The chemical databases that were accessed are ChEMBL (www.ebi.ac.uk/chembl/) (Davies et al., 2015; Mendez et al., 2019) (RRID:SCR_014042) and PubChem (pubchem.ncbi.nlm.nih.gov/) (Kim et al., 2021) (RRID:SCR_004284). The databases were searched with the term “suramin”, and also with the identification numbers (ID) of suramin and its various salts and further derivatives. For PubChem, the IDs used were 8514, 5361, 135538647, 16760668, 11979655, 11979631, 3943541, 11979654, 49772374, 49771850, 54600747, and 11979493. PubMed (pubmed.ncbi.nlm.nih.gov/) (RRID:SCR_004846) was used as a literature database, searching e.g., with “suramin AND target” and manually sorting out the relevant publications from the retrieved results.

All protein sequences were obtained from UniProt (www.uniprot.org) (UniProt-Consortium, 2021) (RRID:SCR_004426) except those of viruses, which were obtained from PDB (https://www.rcsb.org) (RRID:SCR_012820) (PDB-Consortium, 2019). PDB was resorted to in order to make sure that the processed, functional polypeptides were retrieved rather than the whole viral polyproteins. Reviewed entries were used preferably. For posttranslationally cleaved proteins (e.g., thrombin), the sequence of the precursor was used (e.g., prothrombin). For proteins with several isoforms, only the isoform stated by the reference was included; if no such information was provided, the longest isoform was selected.

All procedures were automated with self-made Perl (RRID:SCR_018313) scripts on a BioLinux platform (Field et al., 2006) (RRID:SCR_005399). The scripts served to run the described programs for profile and motif searching, and to parse the programs’ output into tabular format for further analysis. All scripts were tested for accuracy by monitoring the overall numbers of sequences processed and by manual re-testing of individual samples. The scripts are available on request.

Needleman-Wunsch global alignments (Needleman & Wunsch, 1970) were performed with “needle” of the EMBOSS 6.6.0 suite (Rice et al., 2000) (RRID:SCR_008493). Protein distance, defined as d = 1—(No. Similar residues/alignment length), was calculated for all pairs of proteins. The frequency distribution of the distances was visualized with RStudio (version 1.2.1335) (RRID:SCR_000432) using R (version 3.6.0) (RRID:SCR_001905).

Isoelectric points of amino acid sequences were determined with the command “iep” of EMBOSS 6.6.0 (Rice et al., 2000) (RRID:SCR_008493). It calculates the isoelectric point of an amino acid sequence by estimating the overall charge at different pH values. This was performed for the suramin targets as well as for the human proteome, downloaded from UniProt (www.uniprot.org; accession UP000005640; date: 06.01.20). Statistical tests were done in RStudio (version 1.2.1335) (RRID:SCR_000432) using R (version 3.6.0) (RRID:SCR_001905).

Motifs were identified using “hmmscan” with tabular output of the HMMer 3.2.1 package (hmmer.org/) (Eddy, 2009, 2011) (RRID:SCR_005305) against Pfam version 32.0 (El-Gebali et al., 2019) (RRID:SCR_004726). The expectancy (E) value cut-off was set to 0.01. Pfam accessions were linked to ‘molecular function’ GO terms by using a text file produced by GODomainMiner (Alborzi et al., 2018) providing associations between GO term id and Pfam accession numbers (godm.loria.fr/). The GO names were retrieved from QuickGO (www.ebi.ac.uk/QuickGO/) (Binns et al., 2009) (RRID:SCR_004608). For quality control the targets associated with these GO terms were compared to the Denylist of the respective GO term on QuickGO (where the Denylist is called Blacklist) (Binns et al., 2009). QuickGO was further used to link GO terms via “is a” relationship to higher-order terms using the ancestor chart.

A comprehensive list of suramin targets was required as the starting point for target profiling. We aimed to assemble all the proteins that had been published as putative targets of suramin in the scientific literature by performing compound searches in the chemical databases ChEMBL and PubChem. The reported proteins were supplemented with those obtained from papers on suramin targets found in PubMed, and from the references therein. Finally, the solved co-crystal structures of suramin deposited in PDB (Wiedemar et al., 2020) were added. This resulted in an initial, maximally inclusive list of 127 candidate suramin target proteins from 36 different species encompassing mammals (n = 131 sequences) and other vertebrates (n = 6), fungi (n = 1), protozoa (n = 18), plants (n = 1), bacteria (n = 10), and viruses (n = 13) (Supplementary Table S1). Additional information that was collected alongside the targets included the type of assay that was used, the potency of suramin in that assay, and the nature of the evidence for interaction of suramin with its proposed target.

