https://vital.seals.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://vital.seals.ac.za/vital/access/manager/Repository/vital:3990 Wed 12 May 2021 22:59:27 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:17850 Wed 12 May 2021 19:25:06 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:4074 Wed 12 May 2021 18:22:11 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:4165 Wed 12 May 2021 15:45:50 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:27230 Thu 22 Jul 2021 14:38:29 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:35439 Thu 13 May 2021 14:04:58 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:3986 Thu 13 May 2021 08:03:28 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:27820 Thu 13 May 2021 04:39:10 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38089 3000 cases and controls) are required. Macrophage migration inhibitory factor (MIF) is a cytokine that is important in both innate and adaptive immunity that has been shown to play a role in T. brucei pathogenicity using murine models. A total of 27 missense SNVs were modelled using homology modelling to create MIF protein mutants that were investigated using in silico effect prediction tools, molecular dynamics (MD), Principal Component Analysis (PCA), and Dynamic Residue Network (DRN) analysis. Our results demonstrate that mutations P2Q, I5M, P16Q, L23F, T24S, T31I, Y37H, H41P, M48V, P44L, G52C, S54R, I65M, I68T, S75F, N106S, and T113S caused significant conformational changes. Further, DRN analysis showed that residues P2, T31, Y37, G52, I65, I68, S75, N106, and T113S are part of a similar local residue interaction network with functional significance. These results show how polymorphisms such as missense SNVs can affect protein conformation, dynamics, and function. Trypanosomes are auxotrophic for folates and pterins but require them for survival. They scavenge them from their hosts. PTR1 is a multifunctional enzyme that is unique to trypanosomatids that reduces both pterins and folates. In the presence of DHFR inhibitors, PTR1 is over-expressed thus providing an escape from the effects of DHFR inhibition. Both TbPTR1 and TbDHFR are pharmacologically and genetically validated drug targets. In this study 5742 compounds were screened using molecular docking, and 13 promising binding modes were further analysed using MD simulations. The trajectories were analysed using RMSD, Rg, RMSF, PCA, Essential Dynamics Analysis (EDA), Molecular Mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations, and DRN analysis. The computational screening approach allowed us to identify five of the compounds, named RUBi004, RUBi007, RUBi014, RUBi016 and RUBi018 that exhibited antitrypanosomal growth activities against trypanosomes in culture with IC50 values of 12.5 ± 4.8 μM, 32.4 ± 4.2 μM, 5.9 ± 1.4 μM, 28.2 ± 3.3 μM, and 9.7 ± 2.1 μM, respectively. Further when used in combination with WR99210 a known TbDHFR inhibitor RUBi004, RUBi007, RUBi014 and RUBi018 showed antagonism while RUBi016 showed an additive effect. These results indicate that the four compounds might be competing with TbDHFR while RUBi016 might be more specific for TbPTR1. These compounds provide scaffolds that can be further optimised to improve their potency and specificity. Lastly, using a systematic approach we derived CHARMM force-field parameters to accurately describe the TbrPDEB1 bi-metal catalytic center. For dynamics, we employed mixed bonded and non-bonded approach. We optimised the structure using a two-layer QM/MM ONIOM (B3LYP/6-31(g): UFF). The TbrPDEB1 bi-metallic center bonds, angles, and dihedrals were parameterized by fitting the energy profiles from Potential Energy Surface (PES) scans to the CHARMM potential energy function. The parameters were validated by means of MD simulations and analysed using RMSD, Rg, RMSF, hydrogen bonding, bond/angle/dihedral evaluations, EDA, PCA, and DRN analysis. The force-field parameters were able to accurately reproduce the geometry and dynamics of the TbrPDEB1 bi-metal catalytic center during MD simulations. Molecular docking was used to identify 6 potential hits, that inhibited trypanosome growth in vitro. The derived force-field parameters were used to simulate the 6 protein-ligand complexes with the aim of elucidating crucial protein-ligand residue interactions. Using the most potent ligand RUBi022 that had an IC50 of 14.96 μM we were able to identify key residue interactions that can be of use in in silico prediction of potential TbrPDEB1 inhibitors. Overall we demonstrate how bioinformatics tools can complement current disease eradication strategies. Future work will focus on identifying variants identified in Genome Wide Association Studies and partnering with wet labs to carry out further enzyme-ligand activity relationship studies, structure determination or characterisation of appropriate protein-ligand complexes by crystallography, and site specific mutation studies]]> Thu 13 May 2021 04:20:49 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:3890 Thu 13 May 2021 01:56:09 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:4004 Thu 13 May 2021 00:20:34 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:65782 Mon 24 Jul 2023 09:30:24 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:50129 Mon 10 Oct 2022 08:54:24 SAST ]]>