The many roles of computation in drug discovery

Do electrostatic interactions make a difference in physics-based AutoDock4 scoring function?

Physics-based scoring function AutoDock4 is one of the most successfully applied tools in the area of structure-based drug design. However, current scoring functions are still far from being perfect. In a recent work highlighting the strengths and deficiencies of current scoring functions, we discovered that the residual error of ΔGbind predictions made by AutoDock4 is highly correlated to the presence of formally charged fragments in a ligand. In this work, we study how the use of the high-quality atomic charges, applied for contemporary force fields calculation, affects the quality of the experimental ΔGbind prediction by means of AutoDock4. We initially expected that the previously found discrepancy could be attributed to the Gasteiger charges used within AutoDock4. We show that AutoDock4 is, surprisingly, not sensitive to the charges used, and the use of QC-derived atomic charges does not lead to any statistical improvements. We also briefly discuss the role of the explicit empirical hydrogen bond term along with the electrostatic term.

Anticonvulsant activity of Tetrahydrolinalool: behavioral, electrophysiological, and molecular docking approaches

Tetrahydrolinalool (THL) is an acyclic monoterpene alcohol, produced during linalol metabolism and also a constituent of essential oils. As described in the literature, many monoterpenes present anticonvulsant properties, and thus we became interested in evaluating the anticonvulsant activity of Tetrahydrolinalool using in mice model as well as in silico approaches. Our results demonstrated that THL increased latency to seizure onset and also reduced the mortality, in picrotoxin induced seizure tests. The results may be related to GABAergic regulation, which was also suggested in seizure testing induced by 3-mercapto-propionic acid. In the strychnine-induced seizure testing, none of the groups pretreated with THL modulated the parameters indicative of anticonvulsant effect. The electrophysiological results revealed that THL treatment reduces seizures induced by pentylenetetrazole. The in silico molecular docking studies showed that the interaction between THL and a GABAA receptor model formed a stable complex, in comparison to the crystaligraphic structure of diazepam, a structurally related ligand. In conclusion, all the evidences showed that THL presents effective anticonvulsant activity related to the GABAergic pathway, being a candidate for treatment of epileptic syndromes.

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

C-Terminal Decarboxylation of Proline-Derived Building Blocks for Protein-Binding Peptides

Using a set of conformationally restricted Proline-derived Modules (ProMs), our group has recently succeeded in developing inhibitors for the enabled/vasodilator-stimulated phosphoprotein homology 1 (EVH1) domain, which is a key mediator of cell migration and plays an important role in tumor metastasis. While these (formally) pentapeptidic compounds show nanomolecular binding affinities towards EVH1, their drug-like properties and cell permeability need to be further optimized before they can be clinically tested as therapeutic agents against metastasis. In this study, we sought to improve these properties by removing the C-terminal carboxylic acid function of our peptoids, either by late-stage decarboxylation or by direct synthesis. For late-stage decarboxylation of ProM-like systems, a method for reductive halo decarboxylation was optimized and applied to several proline-derived substrates. In this way, a series of new decarboxy ProMs suitable as building blocks for decarboxy EVH1 inhibitors were obtained. In addition, we incorporated decarboxy-ProM-1 into the pentapeptide-like compound Ac[2ClF][ProM-2][Decarb-ProM-1], which showed similar affinity towards EVH1 as the methyl ester derivative (Ac[2Cl-F][ProM-2][ProM1]OMe). However, despite better calculated drug-like properties, this compound did not inhibit chemotaxis in a cellular assay.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Investigative analysis of blood-brain barrier penetrating potential of electronic nicotine delivery systems (e-cigarettes) chemicals using predictive computational models

INTRODUCTION

Seizures are known potential side effects of nicotine toxicity and have been reported in electronic nicotine delivery systems (ENDS, e-cigarettes) users, with the majority involving youth or young adults.

AREAS COVERED

Using chemoinformatic computational models, chemicals (including flavors) documented to be present in ENDS were compared to known neuroactive compounds to predict the blood-brain barrier (BBB) penetration potential, central nervous system (CNS) activity, and their structural similarities. The literature search used PubMed/Google Scholar, through September 2023, to identify individual chemicals in ENDS and neuroactive compounds.The results show that ENDS chemicals in this study contain >60% structural similarity to neuroactive compounds based on chemical fingerprint similarity analyses. The majority of ENDS chemicals we studied were predicted to cross the BBB, with approximately 60% confidence, and were also predicted to have CNS activity; those not predicted to passively diffuse through the BBB may be actively transported through the BBB to elicit CNS impacts, although it is currently unknown.

