Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Modulation of aryl hydrocarbon receptor activity by tyrosine kinase inhibitors (ponatinib and tofacitinib)

Ponatinib and tofacitinib, established kinase inhibitors and FDA-approved for chronic myeloid leukemia and rheumatoid arthritis, are recently undergoing investigation in diverse clinical trials for potential repurposing. The aryl hydrocarbon receptor (AhR), a transcription factor influencing a spectrum of physiological and pathophysiological activities, stands as a therapeutic target for numerous diseases. This study employs molecular modelling tools and in vitro assays to identify ponatinib and tofacitinib as AhR ligands, elucidating their binding and molecular interactions in the AhR PAS-B domain. Molecular docking analyses revealed that ponatinib and tofacitinib occupy the central pocket within the primary cavity, similar to AhR agonists 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and (benzo[a]pyrene) B[a]P. Our simulations also showed that these compounds exhibit good stability, stabilizing many hot spots within the PAS-B domain, including the Dα-Eα loop, which serves as a regulatory element for the binding pocket. Binding energy calculations highlighted ponatinib's superior predicted affinity, revealing F295 as a crucial residue in maintaining strong interaction with the two compounds. Our in vitro data suggest that ponatinib functions as an AhR antagonist, blocking the downstream signaling of AhR pathway induced by TCDD and B[a]P. Additionally, both tofacitinib and ponatinib cause impairment in AhR-regulated CYP1A1 enzyme activity induced by potent AhR agonists. This study unveils ponatinib and tofacitinib as potential modulators of AhR, providing valuable insights into their therapeutic roles in AhR-associated diseases and enhancing our understanding of the intricate relationship between kinase inhibitors and AhR.

Chemical accuracy for ligand-receptor binding Gibbs energies through multi-level SQM/QM calculations

Calculating the Gibbs energies of binding of ligand-receptor systems with a thermochemical accuracy of ± 1 kcal mol-1 is a challenge to computational approaches. After exploration of the conformational space of the host, ligand and their resulting complexes upon coordination by semi-empirical GFN2 MD and meta-MD simulations, the systematic refinement through a multi-level improvement of binding modes in terms of electronic energies and solvation is able to give Gibbs energies of binding of drug molecules to CB[8] and β-CD macrocyclic receptors with such an accuracy. The accurate treatment of a small number of structures outperforms system-specific force-matching and alchemical transfer model approaches without an extensive sampling and integration.

Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design

The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.

Unveiling the synergy: a combined experimental and theoretical study of β-cyclodextrin with melatonin

Melatonin (MT) is a vital hormone controlling biorhythms, and optimizing its release in the human body is crucial. To address MT's unfavorable pharmacokinetics, we explored the inclusion complexes of MT with β-cyclodextrin (β-CD). Nano spray drying was applied to efficiently synthesize these complexes in three molar ratios (MT : β-CD = 1 : 1, 2 : 1, and 1 : 2), reducing reagent use and expediting inclusion. The complex powders were characterized through thermal analyses (TGA and DSC), Fourier transform infrared spectroscopy (FTIR), and in vitro MT release measurements via high-performance liquid chromatography (HPLC). In parallel, computational studies were conducted, examining the stability of MT : β-CD complexes by means of unbiased semi-empirical conformational searches refined by DFT, which produced a distribution of MT : β-CD binding enthalpies. Computational findings highlighted that these complexes are stabilized by specific hydrogen bonds and non-specific dispersive forces, with stronger binding in the 1 : 1 complex, which was corroborated by in vitro release data. Furthermore, the alignment between simulated and experimental FTIR spectra demonstrated the quality of both the structural model and computational methodology, which was crucial to enhance our comprehension of optimizing MT's release for therapeutic applications.

Modelling of AgrA inhibitors to combat anti-microbial resistance in Staphylococcus aureus

Staphylococcus aureus is a Gram-positive bacterium found on human skin that causes skin and soft tissue infections, as well as pneumonia, osteomyelitis, and endocarditis. The prevalence of antibiotic resistant strains has made the treatments less effective. An efficient alternate method for battling these contagious diseases is anti-virulence strategy. The AgrA protein, a key activator of Accessory Gene Regulator system in S. aureus, is vital to the virulence of the organism and, consequently, its pathogenesis. Using a Machine Learning algorithm, the Support Vector Machine (SVM), and a ligand-based pharmacophore modelling method, prediction models of AgrA inhibitors were developed. The metrics of the SVM model were inadequate, hence it was not used for virtual screening. For ligand-based pharmacophore modelling, 14 of 29 compounds were removed from the active set due to a lack of shared pharmacophore properties, and 504 compounds were designated as decoys. A 3D pharmacophore model was created using LigandScout 4.4.5, with a fit score of 57.48, including a positive ionizable group, one hydrogen bond donor, and three hydrogen bond acceptors. The model after further validation was used to virtually screen an external database which resulted in six hits. These compounds were docked with the AgrA domain crystal structure to determine the inhibitor activity. Further, each docked complex was subjected to a 100 ns molecular dynamics simulation. CID238 and CID20510252 demonstrated potent inhibitory binding interactions and hence can be used to develop AgrA inhibitors in future after proper validation.Communicated by Ramaswamy H. Sarma.

