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Structural modeling for the open state of an NMDA receptor.

ABSTRACT: NMDA receptors are tetrameric ligand-gated ion channels that are crucial for neurodevelopment and higher order processes such as learning and memory, and have been implicated in numerous neurological disorders. The lack of a structure for the channel open state has greatly hampered the understanding of the normal gating process and mechanisms of disease-associated mutations. Here we report the structural modeling for the open state of an NMDA receptor. Staring from the crystal structure of the closed state, we repacked the pore-lining helices to generate an initial open model. This model was modified to ensure tight packing between subunits and then refined by a molecular dynamics simulation in explicit membrane. We identify C?-H…O hydrogen bonds, between the C? of a conserved glycine in one transmembrane helix and a carbonyl oxygen of a membrane-parallel helix, at the extracellular side of the transmembrane domain as important for stabilizing the open state. This observation explains why mutations of the glycine are associated with neurological diseases and lead to significant decrease in channel open probability.

SUBMITTER: Pang X

PROVIDER: S-EPMC5733723 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Structural modeling for the open state of an NMDA receptor.

Pang Xiaodong X Zhou Huan-Xiang HX

Journal of structural biology 20170722 3

NMDA receptors are tetrameric ligand-gated ion channels that are crucial for neurodevelopment and higher order processes such as learning and memory, and have been implicated in numerous neurological disorders. The lack of a structure for the channel open state has greatly hampered the understanding of the normal gating process and mechanisms of disease-associated mutations. Here we report the structural modeling for the open state of an NMDA receptor. Staring from the crystal structure of the c ...[more]

PMID: 28739483

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Project description:A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson's taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method's utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.

Project description:N-methyl-D-aspartate receptors (NMDARs)-i.e., transmembrane proteins expressed in neurons-play a central role in the molecular mechanisms of learning and memory formation. It is unclear how the known atomic structures of NMDARs determined by x-ray crystallography and electron cryomicroscopy (18 published Protein Data Bank entries) relate to the functional states of NMDARs inferred from electrophysiological recordings (multiple closed, open, preopen, etc. states). We address this problem by using molecular dynamics simulations at atomic resolution, a method successfully applied in the past to much smaller biomolecules. Our simulations predict that several conformations of NMDARs with experimentally determined geometries, including four "nonactive" electron cryomicroscopy structures, rapidly interconvert on submicrosecond timescales and therefore may correspond to the same functional state of the receptor (specifically, one of the closed states). This conclusion is not trivial because these conformational transitions involve changes in certain interatomic distances as large as tens of Å. The simulations also predict differences in the conformational dynamics of the apo and holo (i.e., agonist and coagonist bound) forms of the receptor on the microsecond timescale. To our knowledge, five new conformations of NMDARs, with geometries joining various features from different known experimental structures, are also predicted by the model. The main limitation of this work stems from its limited sampling (30 μs of aggregate length of molecular dynamics trajectories). Though this level significantly exceeds the sampling in previous simulations of parts of NMDARs, it is still much lower than the sampling recently achieved for smaller biomolecules (up to a few milliseconds), thus precluding, in particular, the observation of transitions between different functional states of NMDARs. Despite this limitation, such computational predictions may guide further experimental studies on the structure, dynamics, and function of NMDARs, for example by suggesting optimal locations of spectroscopic probes. Overall, atomic resolution simulations provide, to our knowledge, a novel perspective on the structure and dynamics of NMDARs, complementing information obtained by experimental methods.

Dataset Information

Structural modeling for the open state of an NMDA receptor.

Publications

Structural modeling for the open state of an NMDA receptor.

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