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Analysis of proteins in computational models of synaptic plasticity

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https://www.biorxiv.org/content/early/2018/01/28/254094
Original languageEnglish
PublisherbioRxiv, at Cold Spring Harbor Laboratory
Number of pages53
DOIs
Publication statusPublished - 28 Jan 2018

Abstract

The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models. To determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events. We cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family CACNA1C, dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase COMT and MAOA. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models. Our analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research.

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