Modelling data for: Short-course combination treatment for experimental chronic Chagas disease
Data files
Dec 14, 2023 version files 22.04 MB
Abstract
Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, affects millions of people in the Americas and across the world leading to considerable morbidity and mortality. Current treatment options, benznidazole (BNZ) and nifurtimox, offer limited efficacy and often lead to adverse side effects due to long treatment durations. Better treatment options are therefore urgently required. Here we describe a pyrrolopyrimidine series, identified through phenotypic screening, that offers a clear opportunity to improve on current treatments. In vitro cell-based washout assays demonstrate that compounds in the series are incapable of killing all parasites, however, combining these pyrrolopyrimidines with a sub-efficacious dose of BNZ can clear all parasites in vitro after five days. Importantly, these findings were replicated in a clinically predictive in vivo model of chronic Chagas disease, where five days of treatment with the combination was sufficient to prevent parasite relapse. Comprehensive mechanism of action studies, supported by ligand-structure modelling, show that compounds from this pyrrolopyrimidine series inhibit the Qi active site of T. cruzi cytochrome b, part of the cytochrome bc1 complex of the electron transport chain. Knowledge of the molecular target enabled a cascade of assays to be assembled to evaluate selectivity over the human cytochrome b homologue. As a result, a highly selective and efficacious lead compound was identified. The combination of our lead compound with BNZ rapidly clears T. cruzi parasites, both in vitro and in vivo, and shows great potential to overcome key issues associated with currently available treatments.
README
Modelling data for Short-course combination treatment for experimental chronic Chagas disease.
https://doi.org/10.5061/dryad.95x69p8r7
The dataset contains PDB files and molecular dynamics files for the modelling section of our manuscript.
Description of the data and file structure
Datafiles are provided for Figure 5, and Supplemental Information Figures S10, S11, S12, S13, S14, S15, S16 and S17.
Figure 5: Docking poses for compounds 1 – 4 into a homology model of T. cruzi cytochrome b.
Files:
Fig5A.pdb: PDB file for docking pose of compound 1 (5-(2-methoxyphenyl)-4,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidin-2-amine)
Fig5B.pdb: PDB file for docking pose of compound 3 (5-(4-Chloro-2-methoxyphenyl)-4,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidin-2-amine)
Fig5C.pdb: PDB file for docking pose of compound 2 (5-(4-Fluoro-2-methoxyphenyl)-4,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidin-2-amine).
Fig5D.pdb: PDB file for docking pose of compound 4 (5-(2,4-Dimethoxyphenyl)-4,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidin-2-amine).
FigS10: Comparison of binding mode of antimycin A and compound 3.
Files:
FigS10A.pdb: Crystal structure of antimycin A in the Qi site of the bc1 complex (PDB ID: 1PPJ)
FigS10B.pdb: Predicted binding mode of compound 3 (pink) at the T. cruzi cytochrome b pocket (light grey) superimposed with the experimentally determined binding mode of antimycin A (blue; PDB ID: 1PPJ)
FigS11: Molecular dynamics simulation of compound 3 in complex with T. cruzi cytochrome b.
FigS11A PL-Contacts_HBond.dat: Datafile for hydrogen bonding interactions reported in 2D ligand-protein interaction diagram (FigS11A).
Columns:
# Frame#: MD frame number
Residue#: Protein Residue Number
Chain: Protein Chain
ResName: Protein Residue Name
AtomName: Atom Name of the protein residue
LigandFragment: Ligand Fragment part
LigandAtomName: Atom Name of the ligand
FigS11A PL-Contacts_Pi-Pi.dat: Datafile for π-π stacking interactions reported in 2D ligand-protein interaction diagram (FigS11A).
Columns:
# Frame#: MD frame number
Residue#: Protein Residue Number
Chain: Protein Chain
ResName: Protein Residue Name
LigandFragment: Ligand Fragment part
Distance: Interaction distance (Å)
Type: Type of π-π stacking interactions, Edge-to-face (e2f), Face-to-face (f2f)
S11-B.CSV: Ligand root mean square fluctuation (RMSF) values for each atom (as indicated in FigS11C in the manuscript).
FigS12: 100 ns MD simulation of compound 3 in complex with T. cruzi cytochrome b.
Files:
FigS12A.csv: Root mean square deviation (RMSD) values of the protein (backbone atoms) and ligand (heavy atoms) during 100ns MD simulation.
Columns:
Timestep: Timepoint in picoseconds.
RMSD-Lig: Root mean square deviation for ligand heavy atoms
RMSD-Protein_Backbone: Root mean square deviation for protein backbone atoms
FigS12B.pdb: PDB for binding mode of compound 3 observed during the MD simulation at 0 ns
FigS12C.pdb: PDB for binding mode of compound 3 observed during the MD simulation at 25 ns
FigS12D.pdb: PDB for binding mode of compound 3 observed during the MD simulation at 50 ns
FigS12E.pdb: PDB for binding mode of compound 3 observed during the MD simulation at 75 ns
FigS12F.pdb: PDB for binding mode of compound 3 observed during the MD simulation at 100 ns
FigS13: Summary of 100 ns MD simulation of compound 3 in complex with T. cruzi cytochrome b. Files report the observed interactions and contacts for each frame of the molecular dynamics stimulation.
