Abstract:Optimization is reported for biaryltriazoles as inhibitors of the tautomerase activity of human macrophage migration inhibitory factor (MIF), a proinflammatory cytokine associated with numerous inflammatory diseases and cancer. A combined approach was taken featuring organic synthesis, enzymatic assaying, crystallography, and modeling including free-energy perturbation (FEP) calculations. X-ray crystal structures for 3a and 3b bound to MIF are reported and provided a basis for the modeling efforts. The accommo… Show more
“…Furthermore, a very potent inhibitor for the macrophage migration inhibitory factor (MIF or MMIF) was developed with the help of FEP calculations. 23 …”
A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein–ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points—something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein–protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the “real” conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.
“…Furthermore, a very potent inhibitor for the macrophage migration inhibitory factor (MIF or MMIF) was developed with the help of FEP calculations. 23 …”
A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein–ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points—something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein–protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the “real” conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.
“…These residues establish interactions with the inhibitor in the crystal structure of the Hu‐MIF/inhibitor complex (PDB entry: 4WR8). In that complex, the polar residues Lys32 and Ser63 bind biaryltriazoles by hydrogen bonds, while Pro33, Tyr36, Trp108, and Phe113 keep the inhibitor bound by stacking interactions . Our structural alignments showed that all of these relevant residues (ie, Lys32, Pro33, Tyr36, Ser63, Trp108, and Phe113) were 100% conserved in Ts‐MIF with a very similar spatial orientation compared with Hu‐MIF (Figure 2B).…”
The human macrophage migration inhibitory factor 1 (Hu-MIF-1) is a protein involved in the inflammatory and immunology response to parasite infection. In the present study, the existence of Hu-MIF-1 from parasites have been explored by mining WormBase. A total of 35 helminths were found to have Hu-MIF-1 homologs, including some parasites of importance for public health. Physicochemical, structural, and biological properties of Hu-MIF-1 were compared with its orthologs in parasites showing that most of these are secretory proteins, with positive net charge and presence of the Cys-Xaa-Xaa-Cys motif that is critical for its oxidoreductase activity. The inhibitor-binding site present in Hu-MIF-1 is well conserved among parasite MIFs suggesting that Hu-MIF inhibitors may target orthologs in pathogens. The binding of Hu-MIF-1 to its cognate receptor CD74 was predicted by computer-assisted docking, and it resulted to be very similar to the predicted complexes formed by parasite MIFs and human CD74. More than 1 plausible conformation of MIFs in the extracellular loops of CD74 may be possible as demonstrated by the different predicted conformations of MIF orthologs in complex with CD74. Parasite MIFs in complex with CD74 resulted with some charged residues oriented to CD74, which was not observed in the Hu-MIF-1/CD74 complex. Our findings predict the binding mode of Hu-MIF-1 and orthologs with CD74, which can assist in the design of novel MIF inhibitors. Whether the parasite MIFs function specifically subvert host immune responses to suit the parasite is an open question that needs to be further investigated. Future research should lead to a better understanding of parasite MIF action in the parasite biology.
“…26 First, a single point screening was done at a concentration of 25 μM and 50 μM and the compounds showing more than 50% inhibition of enzyme activity at 25 μM were tested for IC 50 values (Figs. S2 and S3).…”
Section: Resultsmentioning
confidence: 99%
“…The assay was done following the procedure of Dziedzic et al 26 4-hydrox-yphenyl pyruvate (4-HPP) was used as substrate to quantify tautomerase activity. Stock solutions of 10 mM 4-HPP were made in 50 mM ammonium acetate buffer pH 6.0, and incubated overnight at room temperature to allow equilibration between keto and enol form.…”
Section: Methodsmentioning
confidence: 99%
“…24–28 Using a structure-based virtual screening method, Orita-13 containing a chromen-4-one scaffold was identified as MIF inhibitor. 26,29 Additionally, covalent MIF inhibitors have been described, such as TP, as probes suitable for activity-based protein profiling. 30 Taken together, several small molecule binders of MIF have been developed (Fig.…”
Macrophage migration inhibitory factor (MIF) is an essential signaling cytokine with a key role in the immune system. Binding of MIF to its molecular targets such as, among others, the cluster of differentiation 74 (CD74) receptor plays a key role in inflammatory diseases and cancer. Therefore, the identification of MIF binding compounds gained importance in drug discovery. In this study, we aimed to discover novel MIF binding compounds by screening of a focused compound collection for inhibition of its tautomerase enzyme activity. Inspired by the known chromen-4-one inhibitor Orita-13, a focused collection of compounds with a chromene scaffold was screened for MIF binding. The library was synthesized using versatile cyanoacetamide chemistry to provide diversely substituted chromenes. The screening provided inhibitors with IC's in the low micromolar range. Kinetic evaluation suggested that the inhibitors were reversible and did not bind in the binding pocket of the substrate. Thus, we discovered novel inhibitors of the MIF tautomerase activity, which may ultimately support the development of novel therapeutic agents against diseases in which MIF is involved.
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