A Protein Language Model Reveals Organellar Calcium ATPases at Neuronal Synapses

Abstract

The synapse between neurons hosts the protein machinery for information transfer and storage in the brain. Its local proteome, however, contains many proteins with unclear roles for synaptic function. To glean hypotheses from this local proteome, we developed Synapse Gigamapper (SyGi), a protein language model for predicting protein localization at synapses. SyGi identified 152 amino-acid motifs that are indicative of localization at excitatory or inhibitory synapses and revealed >100 candidate constituents from key cellular pathways. Among these candidates is the endoplasmic reticulum (ER)-bound ATPase for cytosolic Ca2+ clearance, SERCA. We found clusters of SERCA copies at excitatory synapses without ER. Rather, SERCA is colocalized with compartment-specific organelles at synapses-- the spine apparatus and synaptic vesicles. Taken together, SyGi is useful for exposing hidden components of neuronal synapses.