Molecular modeling in drug discovery

Neuroprotective Properties of Coriander-Derived Compounds on Neuronal Cell Damage under Oxidative Stress-Induced SH-SY5Y Neuroblastoma and in Silico ADMET Analysis

An imbalance between reactive oxygen species (ROS) production and antioxidant defense driven by oxidative stress and inflammation is a critical factor in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. Coriander (Coriandrum sativum L.), a culinary plant in the Apiaceae family, displays various biological activities, including anticancer, antimicrobial, and antioxidant effects. Herein, neuroprotective properties of three major bioactive compounds derived from coriander (i.e., linalool, linalyl acetate, and geranyl acetate) were investigated on hydrogen peroxide-induced SH-SY5Y neuroblastoma cell death by examining cell viability, ROS production, mitochondrial membrane potential, and apoptotic profiles. Moreover, underlying mechanisms of the compounds were determined by measuring intracellular sirtuin 1 (SIRT1) enzyme activity incorporated with molecular docking. The results showed that linalool, linalyl acetate, and geranyl acetate elicited their neuroprotection against oxidative stress via protecting cell death, reducing ROS production, preventing cell apoptosis, and modulating SIRT1 longevity. Additionally, in silico pharmacokinetic predictions indicated that these three compounds are drug-like agents with a high probability of absorption and distribution, as well as minimal potential toxicities. These findings highlighted the potential neuroprotective linalool, linalyl acetate, and geranyl acetate for developing alternative natural compound-based neurodegenerative therapeutics and prevention.

The potential of Mitragyna speciosa leaves as a natural source of antioxidants for disease prevention

Mitragyna speciosa is famous for its addictive effect. On the other hand, this plant has good potential as an antioxidant agent, and so far, it was not explicitly explained what the most contributing compound in the leaves to that activity is. This study has been conducted using several computational methods to determine which compounds are the most active in interacting with cytochrome P450, myeloperoxidase, and NADPH oxidase proteins. First, virtual screening was carried out based on molecular docking, followed by profiling the properties of adsorption, distribution, metabolism, excretion, and toxicity (ADMET); the second one is the molecular dynamics (MD) simulations for 100 ns. The virtual screening results showed that three compounds acted as inhibitors for each protein: (-)-epicatechin, sitogluside, and corynoxeine. The ADMET profiles of the three compounds exhibit good drug ability and toxicity. The trajectories study from MD simulations predicts that the complexes of these three compounds with their respective target proteins are stable. Furthermore, these compounds identified in this computational study can be a potential guide for future experiments aimed at assessing the antioxidant properties through in vitro testing.

Automatic molecular fragmentation by evolutionary optimisation

Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ  mol - 1 , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ  mol - 1 for MBE2 and 24.3 kJ  mol - 1 for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ  mol - 1 were observed at the MBE2 and MBE3 levels, respectively.Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

Computational-based drug design of novel small molecules targeting p53-MDMX interaction

The regulation of the p53 tumor suppressor pathway is critically dependent on the activity of Murine Double Minute 2 (MDM2) and Murine Double Minute X (MDMX) proteins. In certain types of cancer cells, excessive amount of MDMX can poly-ubiquitinate p53, which can result in its degradation, leading to a subsequent reduction in the levels of this protein. Therefore, the design of small-molecule inhibitors targeting the MDMX-p53 interaction has emerged as a promising strategy for cancer therapy. In this study, we employed computational techniques including pharmacophore modeling and molecular docking to identify three potential small molecule inhibitors (CID_25094615, CID_137634453, and CID_25094344) of the MDMX-p53 interaction from a PubChem database. Molecular dynamics of 100000 ps were conducted to assess the stability of the MDMX-inhibitor complexes. Our results showed that all three compounds exhibit stable binding with MDMX, with significantly lower root mean square deviation (RMSD) and fluctuation (RMSF) values than the control ligand, indicating superior stability. Additionally, the three compounds exhibit stronger intermolecular hydrogen bond (HBOND) interactions compared to the control, suggesting stronger stability. Overall, our findings highlight the potential of these compounds as lead candidates for the development of novel anticancer agents that target the MDMX-p53 interaction.Communicated by Ramaswamy H. Sarma.