EXPERT OPINION

In lieu of in vitro and in vivo testing, this study screens ENDS chemicals for potential CNS activity and predicts BBB penetration potential using computer-based models, allowing for prioritization for further study and potential early identification of CNS toxicity.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Comprehensive insights into herbal P-glycoprotein inhibitors and nanoformulations for improving anti-retroviral therapy efficacy

The worldwide HIV cases were 39.0 million (33.1-45.7 million) in 2022. Due to genetic variations, HIV-1 is more easily transmitted than HIV-2 and favours CD4 + T cells and macrophages, producing AIDS. Conventional HIV drug therapy has many drawbacks, including adherence issues leading to resistance, side effects that lower life quality, drug interactions, high costs limiting global access, inability to eliminate viral reservoirs, chronicity requiring lifelong treatment, emerging toxicities, and a focus on managing infections. Conventional dosage forms have bioavailability issues due to intestinal P-glycoprotein (P-gp) efflux, which can reduce anti-retroviral drug efficacy and lead to resistance. Use of phyto-constituents with P-gp regulating actions has great benefits for semi-synthetic modification to create formulations with greater bioavailability and reduced toxicity, which improves drug effectiveness. Lipid-based nanocarriers, solid lipid nanoparticles, nanostructured lipid carriers, polymer-based nanocarriers, and inorganic nanoparticles may inhibit P-gp efflux. Employing potent P-gp inhibitors within nanocarriers as a Trojan horse approach can enhance the intracellular accumulation of anti-retroviral drugs (ARDs), which are substrates for efflux transporters. This technique increases oral bioavailability and offers lower-dose options, boosting HIV patient compliance and lowering costs. Molecular docking of the inhibitor with P-gp may anticipate optimum binding and function, allowing drug efflux to be minimised.

Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a-h), and hydrazine monohydrate (NH2NH2•H2O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a-h). After synthesis and characterization of the mentioned cinnolines (3a-h), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, and hDHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC50, EC50, etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds (3a-h). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines (3a-h), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.

Molecular Docking Improved with Human Spatial Perception Using Virtual Reality

Adaptive steered molecular dynamics (ASMD) is a computational biophysics method in which an external force is applied to a selected set of atoms or a specific reaction coordinate to induce a particular molecular motion. Virtual reality (VR) based methods for protein-ligand docking are beneficial for visualizing on-the-fly interactive molecular dynamics and performing promising docking trajectories. In this paper, we propose a novel method to guide ASMD with optimal trajectories collected from human experiences using interactive molecular dynamics in virtual reality (iMD-VR). We also explain the benefits of using VR as a tool for expediting the process of ligand binding, outlining an experimental protocol that enables iMD-VR users to guide Amprenavir into and out of the binding pockets of HIV-1 protease and recreate their respective crystallographic binding poses within 5 minutes. Later, we discuss our analysis of the results from iMD-VR-assisted ASMD simulation and assess its performance compared to a standard ASMD simulation. From the accuracy point of view, our proposed method calculates higher Potential Mean Force (PMF) values consistently relative to a standard ASMD simulation with an almost twofold increase in all the experiments. Finally, we describe the novelty of the research and discuss results showcasing a faster and more effective convergence of the ligand to the protein's binding site as compared to a standard molecular dynamics simulation, proving the effectiveness of VR in the field of drug discovery. Future work includes the development of an artificial intelligence algorithm capable of predicting optimal binding trajectories for many protein-ligand pairs, as well as the required force needed to steer the ligand to follow the said trajectory.

Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Identification and molecular docking of tyrosinase inhibitory peptides from allophycocyanin in Spirulina platensis

BACKGROUND

Tyrosinase, a copper-containing metalloenzyme with catalytic activity, is widely found in mammals. It is the key rate-limiting enzyme that catalyzes melanin synthesis. For humans, tyrosinase is beneficial to the darkening of eyes and hair. However, excessive deposition of melanin in the skin can lead to dull skin color and lead to pigmentation. Therefore, many skin-whitening compounds have been developed to decrease tyrosinase activity. This study aimed to identify a new tyrosinase inhibitory peptide through enzymatic hydrolysis, in vitro activity verification, molecular docking, and molecular dynamics (MD) simulation.

RESULTS

A tripeptide Asp-Glu-Arg (DER) was identified, with a '-CDOCKER_Energy' value of 121.26 Kcal mol-1 . DER has effective tyrosinase inhibitory activity. Research shows that its half maximal inhibitory concentration value is 1.04 ± 0.01 mmol L-1 . In addition, DER binds to tyrosinase residues His85, His244, His259, and Asn260, which are key residues that drive the interaction between the peptide and tyrosinase. Finally, through MD simulation, the conformational changes and structural stability of the complexes were further explored to verify and supplement the results of molecular docking.

CONCLUSION

This experiment shows that DER can effectively inhibit tyrosinase activity. His244, His259, His260, and Asn260 are the critical residues that drive the interaction between the peptide and tyrosinase, and hydrogen bonding is an important force. DER from Spirulina has the potential to develop functional products with tyrosinase inhibition. © 2024 Society of Chemical Industry.

State-of-the-Art Molecular Biophysics in Russia

Thirty years ago, scientists' attention was focused on studying individual molecules, as well as their structure and function [...].

Targeting MicroRNAs with Small Molecules

MicroRNAs (miRs) have been implicated in numerous diseases, presenting an attractive target for the development of novel therapeutics. The various regulatory roles of miRs in cellular processes underscore the need for precise strategies. Recent advances in RNA research offer hope by enabling the identification of small molecules capable of selectively targeting specific disease-associated miRs. This understanding paves the way for developing small molecules that can modulate the activity of disease-associated miRs. Herein, we discuss the progress made in the field of drug discovery processes, transforming the landscape of miR-targeted therapeutics by small molecules. By leveraging various approaches, researchers can systematically identify compounds to modulate miR function, providing a more potent intervention either by inhibiting or degrading miRs. The implementation of these multidisciplinary approaches bears the potential to revolutionize treatments for diverse diseases, signifying a significant stride towards the targeting of miRs by precision medicine.

