Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.

3D-QSAR Studies, Molecular Docking, Molecular Dynamic Simulation, and ADMET Proprieties of Novel Pteridinone Derivatives as PLK1 Inhibitors for the Treatment of Prostate Cancer

Overexpression of polo-like kinase 1 (PLK1) has been found in many different types of cancers. With its essential role in cell proliferation, PLK1 has been determined to be a broad-spectrum anti-cancer target. In this study, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations were applied on a series of novel pteridinone derivatives as PLK1 inhibitors to discover anti-cancer drug candidates. In this work, three models—CoMFA (Q² = 0.67, R² = 0.992), CoMSIA/SHE (Q² = 0.69, R² = 0.974), and CoMSIA/SEAH (Q² = 0.66, R² = 0.975)—of pteridinone derivatives were established. The three models that were established gave Rpred2 = 0.683, Rpred 2= 0.758, and Rpred 2= 0.767, respectively. Thus, the predictive abilities of the three proposed models were successfully evaluated. The relations between the different champs and activities were well-demonstrated by the contour chart of the CoMFA and CoMSIA/SEAH models. The results of molecular docking indicated that residues R136, R57, Y133, L69, L82, and Y139 were the active sites of the PLK1 protein (PDB code: 2RKU), in which the more active ligands can inhibit the enzyme of PLK1. The results of the molecular dynamic MD simulation diagram were obtained to reinforce the previous molecular docking results, which showed that both inhibitors remained stable in the active sites of the PLK1 protein (PDB code: 2RKU) for 50 ns. Finally, a check of the ADME-Tox properties of the two most active molecules showed that molecular N° 28 could represent a good drug candidate for the therapy of prostate cancer diseases.

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3

Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.

Structural Insights from Molecular Modeling of Isoindolin-1-One Derivatives as PI3Kγ Inhibitors against Gastric Carcinoma

The upregulation of phosphoinositol-3-kinase γ (PI3Kγ) is deemed to be positively correlated with tumor-associated-macrophage (TAM)-mediated gastric carcinoma (GC). PI3Kγ suppresses tumor necrosis factor-alpha (TNF-α) and interleukin-12 (IL-12) through activation of the AKT/mTOR pathway, which promotes the immunosuppressant phenotype of TAM. Unlike α and β isoforms, δ and γ isoforms are primarily distributed in leucocytes and macrophages. Dual inhibitors against PI3Kδ and PI3Kγ have been proven to have merits in targeting solid tumors. Furthermore, it has been found that PI3Kδ is activated by cytokines, while PI3Kγ is activated by G-protein-coupled receptors (GPCRs). This facilitates determining the functional difference between these two isoforms. For this goal, selective inhibitors would be immensely helpful. In the current manuscript, we conducted various molecular modeling studies with a series of isoindolin-1-one derivatives as potent PI3Kγ inhibitors by combining molecular docking, molecular dynamics (MD), molecular mechanics, Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA) binding free energy calculation, and three-dimensional structure–activity relationship (3D-QSAR) study. To evaluate the selectivity of γ isoform over δ, the molecular modeling studies of idelalisib analogs reported as PI3Kδ inhibitors were also investigated. The contour polyhedrons were generated from the comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) around the ligand-bound active site for both isoforms, which could emphasize plausible explanations for the physicochemical factors that affect selective ligand recognition. The binding modalities of the two isoforms using CoMFA and MD models were compared, which suggested some key differences in the molecular interactions with the ligands and could be summarized as three subsites (one affinity subsite near the C-helix and DFG and two hydrophobic subsites). In the context of the structure–activity relationship (SAR), several new compounds were designed using a fragment-substitution strategy with the aim of selectively targeting PI3Kγ. The pIC50 values of the designed compounds were predicted by the 3D-QSAR models, followed by the MM-PB/GBSA binding energy estimation. The overall findings suggest that the designed compounds have the potential to be used as PI3Kγ inhibitors with a higher binding affinity and selectivity.

Molecular Modeling Studies of N-phenylpyrimidine-4-amine Derivatives for Inhibiting FMS-like Tyrosine Kinase-3

Overexpression and frequent mutations in FMS-like tyrosine kinase-3 (FLT3) are considered risk factors for severe acute myeloid leukemia (AML). Hyperactive FLT3 induces premature activation of multiple intracellular signaling pathways, resulting in cell proliferation and anti-apoptosis. We conducted the computational modeling studies of 40 pyrimidine-4,6-diamine-based compounds by integrating docking, molecular dynamics, and three-dimensional structure-activity relationship (3D-QSAR). Molecular docking showed that K644, C694, F691, E692, N701, D829, and F830 are critical residues for the binding of ligands at the hydrophobic active site. Molecular dynamics (MD), together with Molecular Mechanics Poison-Boltzmann/Generalized Born Surface Area, i.e., MM-PB(GB)SA, and linear interaction energy (LIE) estimation, provided critical information on the stability and binding affinity of the selected docked compounds. The MD study suggested that the mutation in the gatekeeper residue F691 exhibited a lower binding affinity to the ligand. Although, the mutation in D835 in the activation loop did not exhibit any significant change in the binding energy to the most active compound. We developed the ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models. CoMFA (q² = 0.802, r² = 0.983, and QF32 = 0.698) and CoMSIA (q² = 0.725, r² = 0.965 and QF32 = 0.668) established the structure-activity relationship (SAR) and showed a reasonable external predictive power. The contour maps from the CoMFA and CoMSIA models could explain valuable information about the favorable and unfavorable positions for chemical group substitution, which can increase or decrease the inhibitory activity of the compounds. In addition, we designed 30 novel compounds, and their predicted pIC₅₀ values were assessed with the CoMSIA model, followed by the assessment of their physicochemical properties, bioavailability, and free energy calculation. The overall outcome could provide valuable information for designing and synthesizing more potent FLT3 inhibitors.