Activity Prediction and Structural Insights of Extracellular Signal-Regulated Kinase 2 Inhibitors with Molecular Dynamics Simulations

Computer aided designing of novel pyrrolopyridine derivatives as JAK1 inhibitors

Janus kinases (JAKs) are a family of non-receptor kinases that play a key role in cytokine signaling and their aberrant activities are associated with the pathogenesis of various immune diseases. The JAK1 isoform plays an essential role in the types 1 and II interferon signaling and elicits signals from the interleukin-2, interleukin-4, gp130, and class 2 receptor families. It is ubiquitously expressed in humans and its overexpression has been linked with autoimmune diseases such as myeloproliferative neoplasm. Although JAK1 inhibitors such as Tofacitinib have been approved for medical use, the low potency and off-target effects of these inhibitors have limited their use and calls for the development of novel JAK1 inhibitors. In this study, we used computational methods on a series of pyrrolopyridine derivatives to design new JAK1 inhibitors. Molecular docking and molecular dynamics simulation methods were used to study the protein-inhibitor interactions. 3D-quantitative structure–activity relationship models were developed and were used to predict the activity of newly designed compounds. Free energy calculation methods were used to study the binding affinity of the inhibitors with JAK1. Of the designed compounds, seventeen of the compounds showed a higher binding energy value than the most active compound in the dataset and at least six of the compounds showed higher binding energy value than the pan JAK inhibitor Tofacitinib. The findings made in this study could be utilized for the further development of JAK1 inhibitors.

Polarizable Empirical Force Field for Halogen-Containing Compounds Based on the Classical Drude Oscillator

The quality of the force field is crucial to ensure the accuracy of simulations used in molecular modeling, including computer-aided drug design (CADD). To perform more accurate modeling and simulations of halogenated molecules, in this study the polarizable force field based on the classical Drude oscillator model was extended to both aliphatic and aromatic systems using halogenated ethane and benzene model compounds for the halogens F, Cl, Br, and I. The force field parameters were optimized targeting quantum mechanical dipole moments, water interactions, and molecular polarizabilities as well as experimental observables, including enthalpies of vaporization, molecular volumes, hydration free energies, and dielectric constants. The developed halogenated polarizable force field is capable of reproducing QM relative energies and geometries of both halogen bonds and halogen-hydrogen bond donor interactions at an unprecedented level due to the inclusion of a virtual particle and anisotropic atomic polarizability on the halogen and, notably, the inclusion of Lennard-Jones parameters on the halogen Drude particle. The model was validated on the basis of its ability to accurately reproduce pure solvent properties for halogenated naphthalenes and alkanes, including species analogous to those used as refrigerants. Accordingly, it is anticipated that the model will be applicable for the study of halogenated derivatives in CADD as well as in other chemical and biophysical studies.

Molecular mechanism of carbon nanotube to activate Subtilisin Carlsberg in polar and non-polar organic media

In the work, we mainly used molecular dynamics (MD) simulation and protein structure network (PSN) to study subtilisin Carlsberg (SC) immobilized onto carbon nanotube (CNT) in water, acetonitrile and heptane solvents, in order to explore activation mechanism of enzymes in non-aqueous media. The result indicates that the affinity of SC with CNT follows the decreasing order of water > acetonitrile > heptane. The overall structure of SC and the catalytic triad display strong robustness to the change of environments, responsible for the activity retaining. However, the distances between two β-strands of substrate-binding pocket are significantly expanded by the immobilization in the increasing order of water < acetonitrile < heptane, contributing to the highest substrate-binding energy in heptane media. PSN analysis further reveals that the immobilization enhances structural communication paths to the substrate-binding pocket, leading to its larger change than the free-enzymes. Interestingly, the increase in the number of the pathways upon immobilization is not dependent on the absorbed extent but the desorbed one, indicating significant role of shifting process of experimental operations in influencing the functional region. In addition, some conserved and important hot-residues in the paths are identified, providing molecular information for functional modification.

Probing immobilization mechanism of alpha-chymotrypsin onto carbon nanotube in organic media by molecular dynamics simulation

The enzyme immobilization has been adopted to enhance the activity and stability of enzymes in non-aqueous enzymatic catalysis. However, the activation and stabilization mechanism has been poorly understood on experiments. Thus, we used molecular dynamics simulation to study the adsorption of α-chymotrypsin (α-ChT) on carbon nanotube (CNT) in aqueous solution and heptane media. The results indicate that α-ChT has stronger affinity with CNT in aqueous solution than in heptane media, as confirmed by more adsorption atoms, larger contact area and higher binding free energies. Although the immobilization causes significant structure deviations from the crystal one, no significant changes in secondary structure of the enzyme upon adsorption are observed in the two media. Different from aqueous solution, the stabilization effects on some local regions far from the surface of CNT were observed in heptane media, in particular for S1 pocket, which should contribute to the preservation of specificity reported by experiments. Also, CNT displays to some extent stabilization role in retaining the catalytic H-bond network of the active site in heptane media, which should be associated with the enhanced activity of enzymes. The observations from the work can provide valuable information for improving the catalytic properties of enzymes in non-aqueous media.

Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context

Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.

Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development

Protein kinase C Related Kinase 1 (PRK1) has been shown to be involved in the regulation of androgen receptor signaling and has been identified as a novel potential drug target for prostate cancer therapy. Since there is no PRK1 crystal structure available to date, multiple PRK1 homology models were generated in order to address the protein flexibility. An in-house library of compounds tested on PRK1 was docked into the ATP binding site of the generated models. In most cases a correct pose of the inhibitors could be identified by ensemble docking, while there is still a challenge of finding a reasonable scoring function that is able to rank compounds according to their biological activity. We estimated the binding free energy for our data set of structurally diverse PRK1 inhibitors using the MM-PB(GB)SA and QM/MM-GBSA methods. The obtained results demonstrate that a correlation between calculated binding free energies and experimental IC50 values was found to be usually higher than using docking scores. Furthermore, the developed approach was tested on a set of diverse PRK1 inhibitors taken from literature, which resulted in a significant correlation. The developed method is computationally inexpensive and can be applied as a postdocking filter in virtual screening as well as for optimization of PRK1 inhibitors in order to prioritize compounds for further biological characterization.

Assessing protein kinase selectivity with molecular dynamics and mm-pbsa binding free energy calculations

An application of molecular dynamics and molecular mechanics Poisson-Boltzmann surface area techniques to the prediction of protein kinase inhibitor selectivity is presented. A highly active and selective ERK2 inhibitor was placed in equivalent orientations in five different protein kinases (SRC, LCK, GSK3, JNK3 and Aurora-A). Binding free energies were then computed with the molecular mechanics Poisson-Boltzmann surface area approach using 15 nanosecond fully solvated molecular dynamics trajectories of the corresponding protein-ligand complexes. The results show correlation with experimentally determined selectivities and provide useful insights into the underlying structural determinants for selectivity.