The effect of desolvation on the binding of inhibitors to HIV-1 protease and cyclin-dependent kinases: Causes of resistance

Strict Interactions of Fifth Letters, Hydrophobic Unnatural Bases, in XenoAptamers with Target Proteins

XenoAptamers are DNA fragments containing additional letters (unnatural bases, UBs) that bind specifically to their target proteins with high affinities (sub-nanomolar KD values). One of the UBs is the highly hydrophobic 7-(2-thienyl)imidazo[4,5-b]pyridine (Ds), which significantly increases XenoAptamers' affinities to targets. Originally, Ds was developed as a third base pair with a complementary UB, 2-nitro-4-propynylpyrrole (Px), for replication, and thus it can be used for aptamer generation by an evolutional engineering method involving PCR amplification. However, it is unclear whether the Ds base is the best component as the hydrophobic fifth-letter ligand for interactions with target proteins. To optimize the ligand structure of the fifth letter, we prepared 13 Ds variants and examined the affinities of XenoAptamers containing these variants to target proteins. The results obtained using four XenoAptamers prepared by the replacement of Ds bases with variants indicated that subtle changes in the chemical structure of Ds significantly affect the XenoAptamer affinities. Among the variants, placing either 4-(2-thienyl)pyrrolo[2,3-b]pyridine (Ys) or 4-(2-thienyl)benzimidazole (Bs) at specific Ds positions in each original XenoAptamer greatly improved their affinities to targets. The Ys and Bs bases are variants derived by replacing only one nitrogen with a carbon in the Ds base. These results demonstrate the strict intramolecular interactions, which are not simple hydrophobic contacts between UBs and targets, thus providing a method to mature XenoAptamers' affinities to targets.

QM/MM and MM MD Simulations on Enzymatic Degradation of the Nerve Agent VR by Phosphotriesterase

V-type nerve agents are hardly degraded by phosphotriesterase (PTE). Interestingly, the PTE variant of BHR-73MNW can effectively improve the hydrolytic efficiency of VR, especially for its Sp-enantiomer. Here, the whole enzymatic degradation of both Sp and Rp enantiomers of VR by the wild-type PTE and its variant BHR-73MNW was investigated by quantum mechanics/molecular mechanics (QM/MM) calculations and MM molecular dynamics simulations. Present results indicate that the degradation of VR can be initiated by the nucleophilic attack of the bridging OH- and the zinc-bound water molecule. The QM/MM-predicted energy barriers for the hydrolytic process of Sp-VR are 19.8 kcal mol-1 by the variant with water as a nucleophile and 22.0 kcal mol-1 by the wild-type PTE with OH- as a nucleophile, and corresponding degraded products are bound to the dinuclear metal site in monodentate and bidentate coordination modes, respectively. The variant effectively increases the volume of the large pocket, allowing more water molecules to enter the active pocket and resulting in the improvement of the degradation efficiency of Sp-VR. The hydrolysis of Rp-VR is triggered only by the hydroxide with an energy span of 20.6 kcal mol-1 for the wild-type PTE and 20.7 kcal mol-1 for the variant BHR-73-MNW PTE. Such mechanistic insights into the stereoselective degradation of VR by PTE and the role of water may inspire further studies to improve the catalytic efficiency of PTE toward the detoxification of nerve agents.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Catalytic Properties of Caseinolytic Protease Subunit of Plasmodium knowlesi and Its Inhibition by a Member of δ-Lactone, Hyptolide

The caseinolytic protease (Clp) system plays an essential role in the protein homeostasis of the malaria parasite, particularly at the stage of apicoplast development. The inhibition of this protein is known to have a lethal effect on the parasite and is therefore considered an interesting avenue for antimalaria drugs discovery. The catalytic activity of the Clp system is modulated by its proteolytic subunit (ClpP), which belongs to the serine protease family member and is therefore extensively studied for further inhibitors development. Among many inhibitors, the group of β-lactone is known to be a specific inhibitor for ClpP. Nevertheless, other groups of lactones have never been studied. This study aims to characterize the catalytic properties of ClpP of Plasmodium knowlesi (Pk-ClpP) and the inhibition properties of a δ-lactone hyptolide against this protein. Accordingly, a codon-optimized synthetic gene encoding Pk-ClpP was expressed in Escherichia coli BL21(DE3) and purified under a single step of Ni²⁺-affinity chromatography, yielding a 2.20 mg from 1 L culture. Meanwhile, size-exclusion chromatography indicated that Pk-ClpP migrated primarily as homoheptameric with a size of 205 kDa. The specific activity of pure Pk-ClpP was 0.73 U µg^-1, with a catalytic efficiency k_cat/K_M of 0.05 µM^-1 s^-1, with optimum temperature and pH of 50 °C and 7.0-7.5, respectively. Interestingly, hyptolide, a member of δ-lactone, was shown to inhibit Pk-ClpP with an IC₅₀ value of 17.36 ± 1.44 nM. Structural homology modelling, secondary structure prediction, and far-UV CD spectra revealed that helical structures dominate this protein. In addition, the structural homology modeling showed that this protein forms a barrel-shaped homoheptamer. Docking simulation revealed that the inhibition was found to be a competitive inhibition in which hyptolide was able to dock into the catalytic site and block the substrate. The competitiveness of hyptolide is due to the higher binding affinity of this molecule than the substrate.

STACKED - Solvation Theory of Aromatic Complexes as Key for Estimating Drug Binding

The use of fragments to biophysically characterize a protein binding pocket and determine the strengths of certain interactions is a computationally and experimentally commonly applied approach. Almost all drug like molecules contain at least one aromatic moiety forming stacking interactions in the binding pocket. In computational drug design, the strength of stacking and the resulting optimization of the aromatic core or moiety is usually calculated using high level quantum mechanical approaches. However, as these calculations are performed in a vacuum, solvation properties are neglected. We close this gap by using Grid Inhomogeneous Solvation Theory (GIST) to describe the properties of individual heteroaromatics and complexes and thereby estimate the desolvation penalty. In our study, we investigated the solvation free energies of heteroaromatics frequently occurring in drug design projects in complex with truncated side chains of phenylalanine, tyrosine, and tryptophan. Furthermore, we investigated the properties of drug-fragments crystallized in a fragment-based lead optimization approach investigating PDE-10-A. We do not only find good correlation for the estimated desolvation penalty and the experimental binding free energy, but our calculations also allow us to predict prominent interaction sites. We highlight the importance of including the desolvation penalty of the respective heteroaromatics in stacked complexes to explain the gain or loss in affinity of potential lead compounds.

Machine learning for the prediction of molecular dipole moments obtained by density functional theory

Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened. Random forest regression models predicted an external test set of 3368 compounds achieving mean absolute error up to 0.44 D. The results represent a significant improvement of the dipole moments calculated using empirical point charges located at the nucleus, even assuming the DFT-optimized geometry (root mean square error, RMSE, of 0.68 D vs. 1.53 D and R² = 0.87 vs. 0.66).