Files:
FigS13 PL-Contacts_HBond.dat: data for hydrogen bonds
FigS13 PL-Contacts_Hydrophobic.dat : data for hydrophobic interactions
FigS13 PL-Contacts_Pi-Cation : data for π-cation interactions
FigS13 PL-Contacts_Pi-Pi : data for π-π interactions
FigS13 PL-Contacts_WaterBridge: data for waterbridges
Columns (combined for all files):
# Frame#: MD frame number
Residue#: Protein Residue Number
Chain: Protein Chain
ResName: Protein Residue Name
AtomName: Atom Name of the protein residue
LigandFragment: Ligand Fragment part
LigandAtomName: Atom Name of the ligand
Distance: Interaction distance (Å)
Type: Type of π-π stacking interactions, Edge-to-face (e2f), Face-to-face (f2f)
Fig S14: Analysis of resistance mutations in Qi site of T. cruzi cytochrome b.
Files:
FigS14A.pdb: Homology model for T. cruzi cytochrome b.
FigS14B.pdb: Homology model for T. cruzi cytochrome b with L197I mutation.
FigS14C.pdb: Homology model for T. cruzi cytochrome b with F222L mutation.
FigS14D.pdb: PDB file with predicted binding mode of compound 3 (pink) at the T. cruzi cytochrome b pocket (light grey) superimposed with I197 (blue) and L222 (magenta) mutations.
Figs S15-S16: Predicted binding modes for compounds 1 - 4 in the T. cruzi cytochrome b pocket bearing L197I mutation.
FigS15-16-A.pdb: Docking pose of compound 1 in the T. cruzi cytochrome b pocket bearing L197I mutation.
FigS15-16-B.pdb: Docking pose of compound 3 in the T. cruzi cytochrome b pocket bearing L197I mutation.
FigS15-16-C.pdb: Docking pose of compound 2 in the T. cruzi cytochrome b pocket bearing L197I mutation.
FigS15-16-D.pdb: Docking pose of compound 4 in the T. cruzi cytochrome b pocket bearing L197I mutation.
FigS17: Predicted binding modes of compound 3 in the T. cruzi cytochrome b pocket bearing F222L mutation.
FigS17_lig-1.pdb: Docking pose 1 for compound 3.
FigS17_lig-2.pdb: Docking pose 2 for compound 3.
FigS17_lig-3.pdb: Docking pose 3 for compound 3.
FigS17_pro: Homology model for T. cruzi cytochrome b with F222L mutation.
Methods
Protein and ligand preparation for molecular modelling
Our molecular modelling studies were based on a previously generated homology model of T. cruzi cytochrome b. The protein structure with a conserved water molecule interacting with F33 was prepared using the Protein Preparation module in the Schrödinger suite (Schrödinger Release 2021-2). Protonation states were assigned by PROPKA at pH 7.0, and the hydrogen bonding network was consequently optimized. A restrained energy minimization step was then executed using the OPLS4 force field with default settings. Models of cytochrome b bearing either L197I or F222L mutations were prepared in Maestro by mutating one residue at a time. Subsequently, the mutated versions of cytochrome b were processed with the Protein Preparation tool in an identical manner to the wild-type enzyme.
Ligand structures (compounds 1-4) were prepared with Schrödinger’s LigPrep (Schrödinger Release 2021-2). All possible tautomeric forms and stereoisomers were generated at pH 7.0 ± 0.4 using Epik.
Docking studies
Molecular docking studies were carried out using Glide Standard Precision (SP) mode (Schrödinger Release 2021-2). First, the docking grid boxes were generated using the Receptor Grid Generation tool; bound ubiquinone (UQ2) was selected as the center of the grid, and a cube of 10 Å was defined as the inner box. During docking, a total of 20 poses per ligand were subjected to post-docking minimization, and a maximum of ten docking poses for each ligand were output. This setup was able to successfully reproduce the experimentally determined binding mode of antimycin A.
Molecular dynamic simulation
Molecular Dynamic (MD) simulation of the binding mode generated by molecular docking was carried out to analyze the stability of the pose and the interactions at the binding site. The docking pose of compound 3 in complex with T. cruzi cytochrome b was used as the initial coordinate for the generation of the MD system. Desmond software (Schrödinger Release 2021-2) was utilized to set up the system and run the MD simulation. As cytochrome b is embedded in the inner mitochondrial membrane, MD was performed in membrane; thus, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes were added to the model system by placing the two layers automatically. The system was then solvated using the SPC (simple point-charge) water model in a Periodic Boundary Conditions orthorhombic box and neutralized with Na+ ions at a salt concentration of 0.15 M. The option “exclude ions and salt placement” was turned on to allow a 10 Å buffer zone from the ligand. The default Desmond protocol for energy minimization and model relaxation was applied before performing the production simulation. The OPLS4 force field and NPAT (constant particle number (N), pressure (P), lateral surface area (A), and temperature (T)) ensemble were used. The temperature was kept constant at 300 K, while the pressure was kept at 1.01325 bar. Lastly, a 100 ns MD simulation with a trajectory interval of 5 ps was carried out with the same conditions. Simulation Interactions Diagram (SID) was used for the analysis of the MD simulation. The RMSF and RMSD values were then plotted using R packages.