An insight into the cytotoxic, antimicrobial, antioxidant, and biocontrol perspective of novel Iron(III) complexes of substituted benzimidazoles: Inhibition kinetics and molecular simulations

Mononuclear complexes [FeCl3L2(OH2)] (L = L1, L2) were designed and synthesized by combining FeCl3 with 2-(3'-Aminophenylbenzimidazole) (L1) and 2-[(3'-N-Salicylidinephenyl)benzimidazole] (L2) and were characterized by physico-analytical strategies. The redox properties of the complexes were disclosed by the cyclic voltammetric method. Further, the interactions of complexes with proteins were studied by performing molecular docking engaging protein models of common cancer therapeutic targets to foresee their affinity to bind to these proteins. The complexes evidenced better protein-ligand docking (-8.4 and -9.0 kcal mol-1) and higher binding energies than their ligands. However, the L1 complex displayed improved binding free energy (-33.576 ± 1.01 kcal mol-1) compared to the other complexes and individual ligands. These compounds were screened for in vitro cytotoxic assays against triple-negative breast cancer cell lines (MDA-MB-468 cells), anti-inflammatory, antimicrobial, and antioxidant properties. The in vitro study complemented the in silico assay; therefore, these compounds may be a viable choice for expanding anticancer therapy. Additionally, the L2 showed better biocontrol activity owing to the enhanced growth of Trichoderma and inhibited the growth of Fusarium oxysporum.Communicated by Ramaswamy H. Sarma.

Host Dynamics under General-Purpose Force Fields

Macrocyclic hosts as prototypical receptors to gaseous and drug-like guests are crucial components in pharmaceutical research. The external guests are often coordinated at the center of these macromolecular containers. The formation of host-guest coordination is accompanied by the broken of host-water and host-ion interactions and sometimes also involves some conformational rearrangements of the host. A balanced description of various components of interacting terms is indispensable. However, up to now, the modeling community still lacks a general yet detailed understanding of commonly employed general-purpose force fields and the host dynamics produced by these popular selections. To fill this critical gap, in this paper, we profile the energetics and dynamics of four types of popular macrocycles, including cucurbiturils, pillararenes, cyclodextrins, and octa acids. The presented investigations of force field definitions, refitting, and evaluations are unprecedently detailed. Based on the valuable observations and insightful explanations, we finally summarize some general guidelines on force field parametrization and selection in host-guest modeling.

Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease

The outbreak of novel coronavirus disease 2019 (COVID-19), caused by the novel coronavirus (SARS-CoV-2), has had a significant impact on human health and the economic development. SARS-CoV-2 3CL protease (3CLpro) is highly conserved and plays a key role in mediating the transcription of virus replication. It is an ideal target for the design and screening of anti-coronavirus drugs. In this work, seven β-nitrostyrene derivatives were synthesized by Henry reaction and β-dehydration reaction, and their inhibitory effects on SARS-CoV-2 3CL protease were identified by enzyme activity inhibition assay in vitro. Among them, 4-nitro-β-nitrostyrene (compound a) showed the lowest IC₅₀ values of 0.7297 μM. To investigate the key groups that determine the activity of β-nitrostyrene derivatives and their interaction mode with the receptor, the molecular docking using the CDOCKER protocol in Discovery Studio 2016 was performed. The results showed that the hydrogen bonds between β-NO₂ and receptor GLY-143 and the π-π stacking between the aryl ring of the ligand and the imidazole ring of receptor HIS-41 significantly contributed to the ligand activity. Furthermore, the ligand-receptor absolute binding Gibbs free energies were calculated using the Binding Affinity Tool (BAT.py) to verify its correlation with the activity of β-nitrostyrene 3CLpro inhibitors as a scoring function. The higher correlation(r²=0.6) indicates that the absolute binding Gibbs free energy based on molecular dynamics can be used to predict the activity of new β-nitrostyrene 3CLpro inhibitors. These results provide valuable insights for the functional group-based design, structure optimization and the discovery of high accuracy activity prediction means of anti-COVID-19 lead compounds.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: I. Standard procedure

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein-ligand binding, the standard end-point calculations cannot produce useful outcomes in host-guest binding and thus are not recommended unless alterations are performed.

