Study of NAD-interacting proteins highlights the extent of NAD regulatory roles in the cell and its potential as a therapeutic target

2.1 Data collection

2.2 Gene ontology (GO) analysis of the protein datasets

GO analysis was performed on PANTHER (http://pantherdb.org/) [16], through an overrepresentation test (Fisher’s exact, False Discovery Rate correction), using the Pathways annotation dataset (version 13.0). The NAD-binding, the NAD-related and the NAD-PPIs datasets were analysed.

2.3 Identification of putative NAD-binding proteins

The most frequent protein domains and protein families within the NADBPs dataset were identified [11]. The total of 20,303 human reviewed proteins from the Uniprot database were considered as a reference dataset, and the 50,588 unreviewed proteins as a test dataset. Proteins that presented at least one of the most frequent NADBPs domains were retrieved from both reference and test datasets. The genes/proteins that were found exclusively within the test dataset of unreviewed proteins were identified and further analysed using the NADbinder (http://crdd.osdd.net/raghava/nadbinder/) [17] to predict the number of NAD interacting residues, and the STRING database (https://string-db.org/) v. 11 [15], to obtain the interactions of each of those proteins.

2.4 Molecular docking

To evaluate the potential binding of NAD to the top target, an automated in silico molecular docking analysis was performed using SwissDock web server (http://www.swissdock.ch), as described by Grosdidier and collaborators [18]. NAD ligand was used as provided by ZINC database (https://zinc.docking.org/), with the ID ZINC8214766, and the protein 3D structures of the top target were retrieved from AlphaFold database (https://alphafold.ebi.ac.uk/) [19].

2.5 Cancer associated proteins and potential drug targets

Proteins from the NADBPs dataset were compared with catalogues of protein-coding genes from the subproteomes of the Human Protein Atlas (https://www.proteinatlas.org/) [20]. Namely, the cancer proteome, that contains a list of 569 mutated proteins strongly implicated in cancer, as defined through the catalogue of somatic mutations in cancer (COSMIC), and the druggable proteome, that contains a list of 754 proteins targeted directly by an FDA approved drug, were considered. Currently, approximately four thousand protein-coding genes in the UniProt database have experimental evidence of involvement in several disease conditions, including cancer, neurologic, systemic and cardiovascular disease. From those, a list of 1326 proteins annotated in The Human Protein Atlas as potential drug targets, was also considered, as they belong to known drug target protein classes, such as enzymes, transporters, receptors and ion-channels, and are not yet targets for FDA approved or experimental drugs in the Drugbank database.

3.1 NAD-binding and NAD-related proteins differ in their predominant cellular roles

After collecting data from six different databases, we obtained a NADBPs dataset composed by a total of 439 proteins (Figure 1 and Appendix A). The NAD metabolite was found under different forms and names, and both oxidized and reduced forms were included. The highest numbers of interactions with NAD were found on the databases STITCH, DrugBank and the Human Metabolome Database.

Figure 1:

Venn-diagram showing the number of NAD-binding proteins obtained from each source.

The analysis of the 439 NADBPs showed that around 80% of these proteins were enzymes, most with catalytic activity, involved in metabolite interconversion. The major protein classes were dehydrogenases (92 proteins), from which over 30 were NADH dehydrogenase, and oxidoreductases (55 proteins), but several others were identified, as shown in Figure 2. More than one hundred proteins were mitochondrial isoforms of enzymes, which participate in the chain of reactions responsible for ATP production. Adding to enzymes that use NAD as cofactor in redox reactions, we also found all PARPs and all SIRTs, which are enzymes that use NAD as a substrate. Regarding their molecular function, a small number of proteins involved in regulation or transporter activities was also found.

Figure 2:

Classification of the NAD-binding proteins according to the protein class from PANTHER (www.pantherdb.org). The graphics represent the classes with more than 10 proteins. The remaining 43 classes comprising a total of 105 proteins are under the label “other” and 71 proteins remained non-classified.

The NAD-related datasets included all proteins potentially related to NAD, either by protein names or by any field of description. For the “NAD-related” dataset, we obtained 456 proteins from UniProt and 1907 from IMEX. In a total of 2125 proteins, only 238 were common to both sources. We then identified 279 proteins that were also present in the NADBP dataset, leaving a total of 1846 NAD-related proteins that do not bind NAD directly.

We performed a GO analysis on the 439 proteins of the NADBPs dataset (Table 1) and on the 1846 proteins of the NAD-related dataset, to compare the results of the enriched pathways obtained in each one. Only two pathways were common to the two datasets, Glycolysis and the FAS signalling pathway. We found 31 pathways specific of the NADBPs dataset, that were not enriched on the NAD-related dataset. Those included pathways related to biosynthesis or metabolism of nucleic acids, carbohydrates, and amino acids.

