The druggable genome: Twenty years later

Abstract

The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target's druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.

Keywords: artificial intelligence; drug target identification; druggability; druggable genome; knowledge graph; tractability.

FIGURE 1

High-level schematic showing the overall structure-based assessment workflow.

FIGURE 2

(A) Clustered pockets across all AlphaFold 2 structures of the human kinome, with the AlphaFold 2 model of cyclin-dependent kinase 7 (UniProt ID P50613). The cyan points show the ATP binding site of each kinase, allowing us to rank the kinome purely based on ATP-pocket druggability. (B) The frequency distribution of ATP site scores is shown in yellow, with known druggable (blue) and non-druggable (green) targets for comparison.

FIGURE 3

(A) Tractability for PLpro (yellow) vs. known druggable (blue) and non-druggable (green) targets. (B–E) Plots to show conservation metrics across coronavirus species HKU1, 229E, OC43, NL63, MERS, SARS-CoV-2 and SARS-CoV. In each plot, the hotspot tractability score for a given residue is shown as a black cross. (B) Number of different amino acids. (C) Number of amino acid properties. (D) Frequency of the most common amino acid observed. (E) Frequency with which an amino acid is differentially conserved between species for each predicted hotspot residue. A position is considered differentially conserved when it is conserved within a given species (JS-divergence score >0.8) and different from a corresponding conserved position in a different species. As we consider multiple species, this plot shows the average number of times a given position is labelled as differentially conserved.