Calculation of protein-ligand binding entropies using a rule-based molecular fingerprint

The use of fast in silico prediction methods for protein-ligand binding free energies holds significant promise for the initial phases of drug development. Numerous traditional physics-based models (e.g., implicit solvent models), however, tend to either neglect or heavily approximate entropic contributions to binding due to their computational complexity. Consequently, such methods often yield imprecise assessments of binding strength. Machine learning models provide accurate predictions and can often outperform physics-based models. They, however, are often prone to overfitting, and the interpretation of their results can be difficult. Physics-guided machine learning models combine the consistency of physics-based models with the accuracy of modern data-driven algorithms. This work integrates physics-based model conformational entropies into a graph convolutional network. We introduce a new neural network architecture (a rule-based graph convolutional network) that generates molecular fingerprints according to predefined rules specifically optimized for binding free energy calculations. Our results on 100 small host-guest systems demonstrate significant improvements in convergence and preventing overfitting. We additionally demonstrate the transferability of our proposed hybrid model by training it on the aforementioned host-guest systems and then testing it on six unrelated protein-ligand systems. Our new model shows little difference in training set accuracy compared to a previous model but an order-of-magnitude improvement in test set accuracy. Finally, we show how the results of our hybrid model can be interpreted in a straightforward fashion.

Assessment of the Anti-Breast Cancer Effects of Urolithin with Molecular Docking Studies in the In Vitro Condition: Introducing a Novel Chemotherapeutic Drug

A lot of research has been done on using natural items as diabetes treatment. The molecular docking study was conducted to evaluate the inhibitory activities of urolithin A against α-amylase, α-glucosidase, and aldose reductase. The molecular docking calculations indicated the probable interactions and the characteristics of these contacts at an atomic level. The results of the docking calculations showed the docking score of urolithin A against α-amylase was -5.169 kcal/mol. This value for α-glucosidase and aldose reductase was -3.657 kcal/mol and -7.635 kcal/mol, respectively. In general, the outcomes of the docking calculations revealed that urolithin A can construct several hydrogen bonds and hydrophobic contacts with the assessed enzymes and reduces their activities considerably. The properties of urolithin against common human breast cancer cell lines, i.e., SkBr3, MDA-MB-231, MCF-7, Hs578T, Evsa-T, BT-549, AU565 and 600MPE were evaluated. The IC50 of the urolithin was 400, 443, 392, 418, 397, 530, 566 and 551 against SkBr3, MDA-MB-231, MCF-7, Hs578T, Evsa-T, BT-549, AU565 and 600MPE, respectively. After doing the clinical trial studies, the recent molecule may be used as an anti-breast cancer supplement in humans. IC50 values of urolithin A on α-amylase, α-glucosidase, and aldose reductase enzymes were obtained at 16.14, 1.06 and 98.73 µM, respectively.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Molecular interaction studies of P3CL on bovine serum albumin through biophysical approach

In the fields of pharmacology and life sciences, it is essential to study how prescribed drugs interact with carrier proteins in human serum albumin. The current study has evaluated the binding properties of rhodanine derivative; (z)-2-(4-(5-((3-(3-chlorophenyl)-1-phenyl-1H-pyrazol-4-oxo-2-thioxothiazolidin-3-yl)benzamido)acetic acid (P3CL) on bovine serum albumin (BSA) by biophysical approach. BSA is a homology model of Human serum albumin. Due to the cost-effectiveness of Human Serum Albumin (HSA) we have studied the binding properties of rhodanine derivative (P3CL) on BSA. The BSA-P3CL interactions were investigated by fluorescence spectroscopy and revealed the presence of a static quenching mechanism. P3CL possesses good binding affinity on BSA with binding constant KP3CL = 5.36330 × 1013 M-1 binding free energy. We have calculated the binding free energy, the number of binding sites, and the binding constants. The establishment of hydrogen bonds and the active participation of amino acids in drug binding were confirmed by molecular docking studies. As conventional processes for the investigation of pharmacological drugs, therapeutic combinations, and coordinated drug intake, the offered strategies are simple to comprehend, accurate, and rapid to put into practice. Our findings will support an additional investigation into ligand's pharmacological activity.Communicated by Ramaswamy H. Sarma.