High-Throughput Molecular Modeling and Evaluation of the Anti-Inflammatory Potential of Açaí Constituents against NLRP3 Inflammasome

The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Probing the interaction between the metallo-β-lactamase SMB-1 and ampicillin by multispectral approaches combined with molecular dynamics

Metallo-β-lactamases (MβLs) hydrolyze and inactivate β-lactam antibiotics, are a pivotal mechanism conferring resistance against bacterial infections. SMB-1, a novel B3 subclass of MβLs from Serratia marcescens could deactivate almost all β-lactam antibiotics including ampicillin (AMP), which has posed a serious threat to public health. To illuminate the mechanism of recognition and interaction between SMB-1 and AMP, various fluorescence spectroscopy techniques and molecular dynamics simulation were employed. The results of quenching spectroscopy unraveled that AMP could make SMB-1 fluorescence quenching that mechanism was the static quenching; the synchronous and three-dimensional fluorescence spectra validated that the microenvironment and conformation of SMB-1 were altered after interaction with AMP. The molecular dynamics results demonstrated that the whole AMP enters the binding pocket of SMB-1, even though with a relatively bulky R1 side chain. Loop1 and loop2 in SMB-1 undergo significant fluctuations, and α2 (71-73) and local α5 (186-188) were turned into random coils, promoting zinc ion exposure consistent with circular dichroism spectroscopy results. The binding between them was driven by a combination of enthalpy and entropy changes, which was dominated by electrostatic force in agreement with the fluorescence observations. The present study brings structural insights and solid foundations for the design of new substrates for β-lactamases and the development of effective antibiotics that are resistant to superbugs.

Phytochemical profiling, antiviral activities, molecular docking, and dynamic simulations of selected Ruellia species extracts

The antiviral properties of the flowering aerial extracts of Ruellia tuberosa and Ruellia patula were investigated through phytochemical profiling via LC-MS/MS and HPLC techniques. Qualitative LC-MS/MS analyses identified seventy-seven metabolites from both Ruellia species. R. tuberosa had the highest phenolic content (49.3%), whereas R. patula had the highest flavonoid content (57.8%). Additionally, quantitative HPLC investigations of the compounds identified by LC-MS/MS were performed using the available standard compounds. The main constituents in the R. tuberosa extract was found to be catechin (5321.63 µg/g), gallic acid (2878.71 µg/g), and ellagic acid (2530.79 µg/g), whereas the major compounds in the R. patula extract was found to be rutin (11,074.19 µg/g) and chlorogenic acid (3157.35 µg/g). Furthermore, the antiviral activities of both Ruellia species against HAdV-40, herpes simplex type 2 and H1N1 were evaluated. These findings demonstrated that R. tuberosa was more active than R. patula against all tested viruses, except for the HSV-2 virus, against which R. patula showed greater activity than R. tuberosa, with IC50 values of 20, 65, 22.59, and 13.13 µg/ml for R. tuberosa flowering aerial parts and 32.26, 11.66, and 23.03 µg/ml for R. patula flowering aerial parts, respectively for HAdV-40, herpes simplex type 2, and H1N1. Additionally, computational docking and molecular dynamics simulations were used to assess the molecular interactions between the bioactive compounds and specific viral targets. The combined findings from the in-vitro and in-silico experiments comprehensively evaluated the antiviral activities of both Ruellia species extracts.

Enzymatic Fructosylation of Phenolic Compounds: A New Alternative for the Development of Antidiabetic Drugs

Enzymatic fructosylation has emerged as a strategy to enhance the hydrophilicity of polyphenols by introducing sugar moieties, leading to the development of phenolic glycosides, which exhibit improved solubility, stability, and biological activities compared to their non-glycosylated forms. This study provides a detailed analysis of the interactions between five phenolic fructosides (4MFPh, MFF, DFPh, MFPh, and MFPu) and twelve proteins (11β-HS1, CRP, DPPIV, IRS, PPAR-γ, GK, AMPK, IR, GFAT, IL-1ß, IL-6, and TNF-α) associated with the pathogenesis of T2DM. The strongest interactions were observed for phlorizin fructosides (DFPh) with IR (-16.8 kcal/mol) and GFAT (-16.9 kcal/mol). MFPh with 11β-HS1 (-13.99 kcal/mol) and GFAT (-12.55 kcal/mol). 4MFPh with GFAT (-11.79 kcal/mol) and IR (-12.11 kcal/mol). MFF with AMPK (-9.10 kcal/mol) and PPAR- γ (-9.71 kcal/mol), followed by puerarin and ferulic acid monofructosides. The fructoside group showed lower free energy binding values than the controls, metformin and sitagliptin. Hydrogen bonding (HB) was identified as the primary interaction mechanism, with specific polar amino acids such as serin, glutamine, glutamic acid, threonine, aspartic acid, and lysine identified as key contributors. ADMET results indicated favorable absorption and distribution characteristics of the fructosides. These findings provide valuable information for further exploration of phenolic fructosides as potential therapeutic agents for T2DM.

