Review of 3D templates for in silico homology models of MATs: improved 3D model of hDAT

Jones, Charles B.; Dukat, Małgorzata

doi:10.1007/s00044-022-02863-5

Cited by 3 publications

(6 citation statements)

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“…There is no crystal structure of hDAT; thus, a homology model of hDAT was constructed using our previously described methods. 8 The MOD-ELLER 9.24 software package, employed to generate the homology model, utilizes a crystal structure template and the amino acid sequence of the protein of interest. The hDAT homology model employed three separate crystal structure templates: (1) PDB ID: 4XPB, a dDAT crystal structure, which exhibits the highest sequence identity with hDAT at 55.35%; (2) PDB ID: 6VRH, an hSERT crystal structure, which retains 92% query coverage; and (3) PDB ID: 4XPT, because its co-crystallized ligand 3,4-dichlorophenethylamine possesses chloro substituents in the 3-and 4-positions of the phenyl ring that correspond to the ones in 1b.…”

Section: ■ Methodsmentioning

confidence: 99%

“…To prepare the model, the co-crystallized ligand was removed, hydrogen atoms were added, and the protein was minimized using the Tripos Force Field with Gasteiger-Hückel charges within SybylX 2.1.1. There is no crystal structure of hDAT; thus, a homology model of hDAT was constructed using our previously described methods . The MODELLER 9.24 software package, employed to generate the homology model, utilizes a crystal structure template and the amino acid sequence of the protein of interest.…”

Section: Methodsmentioning

confidence: 99%

“…■ RESULTS AND DISCUSSION 3D Modeling. We generated an hSERT model using the crystal structure PDB ID: 5I6X as a template and utilized our previously reported models of hDAT, 8 to dock 1b−1d 100 times each at the S1 binding site of both transporters. The resultant docking solutions were scored using the ChemPLP scoring function in GOLD2020 and analyzed for molecular interactions within the binding pocket employing Hydropathic INTeractions (HINT) analysis.…”

Section: ■ Introductionmentioning

confidence: 99%

“…We initiated our studies with in silico 3D molecular modeling to test the above hypotheses. Hence, in this investigation, we generated both hDAT and hSERT models followed by docking studies and prepared 1b – 1d (i.e., re-synthesized 1b using the reported method and prepared novel analogues 1c and 1d ) and examined their functional activity at hDAT and hSERT.…”

Section: Introductionmentioning

confidence: 99%

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Do 2-(Benzoyl)piperidines Represent a Novel Class of hDAT Reuptake Inhibitors?

Jones

Eltit

Dukat

2023

ACS Chem. Neurosci.

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2-(Benzoyl)piperidines (analogues of 1a), structural hybrids of the clinically employed ADHD medication methylphenidate (2) and the abused synthetic cathinone pentedrone (3), have been previously reported to act as novel and selective reuptake inhibitors of the human dopamine transporter (hDAT). One of the more potent benzoylpiperidines, as is the case with methylphenidate analogues, is its 3,4-dichloroaryl counterpart. Here, we demonstrate using homology models that these compounds (i.e., benzoylpiperidines and methylphenidate analogues) likely bind in a comparable manner at hDAT. In addition, it is shown here that the 3,4-dichlorobenzoylpiperidine analogue of 1a is more potent than its 3,4-dimethyl counterpart, suggesting that the electronic character of the substituents might play a role in the potency of these hybrids. Furthermore, the 3,4-benz-fused (i.e., naphthyl) benzoylpiperidine analogue acts in the same manner as its corresponding methylphenidate counterpart at hDAT. As with its methylphenidate counterpart, the naphthyl compound also acts, rather uniquely (although with lower potency) relative to other members of the two series, at the human serotonin transporter (hSERT). In conclusion, the benzoylpiperidines represent a novel structural class of hDAT reuptake inhibitors that function in a manner similar to their methylphenidate counterparts.

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Section: ■ Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Do 2-(Benzoyl)piperidines Represent a Novel Class of hDAT Reuptake Inhibitors?

Jones

Eltit

Dukat

2023

ACS Chem. Neurosci.

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“…Computational studies may require the structure of, for instance, a target protein or additional experimentally derived data. Homology modeling has made great strides in recent years [ 43 ]. A detailed analysis and comparison of current known monoamine transporter (MAT) crystal structures and homology modeling focused on dopamine transporters (DAT), a major class of MATs, was reported by Jones et al [ 43 ].…”

Section: Computational Modelingmentioning

confidence: 99%

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Karabulut

Kaur²,

Gauld

2023

Molecules

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Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

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