In the NAD-related dataset, we found 36 pathways that did not appeared in the NADBPs dataset, that were mostly related to signalling. The highest fold enrichment values were found in the pentose phosphate pathway (the highest fold = 10.1), the JAK/STAT signalling, and four pathways related to p53 signalling. Of note, disease related pathways arose in the NAD related dataset, such as Alzheimer, Huntington, and Parkinson diseases. Also, signalling pathways related to angiogenesis, inflammation, and apoptosis, which are disease related mechanisms, were identified within the results.

3.2 Proteins that interact with NADBPs comprise about half of the human proteome

Then, the NAD-protein–protein interactions (NAD-PPIs), i.e., the proteins that interacted with the NADBPs were studied. Using the 439 proteins from the NADBPs dataset, 9823 pairs of proteins from STRING database were obtained, that corresponded to a total of 7815 unique gene name identifiers, 19,682 pairs from BIOGRID database, that corresponded to a total of 6479 unique gene IDs, and 5594 pairs from IMEX, that corresponded to a total of 3301 unique IDs. After mapping each type of ID retrieved from each database to the UniProtKB ID, with reviewed annotation (either using automatic tools or manually, in the case of automatically unmapped IDs), the duplicated entries we removed that were mainly due to gene or protein alternative names, or disease names associated to those genes. From STRING, a total of 7533 proteins were successfully mapped and 75 elements remained unmapped. From BIOGRID, a total of 5752 proteins were mapped and 54 remained unmapped. Most of these unmapped IDs were pseudogenes. From IMEX, 2500 proteins were mapped, and 90 elements remained unmapped. We found 40 CHEBI IDs, that were retrieved from CHEBI database for identification, but were not included for further analysis, since they corresponded to chemical compounds that interact with NADBPs and not protein-protein interactions, as it was intended. The proteins common to the three sources of PPIs were identified, and a final list of 10,020 proteins involved in PPIs with NADBPs remained.

As this represents about half of the human proteins annotated so far, according to the most recent version of UniProt Knowledge Database (UniProtKB 2020_06, [21]), with 20,379 reviewed proteins on the human proteome, the 1368 proteins common to all databases (Figure 3) were further analysed. With this, the selection of the most validated interactions was assured.

Figure 3:

Venn-diagram representing the protein-protein interactions from STRING, BIOGRID and IMEX databases, where the 439 NADBPs were used as query.

A GO analysis was performed on the 1368 proteins from the NAD-PPIs dataset and compared with the results from the NADBPs dataset described previously (Table 1). Similarly to the NAD-related dataset, the NAD-PPIs dataset presented an enrichment in several signalling pathways, as compared to the NADBPs dataset. The pathways with the highest number of genes (over 50) were related to hormone receptors signalling, namely for gonadotropin and for the gastrointestinal peptide hormones cholecystokinin and gastrin, followed by the Wnt signalling and angiogenesis pathways. Several other pathways were related to hormone or growth factor signalling, and disease pathways also emerged, namely three major neurodegenerative diseases, Alzheimer’s, Huntington’s, and Parkinson’s.

3.3 Overview of NADBPs protein structural domains

Protein domains analysis was performed on the 439 NADBPs through PFAM database and all matches that achieved an expectation value (E-value) below 1 (max. 0.88) were selected. The results show the top hit domain for each protein and how many hits were found. Within the 439 proteins, 1101 identifications were made, which corresponded to a total of 412 different domains. Two proteins didn’t have an identified domain (NDUFA11 and GPAT2) and, in the remaining 437 proteins, 222 different domains were identified as top hit. More than half of the proteins (56% – 247 proteins) belonged to the FAD/NAD(P)-binding Rossmann fold superfamily, and 27% belonged to the Ankyrin repeat superfamily. In our approach, the top 15 more common domains, which appeared in more than 10 proteins (Table 2), were selected. Five different ankyrin repeats were among these top domains found. Others were the short chain dehydrogenase, the aldehyde dehydrogenase family, the cytochrome P450 and the poly(ADP-ribose) polymerase (PARP) catalytic domain.

We also identified 65 proteins that had one of the 43 domains containing the term “NAD” in their names or descriptions. Eighteen proteins contain specifically one of the six different “NAD binding domain”, namely the D-isomer specific 2-hydroxyacid dehydrogenase, the 3-hydroxyacyl-CoA dehydrogenase, the lactate/malate dehydrogenase, the malic enzyme, the UDP-glucose/GDP-mannose dehydrogenase family, and the 6-phosphogluconate dehydrogenase NAD binding domains. The NUDIX domain was found only in two proteins from the NAD-binding dataset, namely NUDT12 and NUDT7.

