The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour

Neuronal synapses play fundamental roles in information processing, behaviour and disease. Neurotransmitter receptor complexes, such as the mammalian N-methyl-D-aspartate receptor complex (NRC/MASC) comprising 186 proteins, are major components of the synapse proteome. Here we investigate the organisation and function of NRC/MASC using a systems biology approach. Systematic annotation showed that the complex contained proteins implicated in a wide range of cognitive processes, synaptic plasticity and psychiatric diseases. Protein domains were evolutionarily conserved from yeast, but enriched with signalling domains associated with the emergence of multicellularity. Mapping of protein–protein interactions to create a network representation of the complex revealed that simple principles underlie the functional organisation of both proteins and their clusters, with modularity reflecting functional specialisation. The known functional roles of NRC/MASC proteins suggest the complex co-ordinates signalling to diverse effector pathways underlying neuronal plasticity. Importantly, using quantitative data from synaptic plasticity experiments, our model correctly predicts robustness to mutations and drug interference. These studies of synapse proteome organisation suggest that molecular networks with simple design principles underpin synaptic signalling properties with important roles in physiology, behaviour and disease

Reconstructing protein complexes: From proteomics to systems biology

Modern high throughput technologies in biological science often create lists of interesting molecules. The challenge is to reconstruct a descriptive model from these lists that reflects the underlying biological processes as accurately as possible. Once we have such a model or network, what can we learn from it? Specifically, given that we are interested in some biological process associated with the model, what new properties can we predict and subsequently test? Here, we describe, at an introductory level, a range of bioinformatics techniques that can be systematically applied to proteomic datasets. When combined, these methods give us a global overview of the network and the properties of the proteins and their interactions. These properties can then be used to predict functional pathways within the network and to examine substructure. To illustrate the application of these methods, we draw upon our own work concerning a complex of 186 proteins found in neuronal synapses in mammals. The techniques discussed are generally applicable and could be used to examine lists of proteins involved with the biological response to electric or magnetic field