Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

Discovery of highly potent SARS-CoV-2 nsp14 methyltransferase inhibitors based on adenosine 5'-carboxamides

The emergence of SARS-CoV-2, the causative agent of COVID-19, has highlighted the need for advanced antiviral strategies. Targeting the coronaviral methyltransferase nsp14, which is essential for RNA capping, offers a promising approach for the development of small-molecule inhibitors. We designed and synthesized a series of adenosine 5'-carboxamide derivatives as potential nsp14 inhibitors and identified coumarin analogs to be particularly effective. Structural modifications revealed the importance of the 5'-carboxyl moiety for the inhibitory activity, showing superior efficacy compared to other modifications. Notably, compound 18l (HK370) demonstrated high selectivity and favorable in vitro pharmacokinetic properties and exhibited moderate antiviral activity in cell-based assays. These findings provide a robust foundation for developing targeted nsp14 inhibitors as a potential treatment for COVID-19 and related diseases.

Challenges in natural product-based drug discovery assisted with in silico-based methods

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

Benzimidazoles Containing Piperazine Skeleton at C-2 Position as Promising Tubulin Modulators with Anthelmintic and Antineoplastic Activity

Benzimidazole anthelmintic drugs hold promise for repurposing as cancer treatments due to their interference with tubulin polymerization and depolymerization, manifesting anticancer properties. We explored the potential of benzimidazole compounds with a piperazine fragment at C-2 as tubulin-targeting agents. In particular, we assessed their anthelmintic activity against isolated Trichinella spiralis muscle larvae and their effects on glioblastoma (U-87 MG) and breast cancer (MDA-MB-231) cell lines. Compound 7c demonstrated exceptional anthelmintic efficacy, achieving a 92.7% reduction in parasite activity at 100 μg/mL after 48 hours. In vitro cytotoxicity analysis of MDA-MB 231 and U87 MG cell lines showed that derivatives 7b, 7d, and 7c displayed lower IC50 values compared to albendazole (ABZ), the control. These piperazine benzimidazoles effectively reduced cell migration in both cell lines, with compound 7c exhibiting the most significant reduction, making it a promising candidate for further study. The binding mode of the most promising compound 7c, was determined using the induced fit docking-molecular dynamics (IFD-MD) approach. Regular docking and IFD were also employed for comparison. The IFD-MD analysis revealed that 7c binds to tubulin in a unique binding cavity near that of ABZ, but the benzimidazole ring was fitted much deeper into the binding pocket. Finally, the absolute free energy of perturbation technique was applied to evaluate the 7c binding affinity, further confirming the observed binding mode.

Simulation of Molecular Dynamics of SARS-CoV-2 S-Protein in the Presence of Multiple Arbidol Molecules: Interactions and Binding Mode Insights

In this work, we evaluated the antiviral activity of Arbidol (Umifenovir) against SARS-CoV-2 using a pseudoviral system with the glycoprotein S of the SARS-CoV-2 virus on its surface. In order to search for binding sites to protein S of the virus, we described alternative binding sites of Arbidol in RBD and in the ACE-2-RBD complex. As a result of our molecular dynamics simulations combined with molecular docking data, we note the following fact: wherever the molecules of Arbidol bind, the interaction of the latter affects the structural flexibility of the protein. This interaction may result both in a change in the shape of the domain-enzyme binding interface and simply in a change in the structural flexibility of the domain, which can subsequently affect its affinity to the enzyme. In addition, we examined the possibility of Arbidol binding in the stem part of the surface protein. The possibility of Arbidol binding in different parts of the protein is not excluded. This may explain the antiviral activity of Arbidol. Our results could be useful for researchers searching for effective SARS-CoV-2 virus inhibitors targeting the viral entry stage.

