The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest

Abstract

Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

Figure 1.

Extending STRING with user-submitted genomes. Submitted genomes are first searched against the existing STRING genomes, and orthology is used to transfer all relevant information (interactions, annotations) from closely related organisms. The submitted genomes then become available on the web interface, via the programmatic Application Programming Interface (API), and for bulk downloads.

Figure 2.

Improved interaction prediction based on co-expression. Interaction scores based on co-expression have been ranked and benchmarked against common KEGG pathway membership as ground truth. (A) Performance comparison of co-expression network between STRING version 11.5 and STRING version 12.0. (B) Overview of the performance of all expression datasets contributing to the STRING version 12.0 co-expression channel.

Figure 3.

Processing and scoring of experimental interaction evidence. Experimental evidence is retrieved from several public databases. Protein pairs from low-throughput (LT) experiments are grouped by detection method and pairs coming from high-throughput (HT) experiments are grouped by experiment. Within each group, pairs are benchmarked against the KEGG pathway database to assess the confidence of identifying functional associations. All LT pairs are assigned the benchmark score derived for the particular detection method. HT pairs are scored based on calibration on the experiment level.

Figure 4.

Reliability estimates for protein–protein interaction detection assays. The top three most prolific experimental interaction assay types are shown, ranked by the number of protein pairs to which they contribute in STRING. Benchmarked on KEGG pathways, they yield (on average) lower confidence scores when derived from the high-throughput assays (A), in contrast, for the equivalent low-throughput experiments (shown in color), the predicted confidence is substantially higher (B). The distributions shown in (A) encompass all HT interactions of a given assay type (for simplicity); in the actual scoring computations in STRING, each HT interaction datasets is scored separately.