A systematic search method for the identification of tightly packed transmembrane parallel alpha-helices

Carbamate Insecticides Target Human Melatonin Receptors

Carbaryl (1-naphthyl methylcarbamate) and carbofuran (2,3-dihydro-2,2-dimethyl-7-benzofuranyl methylcarbamate) are among the most toxic insecticides, implicated in a variety of diseases including diabetes and cancer among others. Using an integrated pharmacoinformatics based screening approach, we have identified these insecticides to be structural mimics of the neurohormone melatonin and were able to bind to the putative melatonin binding sites in MT₁ and MT₂ melatonin receptors in silico. Carbaryl and carbofuran then were tested for competition with 2-[¹²⁵I]-iodomelatonin (300 pM) binding to hMT₁ or hMT₂ receptors stably expressed in CHO cells. Carbaryl and carbofuran showed higher affinity for competition with 2-[¹²⁵I]-iodomelatonin binding to the hMT₂ compared to the hMT₁ melatonin receptor (33 and 35-fold difference, respectively) as predicted by the molecular modeling. In the presence of GTP (100 μM), which decouples the G-protein linked receptors to modulate signaling, the apparent efficacy of carbaryl and carbofuran for 2-[¹²⁵I]-iodomelatonin binding for the hMT₁ melatonin receptor was not affected but significantly decreased for the hMT₂ melatonin receptor compatible with receptor antagonist/inverse agonist and agonist efficacy, respectively. Altogether, our data points to a potentially new mechanism through which carbamate insecticides carbaryl and carbofuran could impact human health by altering the homeostatic balance of key regulatory processes by directly binding to melatonin receptors.

Molecular dynamics simulation approach for the prediction of transmembrane helix–helix heterodimers assembly

Computational methods are useful to identify favorable structures of transmembrane (TM) helix oligomers when experimental data are not available or when they cannot help to interpret helix-helix association. We report here a global search method using molecular dynamics (MD) simulations to predict the structures of transmembrane homo and heterodimers. The present approach is based only on sequence information without any experimental data and is first applied to glycophorin A to validate the protocol and to the HER2-HER3 heterodimer receptor. The method successfully reproduces the experimental structures of the TM domain of glycophorin A (GpA(TM)) with a root mean square deviation of 1.5 A. The search protocol identifies three energetically stable models of the TM domain of HER2-HER3 receptor with favorable helix-helix arrangement, including right-handed and left-handed coiled-coils. The predicted TM structures exhibit the GxxxG-like motif at the dimer interface which is presumed to drive receptor oligomerization. We demonstrate that native structures of TM domain can be predicted without quantitative experimental data. This search protocol could help to predict structures of the TM domain of HER heterodimer family.

In Silico Screening of Mutational Effects on Transmembrane Helix Dimerization: Insights from Rigid-Body Docking and Molecular Dynamics Simulations

In this study, a docking-based protocol has been probed for its ability to predict the effects of 32 single and double mutations on glycophorin A (GpA) homodimerization. Rigid-body docking simulations have been paralleled by molecular dynamics (MD) simulations in implicit membrane. The rigid-body docking-based approach proved effective in reconstituting the native architecture of the GpA dimer for the wild type and the wild-type-like mutants. The good correlative models between the intermolecular interaction descriptors derived both by rigid-body docking simulations and by MD simulations and experimental relative free energies support the assumption that the mutation-induced changes in the association free energy of GpA helices are essentially ascribed to differences in the packing interactions, whereas almost all the variations in the entropic contributions to the association free energy are constant and/or negligible. The MD-based models achieved provide insights into the structural determinants for disruptive and restoring mutational effects. The computational approaches presented in this study are fast and effective, and constitute simple and promising tools for in silico screening of mutational effects on the association properties of integral membrane proteins.