Structural identification and combination mechanism of iron (II)-chelating Atlantic salmon (Salmo salar L.) skin active peptides

In order to utilize salmon skin for high value, and investigate the structural identification and combination mechanism of iron (II)-chelating peptides systemically, Atlantic salmon (Salmo salar L.) skin, a by-product of Atlantic salmon processing, was treated by two-step enzymatic hydrolysis to obtain salmon skin active peptides (SSAP). Then they reacted with iron (II) to obtain iron (II)-chelating salmon skin active peptides (SSAP-Fe) with a high iron (II) chelating ability of 98.84%. The results of Fourier transform infrared spectroscopy (FTIR), circular dichroism (CD) spectroscopy, 8-anilino-1-naphthalenesulfonic acid ammonium salt hydrate (ANS) combined fluorescence measurement, isothermal titration calorimetry (ITC) and full wavelength ultraviolet (UV) scanning showed that the structural characteristics of SSAP changed before and after chelating iron (II). Reverse phase high performance liquid chromatography (RP-HPLC) and mass spectrometry were used to identify and quantify the peptides in SSAP-Fe. Four peptide sequences (STEGGG, GIIKYGDDFMH, PGQPGIGYDGPAGPPGPPGPPGAP and QNQRESWTTCRSQSSLPDG) were identified. The content of PGQPGIGYDGPAGPPGPPGPPGAP was the highest, at 25.17 μg/mg. The pharmacokinetic and pharmacodynamic properties of these four peptides were also investigated, and the results indicated that they have satisfactory predicted ADMET properties. Molecular docking technology was used to analyze the binding sites between iron (II) and SSAP, and it was found that PGQPGIGYDGPAGPPGPPGPPGAP had the lowest predicted binding energy with iron (II) and the most stable predicted binding energy with iron (II). This results showed that the stability of SSAP-Fe were closely related to the number of covalent bonds and the types of amino acids. This study revealed the structure and combination mechanism of SSAP-Fe, and indicated that SSAP-Fe prepared by chelation may be used as a Fe supplement that can be applied in functional foods or ingredients.

Drug discovery: In silico dry data can bypass biological wet data?

The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world of drug discovery, by pretending to identify new hits through the sole use of computer aided drug design (CADD). Does the golden era of dry data run the risk of overshadowing the importance of wet data and, in doing so, forget that in silico and biological data need each other in successful preclinical drug discovery programmes?

Synergistic effect of potential alpha-amylase inhibitors from Egyptian propolis with acarbose using in silico and in vitro combination analysis

BACKGROUND

Type 2 Diabetes mellitus (DM) is an affliction impacting the quality of life of millions of people worldwide. An approach used in the management of Type 2 DM involves the use of the carbohydrate-hydrolyzing enzyme inhibitor, acarbose. Although acarbose has long been the go-to drug in this key approach, it has become apparent that its side effects negatively impact patient adherence and subsequently, therapeutic outcomes. Similar to acarbose in its mechanism of action, bee propolis, a unique natural adhesive biomass consisting of biologically active metabolites, has been found to have antidiabetic potential through its inhibition of α-amylase. To minimize the need for ultimately novel agents while simultaneously aiming to decrease the side effects of acarbose and enhance its efficacy, combination drug therapy has become a promising pharmacotherapeutic strategy and a focal point of this study.

METHODS

Computer-aided molecular docking and molecular dynamics (MD) simulations accompanied by in vitro testing were used to mine novel, pharmacologically active chemical entities from Egyptian propolis to combat Type 2 DM. Glide docking was utilized for a structure-based virtual screening of the largest in-house library of Egyptian propolis metabolites gathered from literature, in addition to GC-MS analysis of the propolis sample under investigation. Thereafter, combination analysis by means of fixed-ratio combinations of acarbose with propolis and the top chosen propolis-derived phytoligand was implemented.

RESULTS

Aucubin, identified for the first time in propolis worldwide and kaempferol were the most promising virtual hits. Subsequent in vitro α-amylase inhibitory assay demonstrated the ability of these hits to significantly inhibit the enzyme in a dose-dependent manner with an IC50 of 2.37 ± 0.02 mM and 4.84 ± 0.14 mM, respectively. The binary combination of acarbose with each of propolis and kaempferol displayed maximal synergy at lower effect levels. Molecular docking and MD simulations revealed a cooperative binding mode between kaempferol and acarbose within the active site.

CONCLUSION

The suggested strategy seems imperative to ensure a steady supply of new therapeutic entities sourced from Egyptian propolis to regress the development of DM. Further pharmacological in vivo investigations are required to confirm the potent antidiabetic potential of the studied combination.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling

Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.

Illicium verum L. (Star Anise) Essential Oil: GC/MS Profile, Molecular Docking Study, In Silico ADME Profiling, Quorum Sensing, and Biofilm-Inhibiting Effect on Foodborne Bacteria

Illicium verum, or star anise, has many uses ranging from culinary to religious. It has been used in the food industry since ancient times. The main purpose of this study was to determine the chemical composition, antibacterial, antibiofilm, and anti-quorum sensing activities of the essential oil (EO) obtained via hydro-distillation of the aerial parts of Illicium verum. Twenty-four components were identified representing 92.55% of the analyzed essential oil. (E)-anethole (83.68%), limonene (3.19%), and α-pinene (0.71%) were the main constituents of I. verum EO. The results show that the obtained EO was effective against eight bacterial strains to different degrees. Concerning the antibiofilm activity, trans-anethole was more effective against biofilm formation than the essential oil when tested using sub-inhibitory concentrations. The results of anti-swarming activity tested against P. aeruginosa PAO1 revealed that I. verum EO possesses more potent inhibitory effects on the swarming behavior of PAO1 when compared to trans-anethole, with the percentage reaching 38% at a concentration of 100 µg/mL. The ADME profiling of the identified phytocompounds confirmed their important pharmacokinetic and drug-likeness properties. The in silico study using a molecular docking approach revealed a high binding score between the identified compounds with known target enzymes involved in antibacterial and anti-quorum sensing (QS) activities. Overall, the obtained results suggest I. verum EO to be a potentially good antimicrobial agent to prevent food contamination with foodborne pathogenic bacteria.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Conformational States of the CXCR4 Inhibitor Peptide EPI-X4-A Theoretical Analysis

