Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors

Interaction of vernodalin with p38 mitogen-activated protein kinase (p38 MAPK) and subsequent effects in lung cancer cell model

The p38 mitogen-activated protein kinase (p38 MAPK) is a key regulator of numerous cellular processes relevant to cancer therapy. Apart from reactive oxygen species (ROS)-mediated activation, the p38 pathway can also be activated through direct interactions between the p38 MAPK protein which stabilize an open conformation of the activation loop, exposing the phosphorylation sites. Therefore, assessing p38 MAPK expression and the direct binding of anticancer agents with p38 MAPK can provide critical insights for advancing the development of innovative anticancer therapies. In this research, following the incubation of A549 lung cancer cells with vernodalin (VN), a sesquiterpene lactone, several cellular assays were conducted. It was noted that VN suppressed the proliferation of A549 cells with IC50 values of 65.80 μM, 39,90 μM, and 25.85 μM at 24 h, 48 h, and 72 h, respectively, while, IC50 values were higher for VN in BEAS-2B human bronchial epithelial cells. Moreover, we discovered that VN leads to excessive ROS and MDA production by downregulating the activities of SOD/CAT/GPX, reducing GSH levels, and decreasing both HO-1 mRNA expression and activity, along with Nrft mRNA expression. We subsequently found that VN boosted the expression of p38 MAPK, Bax/Bcl-2 mRNA, and caspase-3/-9 mRNA and activity in A549 cells. Molecular docking analysis revealed strong binding affinity between VN and p38 MAPK (-9.97 kcal/mol) near the ATP-binding site (Asp-150 and Arg-149) and the activation loop (Gly-170 and Leu-171) facilitated by hydrophobic as well as hydrogen bond interactions. The fluorescence spectroscopy analysis demonstrated that the spontaneous interaction between p38 MAPK and one molecule VN occurred with logKb = 5.02 ± 0.18 and ΔG∘ = -28.49 ± 1.91 kJ/mol. Multi-spectroscopy measurements revealed minor alterations in the p38 MAPK conformation following interaction with VN. We hypothesize that the pro-apoptotic effect of VN on lung cancer cells may be mediated by two mechanisms: (1) ROS-dependent modulation of p38 pathway, and/or (2) direct ligand-receptor interaction between VN and critical residues near the ATP-binding pocket or activation loop of p38 MAPK, thereby stabilizing its catalytically active conformation. However, these preliminary findings necessitate further experimental validation and confirmation in subsequent studies.

Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time

The dissociation rate constant (koff) significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for de novo drug design guided by koff. First, a comprehensive database containing 2,773 unique koff values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of koff with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low koff. Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/koff) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based de novo drug design framework in designing drug candidates with prolonged τ.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k_off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k_off value. We observed a high correlation among the simulated, predicted, and experimental -log(k_off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k_off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k_off values and are not in our ML training dataset. The predicted k_off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k_off of other proteins and will enhance the kinetics-based drug design endeavor.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Fabrication of food-grade Pickering high internal phase emulsions (HIPEs) stabilized by a dihydromyricetin and lysozyme mixture

This study evaluated the feasibility of fabricating food-grade HIPEs using a dihydromyricetin and lysozyme mixture. The effects of the oil phase volume fraction (φ), composition (lysozyme:dihydromyricetin, k), and addition amount (w) of the mixture on the formation and properties of the HIPEs were analyzed. Then, the interactions of dihydromyricetin and lysozyme were investigated. The results indicated that when w was 0.4%, HIPEs with φ value of 90% could be obtained. Furthermore, the k also affected the microstructure, mechanical properties, oil oxidation, and lutein protection ability of the HIPEs. However, the presence of dihydromyricetin did not affect lysozyme activity. Both isothermal titration calorimetry and molecular simulations proved that they did not form a typical host-guest complex. But, dihydromyricetin could absorb on the lysozyme surface. Therefore, we speculated that lysozyme and dihydromyricetin particles could overlap and form a 3D network structure to stabilize the HIPEs, which was consistent with the microstructure observations.

Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (k_off) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of k_off values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Druggable Transient Pockets in Protein Kinases

Drug discovery using small molecule inhibitors is reaching a stalemate due to low selectivity, adverse off-target effects and inevitable failures in clinical trials. Conventional chemical screening methods may miss potent small molecules because of their use of simple but outdated kits composed of recombinant enzyme proteins. Non-canonical inhibitors targeting a hidden pocket in a protein have received considerable research attention. Kii and colleagues identified an inhibitor targeting a transient pocket in the kinase DYRK1A during its folding process and termed it FINDY. FINDY exhibits a unique inhibitory profile; that is, FINDY does not inhibit the fully folded form of DYRK1A, indicating that the FINDY-binding pocket is hidden in the folded form. This intriguing pocket opens during the folding process and then closes upon completion of folding. In this review, we discuss previously established kinase inhibitors and their inhibitory mechanisms in comparison with FINDY. We also compare the inhibitory mechanisms with the growing concept of “cryptic inhibitor-binding sites.” These sites are buried on the inhibitor-unbound surface but become apparent when the inhibitor is bound. In addition, an alternative method based on cell-free protein synthesis of protein kinases may allow the discovery of small molecules that occupy these mysterious binding sites. Transitional folding intermediates would become alternative targets in drug discovery, enabling the efficient development of potent kinase inhibitors.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Recent progress in molecular simulation methods for drug binding kinetics

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Drug Discovery Targeting Anaplastic Lymphoma Kinase (ALK)

As a receptor tyrosine kinase of insulin receptor (IR) subfamily, anaplastic lymphoma kinase (ALK) has been validated to play important roles in various cancers, especially anaplastic large cell lymphoma (ALCL), nonsmall cell lung cancer (NSCLC), and neuroblastomas. Currently, five small-molecule inhibitors of ALK, including Crizotinib, Ceritinib, Alectinib, Brigatinib, and Lorlatinib, have been approved by the U.S. Food and Drug Administration (FDA) against ALK-positive NSCLCs. Novel type-I¹/₂ and type-II ALK inhibitors with improved kinase selectivity and enhanced capability to combat drug resistance have also been reported. Moreover, the "proteolysis targeting chimera" (PROTAC) technique has been successfully applied in developing ALK degraders, which opened a new avenue for targeted ALK therapies. This review provides an overview of the physiological and biological functions of ALK, the discovery and development of drugs targeting ALK by focusing on their chemotypes, activity, selectivity, and resistance as well as potential therapeutic strategies to overcome drug resistance.