Assessment of target druggability guided by search and characterization of hot spots is a pivotal step in early stages of drug-discovery. The raw output of FTMap provides the data to perform this task, but it relies on manual intervention to properly combine different sets of consensus sites, therefore allowing identification of hot spots and evaluation of strength, shape and distance among them. Thus, the user’s previous experience on the target and the software has a direct impact on how data generated by FTMap server can be explored. DRUGpy plugin was developed to overcome this limitation. By automatically assembling and scoring all possible combinations of consensus sites, DRUGpy plugin provides FTMap users a straight-forward method to identify and characterize hot spots in protein targets. DRUGpy is available in all operating systems that support PyMOL software. DRUGpy promptly identifies and characterizes pockets that are predicted by FTMap to bind druglike molecules with high-affinity (druggable sites) or low-affinity (borderline sites) and reveals how protein conformational flexibility impacts on the target’s druggability. The use of DRUGpy on the analysis of trypanothione reductases (TR), a validated drug target against trypanosomatids, showcases the usefulness of the plugin, and led to the identification of a druggable pocket in the conserved dimer interface present in this class of proteins, opening new perspectives to the design of selective inhibitors.
The concept of druggable targets has evolved over the last decade. Initially, it was restricted to biomolecules that were characterize to possess binding sites for approved drugs. A more recent and broader perspective now includes all proteins with pockets that are suitable to bind druglike molecules.
DRUGpy workflow. The raw output of FTMap server (1) is loaded through DRUGpy menu (2). CSs ≥ 5 probes are employed to build combinations of hot spots and calculate their features (strength, max_distance and center-to-center distance.) The ensembles may be classified (3) as druggable (D), druggable small (Ds), borderline (B), borderline small (Bs). The DRUGpy output (4) is shown as objects in PyMOL (a) and the overlap among the hot spots is depicted in the fractional overlap matrix (b). The features of each combination can be inspected in the “properties” button (c). In case no druggable/borderline combination is found, the raw output of FTMap server is loaded in PyMOL.
Thus, the availability of several web servers to identify and characterize binding pockets, together with their high success rate in predicting protein pockets ability to bind drug-like molecules, explain their large use by the scientific community. FTMap, for instance, relies on physical principles that employ in silico solvent mapping to identify hot spots, and classify the binding sites according to different levels of druggability (druggable, D: if they are prone to bind druglike compounds with nanomolar affinity; borderline, B: if they are prone to bind druglike molecules with mili or micromolar affinity); druggable small, Ds: druggable only by peptides, macrocycle, or charged compound; borderline small, Bs: druggable (mili or micromolar affinity) by peptide, macrocycle, or charged compound; not druggable, N).
Clustering of TR (left panel) and GR (right panel) structures according to Bio3D tools [47]. Score (A and F) and loading (B and G) plots reveal that crystallographic structures are clustered into three families (green, red and black), as shown in detail by the dendrograms (D and I). The loading plot of PC1 (C and H) highlights the residues with large contribution to the conformational diversity. The locations of those regions are depicted on the 3D X-ray of TR (2JK6 – E) and GR (3DK9-J), presented in cartoon.
Even though druggability prediction based on FTMap has already been exploited with different purposes such as to prioritize pockets for drug discovery or to shed light on the putative binding sites of a specific compound such as 7-hydroxycoumarin, many authors just employed the strength of the main hot spot (number of probes in the consensus sites) as a criteria to assess pockets’ druggability. This naïve approach comes from the fact that the raw output of FTMap server only provides information about the consensus sites (CSs), with no account on how and if those CSs can be combined to assess targets’ druggability. To overcome this limitation, they have developed a novel PyMOL plugin (DRUGpy) that not only provides an accurate assessment of druggability in protein targets, but also allows the user to compare and contrast the impact of protein flexibility on hot spots druggability, or their drift in the protein surface due to conformational changes. The analysis of the enzyme trypanothione reductase (TR), a validated drug target for trypanosomatids, and its comparison with its human homologous enzyme glutathione reductase (GR), showcases how this tool might be employed from the drug-design standpoint of view and what type of analysis it provides.
Fraction of druggable (D and Ds) or borderline (B and Bs) hot spots identified according to number of CSs. The fraction of hot spots was calculated by (Number-hot-spots for nCSs)/(Max-number-hot-spots), where nCSs represents the number of CSs employed to build the ensembles and Max-number-hot-spots is the number of hot spots achieved for nCSs=5. Results for TR are depicted in gray and for GR in black. Druggable hot spots are shown as circles and borderline as squares.
Trypanothione reductase (TR) is a member of the flavin dinucleotide (FAD-dependent) NADPH oxidoreductase that catalyzes the reduction of trypanothione (TS2) to its reduced form (TSH). Through a ping pong mechanism, NADPH transfers hydrogen to FAD, which then promotes trypanothione reduction. TR is a homodimeric protein whose active sites are composed of two catalytic cysteine residues located in a large pocket 20 Å long and 15 Å wide, with residues from both the FAD binding and interface domains. Since parasites from the Trypanosomatidae family rely on TR to survive the oxidative stress response of the host during the infection process, it has long been considered a promising target for trypanocidal drug development.
Fraction overlap (FOA) and binding site similarity analysis (BSSA) of the main consensus site and the strongest druggable/borderline hot spots in representative TR and GR structures. FOA for the main consensus site (CS0) (A) and FOA for the main druggable/borderline hot spots (C). BSSA for the (CS0) (B) and the main druggable/Borderline hot spots (D) according to pocket match.
Indeed, there is genetic and chemical evidence to support drug development efforts targeting TR. Although the human host counterpart, glutathione reductase (GR), carries out the equivalent biochemical step, it does not recognize TS as a substrate and vice-versa. This fact, along with the availability of structural data on both TRs and GRs, supported the development of competitive TR inhibitors exploring either the trypanothione (TS) or the NADPH binding site. These inhibitors can be grouped into 7 chemical classes (tricyclics, diarylsulfide derivatives, polyamine derivatives, quinone derivatives, nitro substituted, bisbenzylisoquinoline alkaloids, and quaternary alkylammonium compounds), among which several potent and selective inhibitors have been described.
(A, D) Overall localization of the druggable hot spots in the ligand-free TR structure (2JK6) and ligand-free GR structure (3DK9) respectively, whose dimerization interface residues are highlighted in blue and pink. (B, C) The insets of each structure show that the druggable hot spots are located in the equivalent region, despite the low conservation of the residues in their vicinity. The residues numbering follows the sequence alignment provided in the supplementary material.
Despite the large number of identified ligands, the large hydrophobic active site of TR imposes a challenge to find druglike TR inhibitors. Thus, the search and identification of new sites or crevices for modulating activity apart from TR’s active site have been raised as a strategy for the development of new antitrypanosomatid drugs. The results provided by DRUGpy can certainly help to guide those efforts. In this work, a new druggable pocket was identified and characterized in the TR dimer interface, as a first step towards this goal.
Teixeira, O., Lacerda, P., Froes, T. et al. Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy. J Comput Aided Mol Des (2021). https://doi.org/10.1007/s10822-021-00403-8
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