Diversity of synaptic protein complexes as a function of the abundance of their constituent proteins: A modeling approach
The postsynaptic density (PSD) is a dense protein network playing a key role in information processing during learning and memory, and is also indicated in a number of neurological disorders. Efforts to characterize its detailed molecular organization are encumbered by the large variability of the a...
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description | The postsynaptic density (PSD) is a dense protein network playing a key role in information processing during learning and memory, and is also indicated in a number of neurological disorders. Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins (NMDAR, AMPAR, PSD-95, SynGAP, GKAP, Shank3, Homer1) based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients (altogether 524 different simulations). Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse. |
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Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins (NMDAR, AMPAR, PSD-95, SynGAP, GKAP, Shank3, Homer1) based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients (altogether 524 different simulations). Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009758</identifier><identifier>PMID: 35041658</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Association reactions ; Availability ; Binding sites ; Biology and Life Sciences ; Brain ; Circadian rhythms ; Complex formation ; Computer Simulation ; Constituents ; Data processing ; Experimental methods ; Gene expression ; Glutamate receptors ; Glutamic acid receptors ; Humans ; Information processing ; Kinases ; Medicine and Health Sciences ; Models, Neurological ; N-Methyl-D-aspartic acid receptors ; Nerve Tissue Proteins - chemistry ; Nerve Tissue Proteins - metabolism ; Neurological diseases ; Observations ; Permeability ; Physical Sciences ; Post-Synaptic Density - metabolism ; Post-Synaptic Density - physiology ; Postsynapse ; Postsynaptic density ; Postsynaptic density proteins ; Protein-protein interactions ; Proteins ; Research and Analysis Methods ; Research methodology ; Self-association ; Simulation ; Stochasticity ; Synapses - chemistry ; Synapses - metabolism ; α-Amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors</subject><ispartof>PLoS computational biology, 2022-01, Vol.18 (1), p.e1009758</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Miski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. 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Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins (NMDAR, AMPAR, PSD-95, SynGAP, GKAP, Shank3, Homer1) based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients (altogether 524 different simulations). Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35041658</pmid><doi>10.1371/journal.pcbi.1009758</doi><orcidid>https://orcid.org/0000-0002-6404-2598</orcidid><orcidid>https://orcid.org/0000-0002-2919-5601</orcidid><orcidid>https://orcid.org/0000-0002-1407-8728</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Association reactions Availability Binding sites Biology and Life Sciences Brain Circadian rhythms Complex formation Computer Simulation Constituents Data processing Experimental methods Gene expression Glutamate receptors Glutamic acid receptors Humans Information processing Kinases Medicine and Health Sciences Models, Neurological N-Methyl-D-aspartic acid receptors Nerve Tissue Proteins - chemistry Nerve Tissue Proteins - metabolism Neurological diseases Observations Permeability Physical Sciences Post-Synaptic Density - metabolism Post-Synaptic Density - physiology Postsynapse Postsynaptic density Postsynaptic density proteins Protein-protein interactions Proteins Research and Analysis Methods Research methodology Self-association Simulation Stochasticity Synapses - chemistry Synapses - metabolism α-Amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors |
title | Diversity of synaptic protein complexes as a function of the abundance of their constituent proteins: A modeling approach |
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