3.4 Identification of 13 new NAD-binding proteins based on protein domains

We searched for the 15 domains that were identified in ten or more proteins from the NADBPs dataset (Table 2) within the dataset of the full human proteome unreviewed proteins (test dataset) and obtained 901 protein sequences. After removing all protein fragments and duplicates, 255 proteins were identified, which corresponded to 204 single genes. A similar approach was performed in the reference dataset yielding 474 genes. Given our aim to identify uncharacterized proteins, from the 204 genes, 195 that were also identified in the reference dataset were excluded and 8 genes remained, corresponding to 13 protein sequences, found uniquely in the test dataset (Table 3).

Among the 13 proteins, there were five isoforms of the Diacylglycerol (DAG) kinase, four encoded by the DGKI gene, and one encoded by DGKZ gene. There were two other kinase isoforms, from the Leucine-rich repeat serine/threonine-protein kinase 1, encoded by the LRRK1 gene. There were also two proteins related to membrane transport, the Sodium/hydrogen exchanger 9B2 (SLC9B2) and two isoforms of a short transient receptor potential channel encoded by the TRPC3 gene. A smaller isoform of the POTEB member of the ankyrin family was also found. Of note, POTEB was the only protein that presented simultaneously two of the 15 domains (Ank_2 e Ank_5). Additionally, there were two proteins resultant from the readthrough of two genes, CYP3A7-CYP3A51P, which belong to a subfamily of the Cytochrome P450, and FPGT-TNNI3K, from the neighbouring fucose-1-phosphate guanylyltransferase (FPGT) and TNNI3 interacting kinase (TNNI3K) genes.

To evaluate the possibility that NAD has an impact on the interactions between these proteins, we further searched for the interactions of each of the proteins. DGKI and SLC9B2 had no reported interactions, as well as the proteins resultant from the two readthrough events. LRRK1 had the highest number of interactions, followed by TRPC3.

3.5 Number of NAD interacting residues in new and known NADBPs

The 13 identified proteins were further analysed using the NADbinder software (Table 3). Here, instead of the protein structure, the protein sequence is considered. The highest number of NAD-interacting residues was 33 and was identified in the longest isoform of TRPC3, with 793 amino acids, followed by the longest isoform of DGKI with 1078 amino acids, where 31 residues were identified. The five DAG kinase isoforms retrieved more than 20 NAD-interacting residues, as well as the two readthrough proteins. A positive correlation was observed between the amino acid length and the number of NAD-interacting residues identified.

To serve as a reference, six proteins known to be involved in NAD metabolism were additionally scanned for the number of NAD interacting residues. Namely, two enzymes that consume NAD intracellularly (PARP1 and SIRT1), two enzymes that consume NAD extracellularly (CD38 and CD73), and two enzymes that participate in NAD biosynthesis (NAMPT and NAPRT) were analysed. Among these sequences, there was no significant correlation between the number of NAD-binding residues and protein length. According to the NADbinder analysis results, CD73 had the highest number of NAD interacting residues (51), and the remaining NAD-consuming enzymes had between 37 and 43 residues, which were higher than the ones identified in the 13 previously studied proteins. NAMPT and NAPRT don’t interact directly with the NAD molecule, and presented 39 and 18 NAD interacting residues, respectively. However, they bind nicotinamide and nicotinic acid, similar molecules that are NAD precursors, and are responsible for the first steps of their conversion into NAD.

3.6 NUDIX containing proteins in NADBPs dataset

As it was previously described that NUDIX domain directly interacts with NAD, the proteins within NADBPs dataset that are NUDIX hydrolases, NUDT7 and NUDT12, were also studied. In NUDT7, 21 NAD interacting residues were identified and, in NUDT12, only six residues were detected. We also searched for their interactors, considering only experimentally validated physical interactions, and found three proteins that interact with NUDT7 and three proteins that interact with NUDT12 (Figure 4).

Figure 4:

Protein–protein interactions of NUDIX proteins from the NAD-binding proteins dataset. (a) NUDT7 and (b) NUDT12. Queried proteins are represented by red nodes and the line thickness indicates the confidence level of the interaction. Only physical interactions are represented. The network was obtained through STRING (string-db.org).

3.7 Identification of NADBPS as potential new drug targets mutated in cancer

We found 122 proteins that are NAD-binding and potential drug targets. Two of them also belong to the set of cancer mutated genes, fumarate hydratase (FH) and 5′-nucleotidase, cytosolic II (NT5C2). Additionally, three other NADBPs, which mutations are implicated in cancer, were found in the catalogue of FDA approved targets, namely, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC), androgen receptor (AR) and isocitrate dehydrogenase 2 (IDH2).