Quercetin and Kaempferol as Multi-Targeting Antidiabetic Agents against Mouse Model of Chemically Induced Type 2 Diabetes

Diabetes, a multifactorial metabolic disorder, demands the discovery of multi-targeting drugs with minimal side effects. This study investigated the multi-targeting antidiabetic potential of quercetin and kaempferol. The druggability and binding affinities of both compounds towards multiple antidiabetic targets were explored using pharmacokinetic and docking software (AutoDock Vina 1.1.2). Our findings showed that quercetin and kaempferol obey Lipinski's rule of five and exhibit desirable ADMET (absorption, distribution, metabolism excretion, and toxicity) profiles. Both compounds showed higher binding affinities towards C-reactive protein (CRP), interleukin-1 (IL-1), dipeptidyl peptidase-4 (DPP-IV), peroxisome proliferator-activated receptor gamma (PPARG), protein tyrosine phosphatase (PTP), and sodium-glucose co-transporter-1 (SGLT-1) compared to metformin (the positive control). Both quercetin and kaempferol inhibited α-amylase activity (in vitro) up to 20.30 ± 0.49 and 37.43 ± 0.42%, respectively. Their oral supplementation significantly reduced blood glucose levels (p < 0.001), improved lipid profile (p < 0.001), and enhanced total antioxidant status (p < 0.01) in streptozotocin-nicotinamide (STZ-NA)-induced diabetic mice. Additionally, both compounds significantly inhibited the proliferation of Huh-7 and HepG2 (cancer cells) (p < 0.0001) with no effect on the viability of Vero cell line (non-cancer). In conclusion, quercetin and kaempferol demonstrated higher binding affinities towards multiple targets than metformin. In vitro and in vivo antidiabetic potential along with the anticancer activities of both compounds suggest promise for further development in diabetes management. The combination of both drugs did not show a synergistic effect, possibly due to their same target on the receptors.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

A computational odyssey: uncovering classical β-lactamase inhibitors in dry fruits

In the antibacterial arsenal, β-lactams have held a prominent position, but increasing resistance due to unauthorized use and genetic factors requires new strategies. Combining β-lactamase inhibitors with broad-spectrum β-lactams proves effective in combating this resistance. ESBL producers demand new inhibitors, leading to the exploration of plant-derived secondary metabolites for potent β-lactam antibiotics or alternative inhibitors. Using virtual screening, molecular docking, ADMET analysis, and molecular dynamic simulation, this study actively analyzed the inhibitory activity of figs, cashews, walnuts, and peanuts against SHV-1, NDM-1, KPC-2, and OXA-48 β-lactamases. Using AutoDock Vina, the docking affinities of various compounds for target enzymes were initially screened, revealing 12 bioactive compounds with higher affinities for the target enzymes compared to Avibactam and Tazobactam. Top-scoring metabolites, including Oleanolic acid, Protocatechuic acid, and Tannin, were subjected to MD simulation studies to further analyze the stability of the docked complexes using WebGro. The simulation coordinates, in terms of RMSD, RMSF, SASA, Rg, and hydrogen bonds formed, showed that these phytocompounds are stable enough to retain in the active sites at various orientations. The PCA and FEL analysis also showed the stability of the dynamic motion of Cα residues of phytochemical-bound enzymes. The pharmacokinetic analysis of the top phytochemicals was performed to analyze their bioavailability and toxicity. This study provides new insights into the therapeutic potential of phytochemicals of selected dry fruits and contributes to future experimental studies to identify βL inhibitors from plants.Communicated by Ramaswamy H. Sarma.