EPI-X4, an endogenous peptide inhibitor, has exhibited potential as a blocker of CXCR4-a G protein-coupled receptor. This unique inhibitor demonstrates the ability to impede HIV-1 infection and halt CXCR4-dependent processes such as tumor cell migration and invagination. Despite its promising effects, a comprehensive understanding of the interaction between EPI-X4 and CXCR4 under natural conditions remains elusive due to experimental limitations. To bridge this knowledge gap, a simulation approach was undertaken. Approximately 150,000 secondary structures of EPI-X4 were subjected to simulations to identify thermodynamically stable candidates. This simulation process harnessed a self-developed reactive force field operating within the ReaxFF framework. The application of the Two-Phase Thermodynamic methodology to ReaxFF facilitated the derivation of crucial thermodynamic attributes of the EPI-X4 conformers. To deepen insights, an ab initio density functional theory calculation method was employed to assess the electrostatic potentials of the most relevant (i.e., stable) EPI-X4 structures. This analytical endeavor aimed to enhance comprehension of the inhibitor's structural characteristics. As a result of these investigations, predictions were made regarding how EPI-X4 interacts with CXCR4. Two pivotal requirements emerged. Firstly, the spatial conformation of EPI-X4 must align effectively with the CXCR4 receptor protein. Secondly, the functional groups present on the surface of the inhibitor's structure must complement the corresponding features of CXCR4 to induce attraction between the two entities. These predictive outcomes were based on a meticulous analysis of the conformers, conducted in a gaseous environment. Ultimately, this rigorous exploration yielded a suitable EPI-X4 structure that fulfills the spatial and functional prerequisites for interacting with CXCR4, thus potentially shedding light on new avenues for therapeutic development.

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.

Computer-aided drug design course for pharmacy major students in Shenyang Pharmaceutical University following the COVID-19 pandemic: Challenges and opportunities

The computer-aided drug design (CADD) course that spans biochemistry, computational chemistry, medicinal chemistry, and other cutting-edge sciences is considered an important course by pharmaceutical universities in China. The course teaches students how drugs bind to protein targets and exert their biological activities using computer tools, and covers the basic principles of drug development and optimization. Due to the lockdown and social distancing measures adopted during the coronavirus disease 2019 (COVID-19) pandemic, the CADD course in Shenyang Pharmaceutical University was briefly suspended. Thereafter, it was taught in the online mode by adopting a novel blended teaching method. Through a questionnaire survey and final report assessment, we found that blended teaching might provide an opportunity to stimulate greater motivation and interest in students as well as improve teaching effectiveness and learning outcomes of the course. This study describes how we conducted the CADD course during the COVID-19 period with the intention of providing a reference for other teachers to conduct similar courses.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host-guest SAMPL9 blinded challenge

We apply the Alchemical Transfer Method (ATM) and a bespoke fixed partial charge force field to the SAMPL9 bCD host-guest binding free energy prediction challenge that comprises a combination of complexes formed between five phenothiazine guests and two cyclodextrin hosts. Multiple chemical forms, competing binding poses, and computational modeling challenges pose significant obstacles to obtaining reliable computational predictions for these systems. The phenothiazine guests exist in solution as racemic mixtures of enantiomers related by nitrogen inversions that bind the hosts in various binding poses, each requiring an individual free energy analysis. Due to the large size of the guests and the conformational reorganization of the hosts, which prevent a direct absolute binding free energy route, binding free energies are obtained by a series of absolute and relative binding alchemical steps for each chemical species in each binding pose. Metadynamics-accelerated conformational sampling was found to be necessary to address the poor convergence of some numerical estimates affected by conformational trapping. Despite these challenges, our blinded predictions quantitatively reproduced the experimental affinities for the β-cyclodextrin host and, to a lesser extent, those with a methylated derivative. The work illustrates the challenges of obtaining reliable free energy data in in silico drug design for even seemingly simple systems and introduces some of the technologies available to tackle them.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Role of long non-coding RNAs in cancer: From subcellular localization to nanoparticle-mediated targeted regulation

Long non-coding RNAs (lncRNAs) are a class of RNA transcripts more than 200 nucleotides in length that play crucial roles in cancer development and progression. With the rapid development of high-throughput sequencing technology, a considerable number of lncRNAs have been identified as novel biomarkers for predicting the prognosis of cancer patients and/or therapeutic targets for cancer therapy. In recent years, increasing evidence has shown that the biological functions and regulatory mechanisms of lncRNAs are closely associated with their subcellular localization. More importantly, based on the important roles of lncRNAs in regulating cancer progression (e.g., growth, therapeutic resistance, and metastasis) and the specific ability of nucleic acids (e.g., siRNA, mRNA, and DNA) to regulate the expression of any target genes, much effort has been exerted recently to develop nanoparticle (NP)-based nucleic acid delivery systems for in vivo regulation of lncRNA expression and cancer therapy. In this review, we introduce the subcellular localization and regulatory mechanisms of various functional lncRNAs in cancer and systemically summarize the recent development of NP-mediated nucleic acid delivery for targeted regulation of lncRNA expression and effective cancer therapy.

