Dataset Information

LTP requires postsynaptic PDZ-domain interactions with glutamate receptor/auxiliary protein complexes.

ABSTRACT: Long-term potentiation (LTP) is a persistent strengthening of synaptic transmission in the brain and is arguably the most compelling cellular and molecular model for learning and memory. Previous work found that both AMPA receptors and exogenously expressed kainate receptors are equally capable of expressing LTP, despite their limited homology and their association with distinct auxiliary subunits, indicating that LTP is far more promiscuous than previously thought. What might these two subtypes of glutamate receptor have in common? Using a single-cell molecular replacement strategy, we demonstrate that the AMPA receptor auxiliary subunit TARP ?-8, via its PDZ-binding motif, is indispensable for both basal synaptic transmission and LTP. Remarkably, kainate receptors and their auxiliary subunits Neto proteins share the same requirement of PDZ-binding domains for synaptic trafficking and LTP. Together, these results suggest that a minimal postsynaptic requirement for LTP is the PDZ binding of glutamate receptors/auxiliary subunits to PSD scaffolding proteins.

SUBMITTER: Sheng N

PROVIDER: S-EPMC5899490 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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LTP requires postsynaptic PDZ-domain interactions with glutamate receptor/auxiliary protein complexes.

Sheng Nengyin N Bemben Michael A MA Díaz-Alonso Javier J Tao Wucheng W Shi Yun Stone YS Nicoll Roger A RA

Proceedings of the National Academy of Sciences of the United States of America 20180326 15

Long-term potentiation (LTP) is a persistent strengthening of synaptic transmission in the brain and is arguably the most compelling cellular and molecular model for learning and memory. Previous work found that both AMPA receptors and exogenously expressed kainate receptors are equally capable of expressing LTP, despite their limited homology and their association with distinct auxiliary subunits, indicating that LTP is far more promiscuous than previously thought. What might these two subtypes ...[more]

PMID: 29581259

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Project description:The assembly of functional biomolecular condensates often involves liquid-liquid phase separation (LLPS) of proteins with multiple modular domains, which can be folded or conformationally disordered to various degrees. To understand the LLPS-driving domain-domain interactions, a fundamental question is how readily the interactions in the condensed phase can be inferred from interdomain interactions in dilute solutions. In particular, are the interactions leading to LLPS exclusively those underlying the formation of discrete interdomain complexes in homogeneous solutions? We address this question by developing a mean-field LLPS theory of two stoichiometrically constrained solute species. The theory is applied to the neuronal proteins SynGAP and PSD-95, whose complex coacervate serves as a rudimentary model for neuronal postsynaptic densities (PSDs). The predicted phase behaviors are compared with experiments. Previously, a three SynGAP/two PSD-95 ratio was determined for SynGAP/PSD-95 complexes in dilute solutions. However, when this 3:2 stoichiometry is uniformly imposed in our theory encompassing both dilute and condensed phases, the tie-line pattern of the predicted SynGAP/PSD-95 phase diagram differs drastically from that obtained experimentally. In contrast, theories embodying alternate scenarios postulating auxiliary SynGAP-PSD-95 as well as SynGAP-SynGAP and PSD-95-PSD-95 interactions, in addition to those responsible for stoichiometric SynGAP/PSD-95 complexes, produce tie-line patterns consistent with experiment. Hence, our combined theoretical-experimental analysis indicates that weaker interactions or higher-order complexes beyond the 3:2 stoichiometry, but not yet documented, are involved in the formation of SynGAP/PSD-95 condensates, imploring future efforts to ascertain the nature of these auxiliary interactions in PSD-like LLPS and underscoring a likely general synergy between stoichiometric, structurally specific binding and stochastic, multivalent "fuzzy" interactions in the assembly of functional biomolecular condensates.

Project description:PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.

Dataset Information

LTP requires postsynaptic PDZ-domain interactions with glutamate receptor/auxiliary protein complexes.

Publications

LTP requires postsynaptic PDZ-domain interactions with glutamate receptor/auxiliary protein complexes.

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