Special care was taken to use only proteins that physically interact with suramin. Thus the priority for curation of the suramin target list was to minimize the number of false positives; this meant accepting a few false negatives—i.e., proteins that had been wrongly excluded from the list—rather than including proteins that did not actually bind suramin. The following were used as inclusion criteria: inhibition of activity by at least 50% by a suramin concentration of no more than 50 μM, determined in an enzyme-based assay (as opposed to whole-cell assay), except for cell-based assays with viral proteins. Regarding protein complexes of multiple subunits, only the subunit interacting with suramin was included. A subunit was considered to be interacting with suramin if either only one subunit had been included in the assay, or if binding to a specific subunit had been validated experimentally. Otherwise, or if no such information was provided, the whole complex was excluded. Cases where suramin inhibited protein-protein interaction (rather than protein function) were excluded as well. The resulting list of targets consisted of 50 proteins, experimentally validated to be inhibited by suramin (column E of Supplementary Table S1; Figure 1).

To avoid a possible bias from overrepresentation of certain proteins among the suramin targets, e.g., due to the presence of closely related orthologues from different species, redundancy reduction of the sequence set was carried out as follows. All pairwise global alignments of the 50 amino acid sequences were performed and the distance d between each sequence pair was calculated. Based on the frequency distribution of d, a cut-off of 0.6 was chosen (Figure 2). Sequence pairs with a distance below that cut-off were regarded as highly similar, and of a group of highly similar sequences only the longest sequence was kept. After this final step of curation, 44 diverse proteins from 14 different species remained (Table 1; column E of Supplementary Table S1). This renders suramin the most promiscuous drug, surpassing other polypharmacological agents with respect to the number of reported targets (Haupt et al., 2013).

Suramin is much more active against the glycolytic enzymes of T. brucei than against their mammalian orthologues (Willson et al., 1993). At the same time, the glycolytic enzymes of T. brucei have clearly higher isoelectric points (pI between 9 and 11) than their mammalian counterparts (Misset & Opperdoes, 1987). This observation has raised the hypothesis that the negatively charged suramin preferably binds the trypanosomal enzymes because it interacts with clusters of positively charged amino acids that are absent from the mammalian enzymes (Willson et al., 1993). We therefore tested whether the suramin target set has an overrepresentation of positive charges in general. However, the mean isoelectric point of the 44 suramin targets (Table 1) was only slightly higher (pI 7.67) than that of the predicted human proteome (pI 7.40). This difference was not statistically significant (p = .27, Welch two sample t-test).

To identify all the functional motifs in the suramin target sequences, the set of 44 proteins (Table 1) was run against the complete Pfam collection of protein domain families (El-Gebali et al., 2019) with the program hmmscan of the HMMer3 suite (Eddy, 2009). The Pfam database contained 18,000 position-dependent scoring matrices for hidden Markov model-based profile searches (El-Gebali et al., 2019). Using an expectancy (E-value) cut-off of 0.01, this search returned on average eight hits per protein. The total number of different Pfam domains that was detected in the suramin targets was 142. Only 16 of these were associated with more than one protein, and none was associated with more than three, underscoring the heterogeneity of the presumed suramin targets.

Given the diversity not only of the suramin targets but also of the associated Pfam domains, we had to move up yet another level of abstraction to identify potential common denominators. This was done by linking the identified Pfam domains to GO (gene ontology) terms (Ashburner et al., 2000) based on the annotations provided by GODomainMiner (Alborzi et al., 2018). Thus GO terms for molecular function were assigned to the suramin targets via their Pfam domains. The resulting annotations were examined against the Denylist provided by QuickGO (Binns et al., 2009) and annotations that were likely to be incorrect were removed. After this purification step, there remained thirteen GO terms that matched five or more targets (Table 2). Two common themes emerged from this analysis: binding to nucleotides or nucleic acids, and binding to divalent cations such as Mg²⁺, Ca²⁺, or Zn²⁺. This was confirmed by a QuickGO ancestor chart (Binns et al., 2009) to determine the higher-order GO terms, which identified “cation binding” and “nucleotide binding” as the two most frequent entries (Table 3).