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I asked this question Dimethyltryptamine and Sigma 1-type opioid receptor interaction but it seems that I didn't express myself well. I was looking for the place on Sigma 1 type opioid receptor where dimethyltryptamine binds rather than the location of Sigma 1 type opioid receptor.

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There is no definitive answer to this question since the 3D structure of the receptor has not been determined. In this paper;

Laurini et al. (2012) Another Brick in the Wall. Validation of the σ1 Receptor 3D Model by Computer-Assisted Design, Synthesis, and Activity of New σ1 Ligands. Molecular Pharmaceutics 9:3107-3126

the authors report the analysis of their molecular model of the receptor. They design 33 new ligands for the putative binding site with binding affinities across five orders of magnitude and then go on to show that the experimentally-determined affinities are in very good agreement with their in silico studies. The paper provides lots of information about the proposed binding site. The paper also describes the use of in silico alanine scanning mutagenesis to assess the relative importance of residues proposed to form the binding site: these are D126,I128,T151,V152,E172,Y173 and L182. Of these D126 and Y173 seem to be critical.

I must stress that this work does not directly investgate the binding of dimethyltryptamine, and the ligands that are used are not based on an indole ring structure. The molecular model used by the authors was first published in an earlier paper.

Laurini et al. (2011) Homology Model and Docking-Based Virtual Screening for Ligands of the sigma(1) Receptor ACS Medicinal Chemistry Letters 2:834-839

I tried to find out if that paper looks at dimethyltryptamine but unfortunately there seems to be a broken link at the source. Here is the abstract of that paper:

This study presents for the first time the 3D model of the sigma(1) receptor protein as obtained from homology modeling techniques, shows the applicability of this structure to docking-based virtual screening, defines a computational strategy to optimize the results based on a combination of 3D pharmacophore-based docking and MM/PBSA free energy of binding scoring, and provides evidence that these in silico models and recipes are powerful tools on which virtual screening of new sigma(1) ligands can be based. In particular, the validation of the applicability of docking-based virtual screening to homology models is of utmost importance, since no crystal structure is available to date for the sigma(1) receptor, and this missing information still constitutes a major hurdle for a rational ligand design for this important protein target.

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