A Combined Molecular Dynamics and Hydropathic INTeraction (HINT) Approach to Investigate Protein Flexibility: The PPARγ Case Study

Molecular Dynamics (MD) is a computational technique widely used to evaluate a molecular system's thermodynamic properties and conformational behavior over time. In particular, the energy analysis of a protein conformation ensemble produced though MD simulations plays a crucial role in explaining the relationship between protein dynamics and its mechanism of action. In this research work, the HINT (Hydropathic INTeractions) LogP-based scoring function was first used to handle MD trajectories and investigate the molecular basis behind the intricate PPARγ mechanism of activation. The Peroxisome Proliferator-Activated Receptor γ (PPARγ) is an emblematic example of a highly flexible protein due to the extended ω-loop delimiting the active site, and it is responsible for the receptor's ability to bind chemically different compounds. In this work, we focused on the PPARγ complex with Rosiglitazone, a common anti-diabetic compound and analyzed the molecular basis of the flexible ω-loop stabilization effect produced by the Oleic Acid co-binding. The HINT-based analysis of the produced MD trajectories allowed us to account for all of the energetic contributions involved in interconverting between conformational states and describe the intramolecular interactions between the flexible ω-loop and the helix H3 triggered by the allosteric binding mechanism.

Leveraging computational tools to combat malaria: assessment and development of new therapeutics

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

Structural Characterization and Rat Aortic Vascular Reactivity of Bradykinin-Potentiating Peptides (BPPs) from the Snake Venom of Bothrops moojeni from Delta do Parnaíba Region, Brazil

Snake venoms contain various bradykinin-potentiating peptides (BPPs). First studied for their vasorelaxant properties due to angiotensin converting enzyme (ACE) inhibition, these molecules present a range of binding partners, among them the argininosuccinate synthase (AsS) enzyme. This has renewed interest in their characterization from biological sources and the evaluation of their pharmacological activities. In the present work, the low molecular weight fraction of Bothrops moojeni venom was obtained and BPPs were characterized by mass spectrometry. Eleven BPPs or related peptides were sequenced, and one of them, BPP-Bm01, was new. Interestingly, some oxidized BPPs were detected. The three most abundant peptides were BPP-Bm01, BPP-Bax12, and BPP-13a, and their putative interactions with the AsS enzyme were investigated in silico. A binding cavity for these molecules was predicted, and docking studies allowed their ranking. Three peptides were synthesized and submitted to vasorelaxation assays using rat aortic rings. While all BPPs were active, BPP-Bm01 showed the highest potency in this assay. This work adds further diversity to BPPs from snake venoms and suggests, for the first time, a putative binding pocket for these molecules in the AsS enzyme. This can guide the design of new and more potent AsS activators.

Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

Anticancer Effects of Abietane Diterpene 7α-Acetoxy-6β-hydroxyroyleanone from Plectranthus grandidentatus and Its Semi-Synthetic Analogs: An In Silico Computational Approach

The abietane diterpenoid 7α-acetoxy-6β-hydroxyroyleanone (Roy) isolated from Plectranthus grandidentatus demonstrates cytotoxicity across numerous cancer cell lines. To potentiate anticancer attributes, a series of semi-synthetic Roy derivatives were generated and examined computationally. ADMET predictions were used to evaluate drug-likeness and toxicity risks. The antineoplastic potential was quantified by PASS. The DFT models were used to assess their reactivity and stability. Molecular docking determined cancer-related protein binding. MS simulations examined ligand-protein stability. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Favorable ADME attributes and acceptable toxicity profiles were determined for all compounds. Strong anticancer potential was shown across derivatives (Pa 0.819-0.879). Strategic modifications altered HOMO-LUMO gaps (3.39-3.79 eV) and global reactivity indices. Favorable binding was revealed against cyclin-dependent kinases, BCL-2, caspases, receptor tyrosine kinases, and p53. The ligand exhibited a stable binding pose in MD simulations. Network analysis revealed involvement in cancer-related pathways. In silico evaluations predicted Roy and derivatives as effective molecules with anticancer properties. Experimental progress is warranted to realize their chemotherapeutic potential.

