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Analysis of proteins in computational models of synaptic plasticity

View ORCID ProfileKatharina F. Heil, Emilia M. Wysocka, Oksana Sorokina, View ORCID ProfileJeanette Hellgren Kotaleski, View ORCID ProfileT. Ian Simpson, J. Douglas Armstrong, View ORCID ProfileDavid C. Sterratt
doi: https://doi.org/10.1101/254094
Katharina F. Heil
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
2Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
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Emilia M. Wysocka
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
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Oksana Sorokina
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
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Jeanette Hellgren Kotaleski
2Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
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T. Ian Simpson
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
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J. Douglas Armstrong
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
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David C. Sterratt
1School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
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  • For correspondence: david.c.sterratt@ed.ac.uk
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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 MAO A. 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.

Author summary The 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting more proteins should be added to models. We used lists of genes associated with neurological diseases to suggest proteins to include in future models.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 28, 2018.
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Analysis of proteins in computational models of synaptic plasticity
Katharina F. Heil, Emilia M. Wysocka, Oksana Sorokina, Jeanette Hellgren Kotaleski, T. Ian Simpson, J. Douglas Armstrong, David C. Sterratt
bioRxiv 254094; doi: https://doi.org/10.1101/254094
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Analysis of proteins in computational models of synaptic plasticity
Katharina F. Heil, Emilia M. Wysocka, Oksana Sorokina, Jeanette Hellgren Kotaleski, T. Ian Simpson, J. Douglas Armstrong, David C. Sterratt
bioRxiv 254094; doi: https://doi.org/10.1101/254094

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