Computational Prediction of 3,5-Diaryl-1H-Pyrazole and spiropyrazolines derivatives as potential acetylcholinesterase inhibitors for alzheimer disease treatment by 3D-QSAR, molecular docking, molecular dynamics simulation, and ADME-Tox

The efficacy of 40 synthesized variants of 3,5-diaryl-1H-pyrazole and spiropyrazoline' derivatives as acetylcholinesterase inhibitors is verified using a quantitative three-dimensional structure-activity relationship (3D-QSAR) by comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA) models. In this research, different field models proved that CoMSIA/SE model is the best model with high predictive power compared to several models (Qved2 = O.65; R2 = 0.980; R2test = 0.727). Also, contour maps produced by CoMSIA/SE model have been employed to prove the key structural needs of the activity. Consequently, six new compounds have been generated. Among these compounds, M4 and M5 were the most active but remained toxic and had poor absorption capacities. While the M1, M2, M3 and M6 remained highly active while respecting ADMET's characteristics. Molecular docking results showed compound M2 better with acetylcholinesterase than compound 22. The interactions are classical hydrogen bonding with residues TYR:124, TYR:72, and SER:293, which play a critical role in the biological activity as AChE inhibitors. MD results confirmed the docking results and showed that compound M2 had satisfactory stability with (ΔGbinding = -151.225 KJ/mol) in the active site of AChE receptor compared with compound 22 (ΔGbinding = -133.375 KJ/mol). In addition, both compounds had good stability regarding RMSD, Rg, and RMSF. The previous results show that the newly designed compound M2 is more active in the active site of AChE receptor than compound 22.Communicated by Ramaswamy H. Sarma.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A protocol for systematic review

There is still unmet medical need in cancer treatment mainly due to drug resistance and adverse drug events. Therefore, the search for better drugs is essential. Computer-aided drug design (CADD) and discovery tools are useful to streamline the lengthy and costly drug development process. Anthraquinones are a group of naturally occurring compounds with unique scaffold that exert various biological properties including anticancer activities. This protocol describes a systematic review that provide insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment. It was prepared in accordance with the "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 guidelines, and published in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904). Search strategies will be developed based on the combination of relevant keywords and executed in PubMed, Scopus, Web of Science and MedRxiv. Only original studies that employed CADD as primary tool in virtual screening for the purpose of designing or discovering anti-cancer drugs involving anthraquinone scaffold published in English language will be included. Two independent reviewers will be involved to screen and select the papers, extract the data and assess the risk of bias. Apart from exploring the trends and types of CADD methods used, the target proteins of these compounds in cancer treatment will also be revealed in this review. It is believed that the outcome of this study could be utilized to support the ongoing research in similar area with better quality and greater probability of success, consequently optimizing the resources in subsequent in vitro, in vivo, non-clinical and clinical development. It will also serve as an evidence based scientific guide for new research to design novel anthraquinone-derived drug with improved efficacy and safety profile for cancer treatment.

Spectroscopic study and molecular simulation: Bovine serum albumin binding with anticancer Pt complex of amyl dithiocarbamate ligand

Until now, many methods have been proposed to treat cancer, such as radiation therapy and drug therapy, but none of them have caused a complete cure for cancer. Heavy metal complexes such as cisplatin are among the compounds used as drugs in chemotherapy against cancer cells. These compounds cause cell death and have anti-cancer properties, but they have side effects. The biochemical mechanism of cisplatin is related to its interaction with DNA through covalent binding. To reduce the toxicity of metallodrugs, new complexes can be designed containing S, S- bidentate ligands such as diethyldithiocarbamate. Moreover, anti-cancer compounds probably interact with proteins, such as HSA, before passing the cancerous cell membrane and DNA as a target. So, the function of proteins and their stabilities are expected to change. In this research, the mode of binding of [Pt (bpy) (amyl.dtc)]NO3 complex with BSA was evaluated by various thermodynamic methods. Negative binding enthalpy and entropy changes amounts show that the connection between the Platinum compound and BSA occurs via the van Der Waals type of hydrogen bond. The negative Gibbs free energy change was obtained through isothermal titration, which showed interaction proceeds spontaneously. Moreover, the emission titration data showed that protein fluorescence quenching by platinum agent titration is static. Binding, quenching constants, and binding site number were obtained by the Stern-Volmer equation, and only one binding site was determined for this interaction. A Scatchard plot with a positive slope shows the Pt agent-BSA formation is proceeding positively cooperative. The kinetic study displayed that the absorption monitoring followed the second-order model. Finally, molecular docking simulation showed that the position of the Pt agent on protein is placed I under region II.

Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management

Molecular docking is a computational technique that predicts the binding affinity of ligands to receptor proteins. Although it has potential uses in nutraceutical research, it has developed into a formidable tool for drug development. Bioactive substances called nutraceuticals are present in food sources and can be used in the management of diseases. Finding their molecular targets can help in the creation of disease-specific new therapies. The purpose of this review was to explore molecular docking's application to the study of dietary supplements and disease management. First, an overview of the fundamentals of molecular docking and the various software tools available for docking was presented. The limitations and difficulties of using molecular docking in nutraceutical research are also covered, including the reliability of scoring functions and the requirement for experimental validation. Additionally, there was a focus on the identification of molecular targets for nutraceuticals in numerous disease models, including those for sickle cell disease, cancer, cardiovascular, gut, reproductive, and neurodegenerative disorders. We further highlighted biochemistry pathways and models from recent studies that have revealed molecular mechanisms to pinpoint new nutraceuticals' effects on disease pathogenesis. It is convincingly true that molecular docking is a useful tool for identifying the molecular targets of nutraceuticals in the management of diseases. It may offer information about how nutraceuticals work and support the creation of new therapeutics. Therefore, molecular docking has a bright future in nutraceutical research and has a lot of potentials to lead to the creation of brand-new medicines for the treatment of disease.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

A Variational Ansatz for Taylorized Imaginary Time Evolution

Being able to predict molecular properties and interactions is of utmost interest for academia as well as industry. But the vast complexity of strongly correlated molecular systems limits the performance of classical algorithms. In contrast, quantum computation has the potential to be a game changer in the field of molecular simulations. Despite the hope in quantum computation, the capabilities of current quantum computers are still insufficient for handling molecular systems of interest. In this paper, we propose a variational ansatz for today's noisy quantum computers to calculate the ground state with the help of imaginary time evolution. Although the imaginary time evolution operator is not unitary, it can be implemented on a quantum computer by a linear decomposition and subsequent Taylor series expansion. This has the advantage that only a set of shallow circuits needs to be computed on a quantum computer. The parallel nature of this algorithm can be exploited to speed-up simulations even further, if a privileged access to quantum computers is granted.

Antioxidant evaluation and computational prediction of prospective drug-like compounds from polyphenolic-rich extract of Hibiscus cannabinus L. seed as antidiabetic and neuroprotective targets: assessment through in vitro and in silico studies

BACKGROUND

Reports have implicated diabetes mellitus (DM) and Alzheimer's disease (AD) as some of the global persistent health challenges with no lasting solutions, despite of significant inputs of modern-day pharmaceutical firms. This study therefore, aimed to appraise the in vitro antioxidant potential, enzymes inhibitory activities, and as well carry out in silico study on bioactive compounds from polyphenolic-rich extract of Hibiscus cannabinus seed (PEHc).

METHODS

In vitro antioxidant assays were performed on PEHc using standard methods while the identification of phytoconstituents was carried out with high performance liquid chromatography (HPLC). For the in silico molecular docking using Schrodinger's Grid-based ligand docking with energetics software, seven target proteins were retrieved from the database ( https://www.rcsb.org/ ).

RESULTS

HPLC technique identified twelve chemical compounds in PEHc, while antioxidant quantification revealed higher total phenolic contents (243.5 ± 0.71 mg GAE/g) than total flavonoid contents (54.06 ± 0.09 mg QE/g) with a significant (p < 0.05) inhibition of ABTS (IC₅₀ = 218.30 ± 0.87 µg/ml) and 1, 1-diphenyl-2-picrylhydrazyl free radicals (IC₅₀ = 227.79 ± 0.74 µg/ml). In a similar manner, the extract demonstrated a significant (p < 0.05) inhibitory activity against α-amylase (IC₅₀ = 256.88 ± 6.15 µg/ml) and α-glucosidase (IC₅₀ = 183.19 ± 0.23 µg/ml) as well as acetylcholinesterase (IC₅₀ = 262.95 ± 1.47 µg/ml) and butyrylcholinesterase (IC₅₀ = 189.97 ± 0.82 µg/ml), respectively. Furthermore, In silico study showed that hibiscetin (a lead) revealed a very strong binding affinity energies for DPP-4, (PDB ID: 1RWQ) and α-amylase (PDB ID: 1SMD), gamma-tocopherol ( for peptide-1 receptor; PDB ID: 3C59, AChE; PDB ID: 4EY7 and BChE; PDB ID: 7B04), cianidanol for α-glucosidase; PDB ID: 7KBJ and kaempferol for Poly [ADP-ribose] polymerase 1 (PARP-1); PDB ID: 6BHV, respectively. More so, ADMET scores revealed drug-like potentials of the lead compounds identified in PEHc.

CONCLUSION

As a result, the findings of this study point to potential drug-able compounds in PEHc that could be useful for the management of DM and AD.

