Translocatome: a novel resource for the analysis of protein translocation between cellular organelles

Abstract

Here we present Translocatome, the first dedicated database of human translocating proteins (URL: http://translocatome.linkgroup.hu). The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13 066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein–protein interaction database (http://comppi.linkgroup.hu). As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field.

The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization

“…1b). Moreover, using a dataset of 1233 known and machine learning-predicted translocating proteins from the Translocatome database 24 , we found that 41.2% EMF proteins (98 out of 238) could change their subcellular localization upon a regulatory event, representing, again, a higher fraction than in the PPI network (9%, two-sided Fisher's Exact Test, OR = 7.8, P-value < 2.2 x 10 -16 ) (Fig. 1b).…”

The role of 3′UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization

“…We also observed that 111 EMF proteins (46.6% of the total) belong to the multilocalizing proteome, defined by immunofluorescence-based approaches in the Human Protein Atlas (HPA) ( 31 ), a significantly higher fraction than in the PPI network (36.7%, two-sided Fisher's Exact Test, odds ratio (OR) = 1.52, P -value = 1.7 × 10 −3 ) (Figure 1B ). Moreover, using a dataset of 1233 known and machine learning-predicted translocating proteins from the Translocatome database ( 34 ), we found that 41.2% EMF proteins (98 out of 238) could change their subcellular localization upon a regulatory event, representing, again, a higher fraction than in the PPI network (9%, two-sided Fisher's Exact Test, OR = 7.8, P -value<2.2 × 10 −16 ) (Figure 1B ). Overall, this indicates that EMF proteins are more often localized in different cellular districts than expected, a feature that may be regulated by alternative 3′UTRs.…”

Temporal dynamics of protein complex formation and dissociation during human cytomegalovirus infection

“…By integrating our knowledge of protein localization during HCMV infection 12 and the translocatome dataset 55 , we observed that many proteins predicted to translocate do not display altered aggregation profiles. It is possible that some translocation events occur via endosomal trafficking of complexes, rather than individual protein movements.…”

“…Therefore, we interrogated our data to determine if proteins known or predicted to translocate display changes in aggregation properties. We assessed two resources of translocating proteins-the translocatome database 55 (translocations outside the context of infection) and our report of potentially translocating proteins during HCMV infection 12 (Supplementary Data 5). Surprisingly, for both datasets, most Tm values were not changed at any infection time point compared to mock ( Fig.…”