Computer-aided drug design, quantum-mechanical methods for biological problems

Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

A computational study of competing conformational selection and induced fit in an abiotic system

Host-guest complexations can be described by two competing mechanisms, conformational selection (CS) and induced fit (IF). In this work, we used a combination of nudged elastic band (NEB), adaptive steered molecular dynamics (ASMD), and density functional theory (DFT, with a correction for dispersion) to study the dynamics of the pathways (IF/CS) by which two conformers of basket B(+) and B(-) interconvert and trap CX₄ guests (X = Cl and Br). While the results from NEB/DFT studies disclosed host-guest noncovalent contacts reducing the basket's conformational dynamics, ASMD methodology suggested an associative mechanism for the guest complexation. With theory in excellent agreement with experiments, NEB and ASMD emerge as the methods of choice for studying dynamics of supramolecular systems.

Molecular Modeling Strategies of Cancer Multidrug Resistance

Cancer remains a leading cause of morbidity and mortality worldwide. Hence, the increase in cancer cases observed in the elderly population, as well as in children and adolescents, makes human malignancies a prime target for anticancer drug development. Although highly effective chemotherapeutic agents are continuously developed and approved for clinical treatment, the major impediment towards curative cancer therapy remains multidrug resistance (MDR). In recent years, intensive studies have been carried out on the identification of new therapeutic molecules to reverse MDR efflux transporters of the ATP-binding cassette (ABC) superfamily. Although a great deal of progress has been made in the development of specific inhibitors for certain MDR efflux pumps in experimental studies, advanced computational studies can accelerate this drug development process. In the literature, there are many experimental studies on the impact of natural products and synthetic small molecules on the reversal of cancer MDR. Molecular modeling methods provide an opportunity to explain the activity of these molecules on the ABC-transporter family with non-covalent interactions as well as it is possible to carry out studies for the discovery of new anticancer drugs specific to MDR with these methods. The coordinate file of the 3-dimensional (3D) structure of the target protein is indispensable for molecular modeling studies. In some cases where a 3D structure cannot be obtained by experimental methods, the homology modeling method can be applied to obtain the file containing the target protein's information including atomic coordinates, secondary structure assignments, and atomic connectivity. Homology modeling studies are of great importance for efflux transporter proteins that still lack 3D structures due to crystallization problems with multiple hydrophobic transmembrane domains. Quantum mechanics, molecular docking and molecular dynamics simulation applications are the most frequently used molecular modeling methods in the literature to investigate non-covalent interactions between the drug-ABC transporter superfamily. The quantitative structure-activity relationship (QSAR) model provides a relationship between the chemical properties of a compound and its biological activity. Determining the pharmacophore region for a new drug molecule by superpositioning a series of molecules according to their physicochemical properties using QSAR models is another method in which molecular modeling is used in computational drug development studies with ABC transporter proteins. There are also in silico absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) studies conducted to make a prediction about the pharmacokinetic properties, and drug-likeness of new molecules. Drug repurposing studies, which have become a trending topic in recent years, involve identifying possible new targets for an already approved drug molecule. There are few studies in the literature in which drug repurposing performed by molecular modelling methods has been applied on ABC transporter proteins. The aim of the current paper is to create a complete review of drug development studies including aforementioned molecular modeling methods carried out between the years 2019-2021. Furthermore, an intensive investigation is also conducted on licensed applications and free web servers used in in silico studies. The current review is an up-to-date guide for researchers who plan to conduct computational studies with MDR transporter proteins.

On the Interactions of Melatonin/β-Cyclodextrin Inclusion Complex: A Novel Approach Combining Efficient Semiempirical Extended Tight-Binding (xTB) Results with Ab Initio Methods

Melatonin (MT) is a molecule of paramount importance in all living organisms, due to its presence in many biological activities, such as circadian (sleep-wake cycle) and seasonal rhythms (reproduction, fattening, molting, etc.). Unfortunately, it suffers from poor solubility and, to be used as a drug, an appropriate transport vehicle has to be developed, in order to optimize its release in the human tissues. As a possible drug-delivery system, β-cyclodextrin (βCD) represents a promising scaffold which can encapsulate the melatonin, releasing when needed. In this work, we present a computational study supported by experimental IR spectra on inclusion MT/βCD complexes. The aim is to provide a robust, accurate and, at the same time, low-cost methodology to investigate these inclusion complexes both with static and dynamic simulations, in order to study the main actors that drive the interactions of melatonin with β-cyclodextrin and, therefore, to understand its release mechanism.