How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

Drug repurposing through Biophysical Insights: Focus on Indoleamine 2,3-Dioxygenase and Tryptophan 2,3-Dioxygenase Dual Inhibitors

The kynurenine pathway (KP) plays a pivotal role in dampening the immune response in many types of cancer, including TNBC. The intricate involvement of tryptophan degradation via KP serves as a critical regulator in mediating immunosuppression in the tumor microenvironment. The key enzymes that facilitate this mechanism and contribute to tumor progression are indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-dioxygenase (TDO). Despite attempts to use navoximod as a dual-specific inhibitor, its poor bioavailability and lack of clinical efficacy have hampered its utility. To date, no FDA-approved drugs have advanced for dual targeting of these enzymes. Therefore, this study aimed to repurpose the approved drugs from the DrugBank database as novel IDO1/TDO inhibitors. Initially, 2588 FDA-approved compounds were screened by employing molecular docking and pharmacokinetic profiling. Subsequently, methods such as MM-GBSA calculations and machine learning based analysis precisely identified 20 potential lead compounds. The resultant compounds were then assessed for various toxicity endpoints and anticancer activity. The PaccMann server revealed potent anticancer activity, with sensitivities ranging from 0.203 to 24.119 μM against MDA-MB-231 TNBC cell lines. Alongside, the interaction profile with critical residues, strongly reinforced DB06292 (Dapagliflozin) as a compelling hit candidate. Finally, the reliability of the result was corroborated through a rigorous 200 ns molecular dynamics simulation, ensuring the stable binding of the hit against the target proteins. Considering the promising outcomes, we speculate that the proposed hit compound holds strong potential for the management of TNBC.

Predicting Resistance to Small Molecule Kinase Inhibitors

Drug resistance is a critical challenge in treating diseases like cancer and infectious disease. This study presents a novel computational workflow for predicting on-target resistance mutations to small molecule inhibitors (SMIs). The approach integrates genetic models with alchemical free energy perturbation (FEP+) calculations to identify likely resistance mutations. Specifically, a genetic model, RECODE, leverages cancer-specific mutation patterns to prioritize probable amino acid changes. Physics-based calculations assess the impact of these mutations on protein stability, endogenous substrate binding, and inhibitor binding. We apply this approach retrospectively to gefitinib and osimertinib, two clinical epidermal growth factor receptor (EGFR) inhibitors used to treat nonsmall cell lung cancer (NSCLC). Among hundreds of possible mutations, the pipeline accurately predicted 4 out of 11 and 7 out of 19 known binding site mutations for gefitinib and osimertinib, respectively, including the clinically relevant T790M and C797S resistance mutations. This study demonstrates the potential of integrating genetic models and physics-based calculations to predict SMI resistance mutations. This approach can be applied to other kinases and target classes, potentially enabling the design of next-generation inhibitors with improved durability of response in patients.

Unraveling Extremely Damaging IRAK4 Variants and Their Potential Implications for IRAK4 Inhibitor Efficacy

Interleukin-1-receptor-associated kinase 4 (IRAK4) possesses a crucial function in the toll-like receptor (TLR) signaling pathway, and the dysfunction of this molecule could lead to various infectious and immune-related diseases in addition to cancers. IRAK4 genetic variants have been linked to various types of diseases. Therefore, we conducted a comprehensive analysis to recognize the missense variants with the most damaging impacts on IRAK4 with the employment of diverse bioinformatics tools to study single-nucleotide polymorphisms' effects on function, stability, secondary structures, and 3D structure. The residues' location on the protein domain and their conservation status were investigated as well. Moreover, docking tools along with structural biology were engaged in analyzing the SNPs' effects on one of the developed IRAK4 inhibitors. By analyzing IRAK4 gene SNPs, the analysis distinguished ten variants as the most detrimental missense variants. All variants were situated in highly conserved positions on an important protein domain. L318S and L318F mutations were linked to changes in IRAK4 secondary structures. Eight SNPs were revealed to have a decreasing effect on the stability of IRAK4 via both I-Mutant 2.0 and Mu-Pro tools, while Mu-Pro tool identified a decreasing effect for the G198E SNP. In addition, detrimental effects on the 3D structure of IRAK4 were also discovered for the selected variants. Molecular modeling studies highlighted the detrimental impact of these identified SNP mutant residues on the druggability of the IRAK4 ATP-binding site towards the known target inhibitor, HG-12-6, as compared to the native protein. The loss of important ligand residue-wise contacts, altered protein global flexibility, increased steric clashes, and even electronic penalties at the ligand-binding site interfaces were all suggested to be associated with SNP models for hampering the HG-12-6 affinity towards IRAK4 target protein. This given model lays the foundation for the better prediction of various disorders relevant to IRAK4 malfunction and sheds light on the impact of deleterious IRAK4 variants on IRAK4 inhibitor efficacy.

Genetic complementation screening and molecular docking give new insight on phosphorylation-dependent Mastl kinase activation

Mastl is a mitotic kinase that is essential for error-free chromosome segregation. It is an atypical member of AGC kinase family, possessing a unique non-conserved middle region. The mechanism of Mastl activation has been studied extensively in vitro. Phosphorylation of several residues were identified to be crucial for activation. These sites correspond to T193 and T206 in the activation loop and S861 in the C-terminal tail of mouse Mastl. To date, the significance of these phosphosites was not confirmed in intact mammalian cells. Here, we utilize a genetic complementation approach to determine the essentials of mammalian Mastl kinase activation. We used tamoxifen-inducible conditional knockout mouse embryonic fibroblasts to delete endogenous Mastl and screened various mutants for their ability to complement its loss. S861A mutant was able to complement endogenous Mastl loss. In parallel, we performed computational molecular docking studies to evaluate the significance of this residue for kinase activation. Our in-depth sequence and structure analysis revealed that Mastl pS861 does not belong to a conformational state, where the phosphoresidue contributes to C-tail docking. C-tail of Mastl is relatively short and it lacks a hydrophobic (HF) motif that would otherwise help its anchoring over N-lobe, required for the final steps of kinase activation. Our results show that phosphorylation of Mastl C-tail turn motif (S861) is dispensable for kinase function in cellulo.Communicated by Ramaswamy H. Sarma.

Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors

Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.

Enzymatic Generation of Double-Modified AdoMet Analogues and Their Application in Cascade Reactions with Different Methyltransferases

Methyltransferases (MTases) have become an important tool for site-specific alkylation and biomolecular labelling. In biocatalytic cascades with methionine adenosyltransferases (MATs), transfer of functional moieties has been realized starting from methionine analogues and ATP. However, the widespread use of S-adenosyl-l-methionine (AdoMet) and the abundance of MTases accepting sulfonium centre modifications limit selective modification in mixtures. AdoMet analogues with additional modifications at the nucleoside moiety bear potential for acceptance by specific MTases. Here, we explored the generation of double-modified AdoMets by an engineered Methanocaldococcus jannaschii MAT (PC-MjMAT), using 19 ATP analogues in combination with two methionine analogues. This substrate screening was extended to cascade reactions and to MTase competition assays. Our results show that MTase targeting selectivity can be improved by using bulky substituents at the N6 of adenine. The facile access to >10 new AdoMet analogues provides the groundwork for developing MAT-MTase cascades for orthogonal biomolecular labelling.