Discovery of new α-glucosides, antiglycation agent, and in silico study of 2-(3,4-dihydroxyphenyl)-7,8-dihydroxy-3-methoxy-4H-chromen-4-one isolated from Pistacia chinensis

Pistacia chinensis is locally practiced for treating diabetes, pain, inflammation, and erectile dysfunction. Therefore, the current studies subjected the crude extract/fractions and the isolated compound (2-(3,4-dihydroxyphenyl)-7,8-dihydroxy-3-methoxy-4H-chromen-4-one) to α-glucosidase inhibitor and anti-glycation activities. The development of long-term complications associated with diabetes is primarily caused by chronic hyperglycemia. Regarding α-glucosidase, the most significant inhibitory effect was observed with compound 1 (93.09%), followed by the methanolic extract (80.87%) with IC50 values of 45.86 and 86.32 μM. The maximum anti-glycation potential was shown by an isolated compound 1 followed by methanolic extract with effect inhibition of 90.12 and 72.09, respectively. Compound 1 is expected to have the highest gastrointestinal absorption rate, with a predicted absorption rate of 86.156%. This indicates oral suitability. The compound 1 is expected to have no harmful effects on the liver. In addition, our docking results suggest that alpha-glucosidase and isolated compounds showed strong interaction with ILE821, GLN900, and ALA901 residues, along with a -11.95 docking score.

Unlocking Precision Docking for Metalloproteins

Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of high-affinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein-ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein-ligand interactions.

Formulation of novel bioactive gelatin inspired by cinnamaldehyde for combating multi-drug resistant bacteria: Characterization, molecular docking, pharmacokinetic analyses, and in vitro assessments

This study set out to formulate antibacterial and antioxidant gelatin boosted by cinnamaldehyde for combating multi-drug resistant bacteria previously obtained from chronic wounds. Towards this end, gelatin amine groups were conjugated with carbonyl groups of cinnamaldehyde, producing cinnamyl-gelatin Schiff bases. The physicochemical attributes of cinnamyl-gelatin Schiff bases were probed concerning alterations in chemical structures and microstructures compared to native gelatin. Besides, cinnamyl-gelatin Schiff bases exhibited higher thermal stability than gelatin, with a diminishing in solubility due to increases in hydrophobicity features. Interestingly, cinnamyl-gelatin derivatives exerted antibacterial activities versus multi-drug resistant Gram-negative and Gram-positive bacteria, showing maximum growth inhibition at the highest concentration of cinnamaldehyde incorporated into gelatin. The scavenging activities of gelatin against DPPH and ABTS•+ were promoted in cinnamyl-gelatin derivatives from 11.93 ± 0.6 % to 49.9 ± 2.5 % and 12.54 ± 0.63 % to 49.9 ± 3.12 %, respectively. Remarkably, cinnamyl-gelatin derivatives induced the proliferation of fibroblast cells, implying their prospective applications in tissue engineering. Molecular docking and pharmacokinetic investigations disclosed the potential antibacterial mechanisms of cinnamyl-gelatin derivatives alongside their biopharmaceutical applications. Altogether, these findings suggest that cinnamyl-gelatin derivatives could be utilized to tailor antibacterial-free antibiotics and antioxidant wound dressings against virulent bacteria to promote chronic wound recovery.

Identifying potential monkeypox virus inhibitors: an in silico study targeting the A42R protein