Rational design of selective HDAC2 inhibitors for liver cancer treatment: computational insights into the selectivity mechanism through molecular dynamics simulations and QM/MM calculations

The rational design of selective histone deacetylase 2 (HDAC2) inhibitors is beneficial for the therapeutic treatment of liver cancer, though HDAC2 is highly homologous to HDAC8, which may lead to undesired side effects due to the pan-inhibition towards HDAC2 and HDAC8. To clarify the structural basis of selective inhibition towards HDAC2 over HDAC8, we utilized multiple in silico strategies, including sequence alignment, structural comparison, molecular docking, molecular dynamics simulations, free energy calculations, alanine scanning mutagenesis, pharmacophore modeling, protein contacts atlas analysis and QM/MM calculations to study the binding patterns of HDAC2/8 selective inhibitors. Through the whole process described above, it is found that although HDAC2 has conserved GLY154 and PHE210 that also exist within HDAC8, namely GLY151 and PHE208, the two isoforms exhibit diverse binding modes towards their inhibitors. Typically, HDAC2 inhibitors interact with the Zn²⁺ ions through the core chelate group, while HDAC8 inhibitors adopt a bent conformation within the HDAC8 pocket that inclines to be in contact with the Zn²⁺ ions through the terminal hydroxamic acid group. In summary, our data comprehensively elucidate the selectivity mechanism towards HDAC2 over HDAC8, which would guide the rational design of selective HDAC2 inhibitors for liver cancer treatment.

1H-benzimidazole-2-yl hydrazones as tubulin-targeting agents: Synthesis, structural characterization, anthelmintic activity and antiproliferative activity against MCF-7 breast carcinoma cells and molecular docking studies

In the present study, fifteen benzimidazolyl-2-hydrazones 7a-7o of fluoro-, hydroxy- and methoxy-substituted benzaldehydes and 1,3-benzodioxole-5-carbaldehyde were synthesized and their structure was identified by IR, NMR, and elemental analysis. The compounds 7j 2-(3-hydroxybenzylidene)-1-(5(6)-methyl-1H-benzimidazol-2-yl)hydrazone and 7i 2-(3-hydroxybenzylidene)-1-(1H-benzimidazol-2-yl)hydrazone have exerted the strongest anthelmintic activity (100% after 24 h incubation period at 37 °C) against isolated muscle larvae of Trichinella spiralis in an in vitro experiment. The in vitro cytotoxicity assay towards MCF-7 breast cancer cells and mouse embryo fibroblasts 3T3 showed that the studied benzimidazolyl-2-hydrazones exhibit low to moderate cytotoxic effects. The ability of the studied benzimidazolyl-2-hydrazones to modulate microtubule polymerization was confirmed and suggested that their anthelmintic action is mediated through inhibition of the tubulin polymerization likewise the other known benzimidazole anthelmitics. It was also shown that the four most promising benzimidazolyl-2-hydrazones do not affect significantly the AChE activity even at high tested concentration, thus indicating that they do not have the potential for neurotoxic effects. The binding mode of compounds 7j and 7n in the colchicine-binding site of tubulin were clarified by molecular docking simulations. Taken together, these results demonstrate that for the synthesized benzimidazole derivatives the anthelmintic activity against T. spiralis and the inhibition of tubulin polymerization are closely related.

Free energy perturbation in the design of EED ligands as inhibitors of polycomb repressive complex 2 (PRC2) methyltransferase

Free Energy Perturbation (FEP) calculations can provide high-confidence predictions of the interaction strength between a ligand and its protein target. We sought to explore a series of triazolopyrimidines which bind to the EED subunit of the PRC2 complex as potential anticancer therapeutics, using FEP calculations to inform compound design. Combining FEP predictions with a late-stage functionalisation (LSF) inspired synthetic approach allowed us to rapidly evaluate structural modifications in a previously unexplored region of the EED binding site. This approach generated a series of novel triazolopyrimidine EED ligands with improved physicochemical properties and which inhibit PRC2 methyltransferase activity in a cancer-relevant G401 cell line.

Computational prediction method to decipher receptor-glycoligand interactions in plant immunity