An Overview of the Potential Medicinal and Pharmaceutical Properties of Ru(II)/(III) Complexes

This review examines the existing knowledge about Ru(II)/(III) ion complexes with a potential application in medicine or pharmacy, which may offer greater potential in cancer chemotherapy than Pt(II) complexes, which are known to cause many side effects. Hence, much attention has been paid to research on cancer cell lines and clinical trials have been undertaken on ruthenium complexes. In addition to their antitumor activity, ruthenium complexes are under evaluation for other diseases, such as type 2 diabetes, Alzheimer's disease and HIV. Attempts are also being made to evaluate ruthenium complexes as potential photosensitizers with polypyridine ligands for use in cancer chemotherapy. The review also briefly examines theoretical approaches to studying the interactions of Ru(II)/Ru(III) complexes with biological receptors, which can facilitate the rational design of ruthenium-based drugs.

In Silico Method for the Screening of Phytochemicals against Methicillin-Resistant Staphylococcus Aureus

Methicillin-resistant Staphylococcus aureus (MRSA) has evolved resistance even against the last resort β-lactam antibiotics. This is because of the acquisition of an additional penicillin-binding protein 2a (PBP2a) which is a resistance determinant in MRSA. Currently, available PBP2a inhibitors are ineffective against life-threatening and fatal infections caused by microorganisms. Therefore, there is an urgent need to screen natural compounds that could overpass the resistance issue alone or in combination with antibacterial drugs. We studied the interactions of different phytochemicals with PBP2a so that crosslinking of peptidoglycans could be inhibited. In structure-based drug designing, in silico approach plays a key role in determining phytochemical interactions with PBP2a. In this study, a total of 284 antimicrobial phytochemicals were screened using the molecular docking approach. The binding affinity of methicillin, -11.241 kcal/mol, was used as the threshold value. The phytochemicals having binding affinities with PBP2a stronger than methicillin were identified, and the drug-likeness properties and toxicities of the screened phytochemicals were calculated. Out of the multiple phytochemicals screened, nine were found as good inhibitors to be PBP2a, among which cyanidin, tetrandrine, cyclomorusin, lipomycin, and morusin showed strong binding potential with the receptor protein. These best-selected phytochemicals were also docked to the allosteric site of PBP2a, and most of the compounds revealed strong interactions with the allosteric site. These compounds were safe to be used as drugs because they did not show any toxicity and had good bioactivity scores. Cyanidin had the highest binding affinity (S-score of -16.061 kcal/mol) with PBP2a and with high gastrointestinal (GI) absorption. Our findings suggest that cyanidin can be used as a drug against MRSA infection either in purified form or that its structure can lead to the development of more potent anti-MRSA medicines. However, experimental studies are required to evaluate the inhibitory potential of these phytochemicals against MRSA.

GC/MS Profiling, Antibacterial, Anti-Quorum Sensing, and Antibiofilm Properties of Anethum graveolens L. Essential Oil: Molecular Docking Study and In-Silico ADME Profiling

Anethum graveolens L. has been known as an aromatic, medicinal, and culinary herb since ancient times. The main purpose of this study was to determine the chemical composition, antibacterial, antibiofilm, and anti-quorum sensing activities of the essential oil (EO) obtained by hydro-distillation of the aerial parts. Twelve components were identified, representing 92.55% of the analyzed essential oil. Limonene (48.05%), carvone (37.94%), cis-dihydrocarvone (3.5%), and trans-carvone (1.07%) were the main identified constituents. Results showed that the obtained EO was effective against eight bacterial strains at different degrees. Concerning the antibiofilm activity, limonene was more effective against biofilm formation than the essential oil when tested using sub-inhibitory concentrations. The results of anti-swarming activity tested against P. aeruginosa PAO1 revealed that A. graveolens induced more potent inhibitory effects in the swarming behavior of the PAO1 strain when compared to limonene, with a percentage reaching 33.33% at a concentration of 100 µg/mL. The ADME profiling of the identified phytocompounds confirms their important pharmacokinetic and drug-like properties. The in-silico study using molecular docking approaches reveals a high binding score between the identified compounds and known target enzymes involved in antibacterial and anti-quorum sensing (QS) activities. Overall, the obtained results highlight the possible use of A. graveolens EO to prevent food contamination with foodborne pathogenic bacteria.

Accelerated Enveloping Distribution Sampling to Probe the Presence of Water Molecules

Determining the presence of water molecules at protein-ligand interfaces is still a challenging task in free-energy calculations. The inappropriate placement of water molecules results in the stabilization of wrong conformational orientations of the ligand. With classical alchemical perturbation methods, such as thermodynamic integration (TI), it is essential to know the amount of water molecules in the active site of the respective ligands. However, the resolution of the crystal structure and the correct assignment of the electron density do not always lead to a clear placement of water molecules. In this work, we apply the one-step perturbation method named accelerated enveloping distribution sampling (AEDS) to determine the presence of water molecules in the active site by probing them in a fast and straightforward way. Based on these results, we combined the AEDS method with standard TI to calculate accurate binding free energies in the presence of buried water molecules. The main idea is to perturb the water molecules with AEDS such that they are allowed to alternate between regular water molecules and non-interacting dummy particles while treating the ligand with TI over an alchemical pathway. We demonstrate the use of AEDS to probe the presence of water molecules for six different test systems. For one of these, previous calculations showed difficulties to reproduce the experimental binding free energies, and here, we use the combined TI-AEDS approach to tackle these issues.