Monkeypox (now Mpox), a zoonotic disease caused by the monkeypox virus (MPXV) is an emerging threat to global health. In the time span of only six months, from May to October 2022, the number of MPXV cases breached 80,000 and many of the outbreaks occurred in locations that had never previously reported MPXV. Currently there are no FDA-approved MPXV-specific vaccines or treatments, therefore, finding drugs to combat MPXV is of utmost importance. The A42R profilin-like protein of the MPXV is involved in cell development and motility making it a critical drug target. A42R protein is highly conserved across orthopoxviruses, thus A42R inhibitors may work for other family members. This study sought to identify potential A42R inhibitors for MPXV treatment using computational approaches. The energy minimized 3D structure of the A42R profilin-like protein (PDB ID: 4QWO) underwent virtual screening using a library of 36,366 compounds from Traditional Chinese Medicine (TCM), AfroDb, and PubChem databases as well as known inhibitor tecovirimat via AutoDock Vina. A total of seven compounds comprising PubChem CID: 11371962, ZINC000000899909, ZINC000001632866, ZINC000015151344, ZINC000013378519, ZINC000000086470, and ZINC000095486204, predicted to have favorable binding were shortlisted. Molecular docking suggested that all seven proposed compounds have higher binding affinities to A42R (-7.2 to -8.3 kcal/mol) than tecovirimat (-6.7 kcal/mol). This was corroborated by MM/PBSA calculations, with tecovirimat demonstrating the highest binding free energy of -68.694 kJ/mol (lowest binding affinity) compared to the seven shortlisted compounds that ranged from -73.252 to -97.140 kJ/mol. Furthermore, the 7 compounds in complex with A42R demonstrated higher stability than the A42R-tecovirimat complex when subjected to 100 ns molecular dynamics simulations. The protein-ligand interaction maps generated using LigPlot+ suggested that residues Met1, Glu3, Trp4, Ile7, Arg127, Val128, Thr131, and Asn133 are important for binding. These seven compounds were adequately profiled to be potential antivirals via PASS predictions and structural similarity searches. All seven potential lead compounds were scored Pa > Pi for antiviral activity while ZINC000001632866 and ZINC000015151344 were predicted as poxvirus inhibitors with Pa values of 0.315 and 0.215, and Pi values of 0.052 and 0.136, respectively. Further experimental validations of the identified lead compounds are required to corroborate their predicted activity. These seven identified compounds represent solid footing for development of antivirals against MPXV and other orthopoxviruses.

Special Issue "Third Edition: Advances in Molecular Simulation"

Molecular simulation is one of the fastest growing fields in science [...].

Special Issue "Computer-Aided Drug Discovery and Treatment"

This Special Issue aims to highlight some of the latest developments in drug discovery [...].

Real-World Molecular Out-Of-Distribution: Specification and Investigation

This study presents a rigorous framework for investigating molecular out-of-distribution (MOOD) generalization in drug discovery. The concept of MOOD is first clarified through a problem specification that demonstrates how the covariate shifts encountered during real-world deployment can be characterized by the distribution of sample distances to the training set. We find that these shifts can cause performance to drop by up to 60% and uncertainty calibration by up to 40%. This leads us to propose a splitting protocol that aims to close the gap between the deployment and testing. Then, using this protocol, a thorough investigation is conducted to assess the impact of model design, model selection, and data set characteristics on MOOD performance and uncertainty calibration. We find that appropriate representations and algorithms with built-in uncertainty estimation are crucial to improving performance and uncertainty calibration. This study sets itself apart by its exhaustiveness and opens an exciting avenue to benchmark meaningful algorithmic progress in molecular scoring.

In Silico Investigation of Novel Compounds as Inhibitors of Acetylcholinesterase Enzyme for the Treatment of Alzheimer's Diseases

Alzheimer's disease (AD) is a "progressive, neurodegenerative disease that occurs when nerve cells in the brain die." There are only 4 drugs approved by the United States Food and Drug Administration (FDA). Three (donepezil, rivastigmine, and galantamine) out of these four drugs are anticholinesterase inhibitors, while the fourth one memantine is an N-methyl-D-aspartate (NMDA) receptor inhibitor. Currently, two immunotherapy drugs that target amyloid protein (donanemab and lecanemab) are being considered for the treatment of Alzheimer's disease at an early stage. All these drug molecules are still not the complete answer to the treatment of Alzheimer's disease. A recent report from the Office of National Statistics showed that AD is the leading cause of death in 2022. Therefore, there is an urgency to develop more drugs that can treat AD. Based on this urgency, we aim to investigate how bioactive and already approved drugs could be repurposed for inhibiting the anticholinesterase enzyme using computational studies. To achieve this, the data science tool-Python coding was compiled on Jupyter Notebook to mine bioactive compounds from the ChEMBL database. The most bioactive compounds obtained were further investigated using Molecular Operating Environment (MOE) software to carry out molecular docking and ligand analysis, and this was followed by molecular dynamics simulation production at 35 ns using GROMACS 2022.4 on Archer 2 machine. The molecular dynamic analysis was carried out using HeroMDanalysis software. Data mining of the ChEMBL database was carried out for lipase inhibitors, and this gave CHEMBL-ID 1240685, a peptide molecule, the most active compound at the time of data mining. Further literature studies gave Zoladex an FDA-approved drug for the treatment of breast cancer as another compound of interest. The in silico studies were carried out against the anticholinesterase enzyme using two FDA-approved drugs donepezil and galantamine as a template for comparing the in silico activities of the repurposed drugs. A very useful receptor for this study was PDB-1DX6, a cocrystallized galantamine inhibitor of acetylcholinesterase enzyme. The molecular docking analysis (using ligand interactions) and molecular dynamic analysis (root mean square deviation (RMSD) and root mean square fluctuation (RMSF)) showed that the two peptide molecules CHEMBL-1240685 and Zoladex gave the best binding energy and stability when compared to the FDA-approved drugs (donepezil and galantamine). Finally, further literature studies revealed that Zoladex affects memory reduction; therefore, it was dropped as a possible repurposed drug. Our research showed that CHEMBL-1240685 is a potential compound that could be investigated for the inhibition of anticholinesterase enzyme and might be another drug molecule that could be used to treat Alzheimer's disease.

