the tunnels are often not apparent in static crystal structures. However, due to their dynamic nature, computational methods such as molecular dynamics (MD) simulations introducing a natural degree of structural flexibility have been widely applied to study ligand tunnels in atomic detail [8,10,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

Fischer

Smieško

2021

IJMS

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Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 μs of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.

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Section: Introductionmentioning

confidence: 99%

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

Fischer

Smieško

2021

IJMS

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“…The accessibility of TRβ crystal structures merely offers a reliable starting conformation for molecular dynamics. Previously, certain studies have explored the binding mechanism of TRβ antagonists via molecular dynamics . In this study, we utilized molecular dynamics to simulate the binding state of MGL-3196 with wild-type and multiple LBD mutants at the all-atom level.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, certain studies have explored the binding mechanism of TRβ antagonists via molecular dynamics. 22 In this study, we utilized molecular dynamics to simulate the binding state of MGL-3196 with wild-type and multiple LBD mutants at the all-atom level. We analyzed its binding mode and the effect of mutations on the maintenance of the TRβ active conformation with MGL-3196.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamic Simulation To Reveal the Mechanism Underlying MGL-3196 Resistance to Thyroxine Receptor Beta

Lu,

Chen,

Zhuang

et al. 2024

ACS Omega

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Thyroxine receptor beta (TRβ) is a ligand-dependent nuclear receptor that participates in regulating multiple biological processes, particularly playing an important role in lipid metabolism regulation. TRβ is currently a popular therapeutic target for nonalcoholic steatohepatitis (NASH), while no drugs have been approved to treat this disease. is the first TRβ agonist that has succeeded in phase III clinical trials for the treatment of NASH; therefore, studying its molecular mechanism of action is of great significance. In this study, we employed molecular dynamic simulation to investigate the interaction mode between MGL-3196 and TRβ at the all-atom level. More importantly, by comparing the binding patterns of MGL-3196 in several prevalent TRβ mutants, it was identified that the mutations R243Q and H435R located, respectively, around and within the ligand-binding pocket of TRβ cause TRβ to be insensitive to MGL-3196. This indicates that patients with NASH carrying these two mutations may exhibit resistance to the medication of MGL-3196, thereby highlighting the potential impact of TRβ mutations on TRβ-targeted treatment of NASH and beyond.

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“…Thanks to the rapid development of computational hardware (e.g., GPU acceleration) and artificial intelligence (e.g., AlphaFold2), in silico approaches featured by high-throughput screening may provide alternative methods for accelerating the characterization of agonists and antagonists for NRs. ,− Although numerous computational attempts have been conducted in discriminating or understanding the binding preference of NR ligands, − ,− for instance, Ramaprasad et al have built a series of ligand-based machine-learning (ML) models for discriminating actives (agonists and antagonists) from inactives (chemical background or compounds with low activity on the corresponding NR) for a number of NRs, few of them can distinguish between agonists and antagonists using a unified model with structural elucidation. One possible reason may be that it is hard to establish a universal and effective model by integrating (structural) information of multiple NRs into a unified framework, such as understanding why one ligand works as an agonist in one NR target, whereas functioning as an antagonist in another.…”

Section: Introductionmentioning

confidence: 99%

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Wang

Tian

et al. 2022

J. Chem. Inf. Model.

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The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

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Conformational Changes of Thyroid Receptors in Response to Antagonists

Cited by 5 publications

References 55 publications

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

Molecular Dynamic Simulation To Reveal the Mechanism Underlying MGL-3196 Resistance to Thyroxine Receptor Beta

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

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