Microbial and plant cell walls have been selected by the plant immune system as a source of microbe- and plant damage-associated molecular patterns (MAMPs/DAMPs) that are perceived by extracellular ectodomains (ECDs) of plant pattern recognition receptors (PRRs) triggering immune responses. From the vast number of ligands that PRRs can bind, those composed of carbohydrate moieties are poorly studied, and only a handful of PRR/glycan pairs have been determined. Here we present a computational screening method, based on the first step of molecular dynamics simulation, that is able to predict putative ECD-PRR/glycan interactions. This method has been developed and optimized with Arabidopsis LysM-PRR members CERK1 and LYK4, which are involved in the perception of fungal MAMPs, chitohexaose (1,4-β-d-(GlcNAc)6 ) and laminarihexaose (1,3-β-d-(Glc)6 ). Our in silico results predicted CERK1 interactions with 1,4-β-d-(GlcNAc)6 whilst discarding its direct binding by LYK4. In contrast, no direct interaction between CERK1/laminarihexaose was predicted by the model despite CERK1 being required for laminarihexaose immune activation, suggesting that CERK1 may act as a co-receptor for its recognition. These in silico results were validated by isothermal titration calorimetry binding assays between these MAMPs and recombinant ECDs-LysM-PRRs. The robustness of the developed computational screening method was further validated by predicting that CERK1 does not bind the DAMP 1,4-β-d-(Glc)6 (cellohexaose), and then probing that immune responses triggered by this DAMP were not impaired in the Arabidopsis cerk1 mutant. The computational predictive glycan/PRR binding method developed here might accelerate the discovery of protein-glycan interactions and provide information on immune responses activated by glycoligands.

Structural basis for tailor-made selective PI3K α/β inhibitors: a computational perspective

PI3K α and β are Class IA PI3K isoforms that share a highly homologous ATP binding site, differing only in a few residues around the binding site.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Going All In: A Strategic Investment in In Silico Toxicology

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

Recent improvements to the free energy perturbation (FEP) calculations, especially FEP+ , established their utility for pharmaceutical lead optimization. Herein, we propose a modified version of the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol, based on detail studies on several targets by probing a large number of perturbations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop motions and considerable structural changes can be obtained by extending the prior to REST (pre-REST) sampling time from 0.24 ns/λ to 5 ns/λ and 2 × 10 ns/λ, respectively. With this new protocol, much more precise ∆∆G values of the individual perturbations, including the sign of the transformations and decreased error were obtained. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence. Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in the ligand binding domain (LBD) has considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+ . Our improved protocol may further increase the FEP+ accuracy.

In Silico Predictions of Endocrine Disruptors Properties

Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

Discovery of GlyT2 Inhibitors Using Structure-Based Pharmacophore Screening and Selectivity Studies by FEP+ Calculations

In recent years, mammalian Glycine transporter 2 (GlyT2) has emerged as a promising target for the development of compounds against chronic pain states. In our current work, we discovered a new set of promising hits that inhibit the glycine transporter at nano- and micromolar activity and have excellent selectivity over GlyT1 (as shown by in vitro studies) using a newly designed virtual screening (VS) protocol that combines a structure-based pharmacophore and docking screens with a success rate of 75%. Furthermore, the free energy perturbation calculations and molecular dynamics (MD) studies revealed the GlyT2 amino acid residues critical for the binding and selectivity of both Glycine and our Hit1 compound. The FEP+ results well-matched with the available literature mutational data proving the quality of the generated GlyT2 structure. On the basis of these results, we propose that our hit compounds may lead to new chronic pain agents to address unmet and challenging clinical needs.

Computational insights on agonist and antagonist mechanisms of estrogen receptor α induced by bisphenol A analogues

Structural analogues of bisphenol A (BPA) have become widely used as alternatives in BPA-free products. Most toxicological investigations have focused on the estrogenic activities of these analogues, which have been considered as potential environmental estrogens. However, recent studies revealed that certain BPA analogues could dramatically inhibit the proliferation of breast cancer cells, and exhibited strong anti-estrogenic effects compared with the antagonist 4-hydroxytamoxifen (OHT). Thus, we adopted computational models combining molecular dynamics simulations and binding free energy calculations to explore the underlying molecular basis of BPA analogues binding to estrogen receptor α (ERα). We also evaluated ligand-induced structural rearrangements of ERα at the atomic level. Conformational analyses showed that induced-fit H-bonding recognition by Thr347 was an important factor distinguishing antagonist from agonist BPA analogues. Moreover, antagonists of BPA analogues could indirectly induce the structural reposition of key helix 12 and produce an antagonistic conformation of ERα. Compared with OHT, the binding affinity of BPA analogues is stronger for antagonists than agonists. Taken together, we therefore propose computational indicators for screening of anti-estrogenic activities of BPA analogues, which may be beneficial for predicting the estrogenic or anti-estrogenic effects of BPA alternatives.