Revisiting the Plasmodium falciparum druggable genome using predicted structures and data mining

Abstract

The identification of novel drug targets for the purpose of designing small molecule inhibitors is key component to modern drug discovery. In malaria parasites, discoveries of antimalarial targets have primarily occurred retroactively by investigating the mode of action of compounds found through phenotypic screens. Although this method has yielded many promising candidates, it is time- and resource-consuming and misses targets not captured by existing antimalarial compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction and data mining, we systematically assessed the Plasmodium falciparum genome for proteins amenable to target-based drug discovery, identifying 867 candidate targets with evidence of small molecule binding and blood stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 67 high priority candidates. This study also provides a genome-wide data resource and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

Keywords: Plasmodium blood stage targets; druggable genome; malaria data compendium.

Figure 1. Identification of P. falciparum protein-coding genes that are potentially druggable (n = 1,660) and genes that have evidence of blood stage essentiality (n = 2,992).

a) Identification of potentially druggable genes. Diagram illustrating the four methods used to identify genes with evidence of small molecule binding. Out of the 5,318 protein-coding 3D7 falciparum genes, 226 were found through a BLAST search using BindingDB 6,202 validated drug-targets, and 520 falciparum genes were found through orthology queries with OrthoMCL. A total of 1,233 genes had a confident AlphaFill hit transplant, and 927 falciparum genes were mapped through a validated EC number from BRENDA database. b) Binding evidence for validated targets vs. all genes. Distribution of binding evidence for 43 known targets (outer pie) compared to the distribution across all 5,275 P. falciparum 3D7 genes (inner pie). c) Workflow for the identification of 867 candidate targets and subsequent prioritization. Identification and filtering process to define the list of 867 P. falciparum candidate targets. The intersection of 1,660 genes with evidence of small molecule binding and 2,992 genes with essentiality support was used to determine the list of 867 candidates, after excluding hypervariable regions. Subsequent candidate prioritization resulted in 540 candidates that were subjected to initial scoring, 67 of which underwent a second round of scoring, and 27 top ranking targets discussed by a panel of experts that are likely to progress in the near future. d) Essentiality classifications for validated targets vs. all genes. Distribution of essentiality classifications for 43 known targets (outer pie) compared to the distribution of 5,275 3D7 genes (inner pie). Essentiality classifications are described in Methods.

Figure 2. Characteristics of 867 candidate targets compared to 4,451 non-candidate genes.

a) Characteristics of candidate targets vs. non-candidates. Comparison of characteristics between the 867 candidates (blue) and 4,451 non-candidates (purple). Numbers of candidate targets vs non-candidates labelled essential only in the P. falciparum essentiality screen and not the P. berghei datasets (850 vs. 2,421); labelled essential in both P. falciparum and berghei datasets (577 vs. 967); having at least one GO term (857 vs. 3,624); having human ortholog(s) (650 vs 1,356); having >2 associated literature references (570 vs 1,775); or having PDB structures (112 vs 174) are shown. b) Gene ontology (GO) term enrichment analysis for the 867 candidates. GO term enrichment analysis for the 867 candidate targets compared to all 5,318 protein coding genes in the P. falciparum 3D7 genome using GOATOOLS. Terms with ontology tree depth > 2 (n = 939) are displayed based on number of candidates having the GO term (X-axis) versus −log₁₀ Bonferroni corrected enrichment P value, with a maximum uncorrected P-value of 0.05 (Y-axis, Fisher’s exact test). Points corresponding to GO terms are colored by ontology type: red for biological process (BP), blue for cellular component (CC), and green for molecular function (MF). Highly enriched terms or groups of terms are labelled with shared descriptors. c) Scientific literature references for candidates vs. non-candidates. Distributions of number of unique scientific publications associated with the 867 candidate targets (blue) vs. 4,451 non-candidate genes (purple). Median lines are shown for both groups, and the most highly referenced genes are labelled. d) Gene expression in the asexual blood stage (ABS) compared to all protein-coding genes. Distribution of classifications for evidence of gene expression in the asexual blood stage (ring, trophozoite, or schizont) according to Le Roch et al. data. Candidate targets with clear evidence of ABS expression (737, teal), unclear evidence (57, purple), no expression (37, orange) and no data (36, grey) are shown in the outer pie, in contrast to the distribution among all P. falciparum protein-coding genes in the inner pie.

Figure 3. Rubric-based scoring of 540 candidate targets with strong evidence of essentiality.

a) Gene scores across rubric categories. Plot showing first score distributions for 540 candidate targets with strong evidence of blood stage essentiality across the ten categories of the scoring rubric (Methods). Average value per category is shown for each category. b) Frequency distribution of total first score. Histogram showing the frequency (X-axis) of first total score (Y-axis) for the 540 scored candidates. Bin center was determined and plotted with Prism v.9.5. Examples of candidates falling in select total score bins are shown, including gene product description and notable characteristics when applicable. c) Comparison of first and second scores for the top 67 scored candidates subject to secondary review. Identity line is marked by a dashed black line. Dark blue circles denote equal score in both rounds; light blue circles represent score differences of 1–19 points; and cyan circles represent a score difference of at least 20 points.

Figure 4. Selection of five high-scoring targets and examples of two low-scoring but promising candidates.

a) Scoring distribution across categories for top five candidate targets. Individual scores for the top five candidate targets across rubric categories, compared with the average scores across all 540 scored candidates. The average score is highlighted by light purple circles, and top five candidates are shown in blue (TopoI), green (BDP1), orange (GluPho), salmon (ATCase) and dark purple (GyrB). b-c) AlphaFill models for advanced candidate targets. Predicted AlphaFill models for PfATCase (b) and PfGluPho (c) are shown. Red rectangles highlight the region where transplant hits were found, with a zoomed-in inset of hit transplant structure having highest percentage of identity. d) TopoI model. TopoI (PF3D7_0510500) was constructed using UniProt ID Q8I3Z9 and ligand hits (Data S2). For simplicity, five ligands (BDBM-50249684, -50033788, -50259215, -50249691, and -50092821) associated to the UniProt ID were randomly selected from BindingDB hits. Ligands were docked onto the model using openbabel 3.1.1 and smina 2020.12.10. The model was visualized using PyMol version 2.5.5. e-f) AlphaFill models for understudied but promising candidate targets. Predicted AlphaFill models for PfPGM1 (e) and PfARF1 (f) candidate targets. Red rectangles highlight the region of some transplant hits, and a zoomed-in inset including hit transplant structure with highest percentage of identity is shown.