Dataset Information

Atomic level characterization of the nonproton ligand-sensing domain of ASIC3 channels.

ABSTRACT: Acid-sensing ion channels (ASICs) are known to be primarily activated by extracellular protons. Recently, we characterized a novel nonproton ligand (2-guanidine-4-methylquinazoline, GMQ), which activates the ASIC3 channel subtype at neutral pH. Using an interactive computational-experimental approach, here we extend our investigation to delineate the architecture of the GMQ-sensing domain in the ASIC3 channels. We first established a GMQ binding mode and revealed that residues Glu-423, Glu-79, Leu-77, Arg-376, Gln-271, and Gln-269 play key roles in forming the GMQ-sensing domain. We then verified the GMQ binding mode using ab initio calculation and mutagenesis and demonstrated the critical role of the above GMQ-binding residues in the interplay among GMQ, proton, and Ca(2+) in regulating the function of ASIC3. Additionally, we showed that the same residues involved in coordinating GMQ responses are also critical for activation of the ASIC3(E79C) mutant by thiol-reactive compound DTNB. Thus, a range of complementary techniques provide independent evidence for the structural details of the GMQ-sensing domain at atomic level, laying the foundation for further investigations of endogenous nonproton ligands and gating mechanisms of the ASIC3 channels.

SUBMITTER: Yu Y

PROVIDER: S-EPMC3137073 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Atomic level characterization of the nonproton ligand-sensing domain of ASIC3 channels.

Yu Ye Y Li Wei-Guang WG Chen Zhi Z Cao Hui H Yang Huaiyu H Jiang Hualiang H Xu Tian-Le TL

The Journal of biological chemistry 20110517 28

Acid-sensing ion channels (ASICs) are known to be primarily activated by extracellular protons. Recently, we characterized a novel nonproton ligand (2-guanidine-4-methylquinazoline, GMQ), which activates the ASIC3 channel subtype at neutral pH. Using an interactive computational-experimental approach, here we extend our investigation to delineate the architecture of the GMQ-sensing domain in the ASIC3 channels. We first established a GMQ binding mode and revealed that residues Glu-423, Glu-79, L ...[more]

PMID: 21586569

Similar Datasets

Project description:BackgroundShape complementarity and non-covalent interactions are believed to drive protein-ligand interaction. To date protein-protein, protein-DNA, and protein-RNA interactions were systematically investigated, which is in contrast to interactions with small ligands. We investigate the role of covalent and non-covalent bonds in protein-small ligand interactions using a comprehensive dataset of 2,320 complexes.Methodology and principal findingsWe show that protein-ligand interactions are governed by different forces for different ligand types, i.e., protein-organic compound interactions are governed by hydrogen bonds, van der Waals contacts, and covalent bonds; protein-metal ion interactions are dominated by electrostatic force and coordination bonds; protein-anion interactions are established with electrostatic force, hydrogen bonds, and van der Waals contacts; and protein-inorganic cluster interactions are driven by coordination bonds. We extracted several frequently occurring atomic-level patterns concerning these interactions. For instance, 73% of investigated covalent bonds were summarized with just three patterns in which bonds are formed between thiol of Cys and carbon or sulfur atoms of ligands, and nitrogen of Lys and carbon of ligands. Similar patterns were found for the coordination bonds. Hydrogen bonds occur in 67% of protein-organic compound complexes and 66% of them are formed between NH- group of protein residues and oxygen atom of ligands. We quantify relative abundance of specific interaction types and discuss their characteristic features. The extracted protein-organic compound patterns are shown to complement and improve a geometric approach for prediction of binding sites.Conclusions and significanceWe show that for a given type (group) of ligands and type of the interaction force, majority of protein-ligand interactions are repetitive and could be summarized with several simple atomic-level patterns. We summarize and analyze 10 frequently occurring interaction patterns that cover 56% of all considered complexes and we show a practical application for the patterns that concerns interactions with organic compounds.

Project description:The difficulty in characterizing ion conduction through membrane channels at the level of individual permeation events has made it challenging to elucidate the mechanistic principles underpinning this fundamental physiological process. Using long, all-atom simulations enabled by special-purpose hardware, we studied K(+) permeation across the KV1.2/2.1 voltage-gated potassium channel. At experimentally accessible voltages, which include the physiological range, the simulated permeation rate was substantially lower than the experimentally observed rate. The current-voltage relationship was also nonlinear but became linear at much higher voltages. We observed permeation consistent with a "knock-on" mechanism at all voltages. At high voltages, the permeation rate was in accordance with our previously reported KV1.2 pore-only simulations, after the simulated voltages from the previous study were recalculated using the correct method, new insight into which is provided here. Including the voltage-sensing domains in the simulated channel brought the linear current-voltage regime closer to the experimentally accessible voltages. The simulated permeation rate, however, still underestimated the experimental rate, because formation of the knock-on intermediate occurred too infrequently. Reducing the interaction strength between the ion and the selectivity filter did not increase conductance. In complementary simulations of gramicidin A, similar changes in interaction strength did increase the observed permeation rate. Permeation nevertheless remained substantially below the experimental value, largely because of infrequent ion recruitment into the pore lumen. Despite the need to apply large voltages to simulate the permeation process, the apparent voltage insensitivity of the permeation mechanism suggests that the direct simulation of permeation at the single-ion level can provide fundamental physiological insight into ion channel function. Notably, our simulations suggest that the knock-on permeation mechanisms in KV1.2 and KcsA may be different.

Dataset Information

Atomic level characterization of the nonproton ligand-sensing domain of ASIC3 channels.

Publications

Atomic level characterization of the nonproton ligand-sensing domain of ASIC3 channels.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets