Development of a Crystallographic Screening to Identify Sudan Virus VP40 Ligands

Abstract

The matrix protein VP40 of the highly pathogenic Sudan virus (genus Orthoebolavirus) is a multifunctional protein responsible for the recruitment of viral nucleocapsids to the plasma membrane and the budding of infectious virions. In addition to its role in assembly, VP40 also downregulates viral genome replication and transcription. VP40's existence in various homo-oligomeric states is presumed to underpin its diverse functional capabilities during the viral life cycle. Given the absence of licensed therapeutics targeting the Sudan virus, our study focused on inhibiting VP40 dimers, the structural precursors to critical higher-order oligomers, as a novel antiviral strategy. We have established a crystallographic screening pipeline for the identification of small-molecule fragments capable of binding to VP40. Dimeric VP40 of the Sudan virus was recombinantly expressed in bacteria, purified, crystallized, and soaked in a solution of 96 different preselected fragments. Salicylic acid was identified as a crystallographic hit with clear electron density in the pocket between the N- and the C-termini of the VP40 dimer. The binding interaction is predominantly coordinated by amino acid residues leucine 158 (L158) and arginine 214 (R214), which are key in stabilizing salicylic acid within the binding pocket. While salicylic acid displayed minimal impact on the functional aspects of VP40, we delved deeper into characterizing the druggability of the identified binding pocket. We analyzed the influence of residues L158 and R214 on the formation of virus-like particles and viral RNA synthesis. Site-directed mutagenesis of these residues to alanine markedly affected both VP40's budding activity and its effect on viral RNA synthesis, underscoring the potential of the salicylic acid binding pocket as a drug target. In summary, our findings lay the foundation for structure-guided drug design to provide lead compounds against Sudan virus VP40.

Figure 1

Salicylic acid of the Jena FragXtal Screen was identified as a sVP40-binder. (A) Composition of the Jena FragXtal Screen. (B) sVP40 WT (PDB-ID 8B2U) in surface representation with the NTD (dark red) and the CTD (blue). The binding site of SA (white box, formula, boxed rectangle), linker, and C-terminal arm (black box) are shown in panels C and D, respectively. (C) Close-up of the SA binding pocket (SA shown in cyan) between NTD and CTD with H-bonds to Leu158, Arg214 (not all atoms of the side chains were modeled), and a water molecule. (D) A close-up of the linker (wheat) and C-terminal arm (blue) shown in the stick representation, with the remaining part of the structure shown as both cartoon and surface (transparency 60%). (E) Thermal stability of sVP40 in complex with SA. Means of denaturing temperatures upon incubation with increasing concentrations of SA (1–100 mM) were compared with apo sVP40_Δ43 WT (three independent experiments).

Figure 2

SA displays only minor effects on VP40 in functional assays. (A) Treatment with SA influences reporter gene activity in a minigenome assay. HEK293 cells were transfected with pCAGGS sVP40 WT and the minigenome assay components. The cell culture medium was changed 4 h p.t., and increasing concentrations of SA were added (1 μM–1 mM). Cells were lysed 48 h p.t., and reporter gene activity was measured and normalized to the sample without VP40 (set to 100%). For the negative control, no polymerase L was added to the transfection mix. (B, C) The effect of SA treatment on VP40 release. Cells were transfected with pCAGGS GP and pCAGGS sVP40 WT, and the cell culture medium was changed 4 h p.t., and SA added. Supernatants were collected, and cells were lysed 24 h p.t., and Western blot analyses of cellular and released VP40 were performed using rabbit α-VP40 and mouse α-tubulin as primary antibodies and donkey α-mouse and goat α-rabbit IRDye 680 as secondary antibodies. (C) Quantification of VLPs. Released VP40 was normalized to the amount of VP40 in lysates, and sVP40 WT w/o SA was set to 100%. (D) HuH7 cells were infected with SUDV and treated with varying concentrations of SA. Viral titers of the supernatants were assessed at 2 dpi via TCID50. Bars indicate the mean ± SD of at least three independent experiments, and asterisks indicate statistical significance (*P < 0.05 and ****P < 0.0001) compared to the positive control without SA.

Figure 3

Influence of sVP40 pocket mutants on reporter gene activity, budding, and homo-oligomerization. (A) Influence on an MG assay: HEK293 cells were transfected with pCAGGS sVP40 WT or mutants along with the minigenome assay components. Cells were lysed 48 h p.t., and reporter gene activity was measured and normalized to the sample without VP40 (set to 100%). For the negative control, no polymerase L was added to the transfection mix. (B, C) The effect of sVP40 pocket mutants on VP40 release: cells were transfected with pCAGGS GP and sVP40 WT or mutants. Supernatants were collected, and cells lysed 24 h p.t. and Western blot analyses of cellular and released VP40 were performed using rabbit α-sVP40 and mouse α-tubulin as primary antibodies and donkey α-mouse and goat α-rabbit IRDye 680 as secondary antibodies. Quantification of VLPs: released sVP40 was normalized to the amount of sVP40 in lysates, and sVP40 WT was set to 100%. VP40 typically exhibits two major bands, both of which were used for quantification. (D) Influence of different arginine substitutions on reporter gene activity. (E) The effect of arginine substitutions on VP40 release. (F) Size-exclusion chromatography of sVP40 WT and pocket mutants. (G) Area under the curve (AUC) of the different VP40 oligomers: size-exclusion chromatography and calculation of the AUC of the dimeric and octameric peaks and determination of the octameric percentage of total VP40 AUC. Bars indicate the mean ± SD of three independent experiments, and asterisks indicate statistical significance as follows: *P < 0.05, **P < 0.005, ***P < 0.0005, and ****P < 0.00005 compared to sVP40 WT.

Figure 4

Workflow for the analysis of crystallographic data. (A) Promising data sets were refined using Phenix and Coot until the final model was generated. (B) Interesting data sets were identified by using both the refinement pipeline by Schiebel et al. as well as dimple and PanDDA.