Inhibitor design for TMPRSS2: insights from computational analysis of its backbone hydrogen bonds using a simple descriptor

Transmembrane protease serine 2 (TMPRSS2) is an important drug target due to its role in the infection mechanism of coronaviruses including SARS-CoV-2. Current understanding regarding the molecular mechanisms of known inhibitors and insights required for inhibitor design are limited. This study investigates the effect of inhibitor binding on the intramolecular backbone hydrogen bonds (BHBs) of TMPRSS2 using the concept of hydrogen bond wrapping, which is the phenomenon of stabilization of a hydrogen bond in a solvent environment as a result of being surrounded by non-polar groups. A molecular descriptor which quantifies the extent of wrapping around BHBs is introduced for this. First, virtual screening for TMPRSS2 inhibitors is performed by molecular docking using the program DOCK 6 with a Generalized Born surface area (GBSA) scoring function. The docking results are then analyzed using this descriptor and its relationship to the solvent-accessible surface area term ΔGsa of the GBSA score is demonstrated with machine learning regression and principal component analysis. The effect of binding of the inhibitors camostat, nafamostat, and 4-guanidinobenzoic acid (GBA) on the wrapping of important BHBs in TMPRSS2 is also studied using molecular dynamics. For BHBs with a large increase in wrapping groups due to these inhibitors, the radial distribution function of water revealed that certain residues involved in these BHBs, like Gln438, Asp440, and Ser441, undergo preferential desolvation. The findings offer valuable insights into the mechanisms of these inhibitors and may prove useful in the design of new inhibitors.

Sulfadiazine Exerts Potential Anticancer Effect in HepG2 and MCF7 Cells by Inhibiting TNFα, IL1b, COX-1, COX-2, 5-LOX Gene Expression: Evidence from In Vitro and Computational Studies

Drug repurposing is a promising approach that has the potential to revolutionize the drug discovery and development process. By leveraging existing drugs, we can bring new treatments to patients more quickly and affordably. Anti-inflammatory drugs have been shown to target multiple pathways involved in cancer development and progression. This suggests that they may be more effective in treating cancer than drugs that target a single pathway. Cell viability was measured using the MTT assay. The expression of genes related to inflammation (TNFa, IL1b, COX-1, COX-2, and 5-LOX) was measured in HepG2, MCF7, and THLE-2 cells using qPCR. The levels of TNFα, IL1b, COX-1, COX-2, and 5-LOX were also measured in these cells using an ELISA kit. An enzyme binding assay revealed that sulfadiazine expressed weaker inhibitory activity against COX-2 (IC50 = 5.27 μM) in comparison with the COX-2 selective reference inhibitor celecoxib (COX-2 IC50 = 1.94 μM). However, a more balanced inhibitory effect was revealed for sulfadiazine against the COX/LOX pathway with greater affinity towards 5-LOX (IC50 = 19.1 μM) versus COX-1 (IC50 = 18.4 μM) as compared to celecoxib (5-LOX IC50 = 16.7 μM, and COX-1 IC50 = 5.9 μM). MTT assays revealed the IC50 values of 245.69 ± 4.1 µM and 215.68 ± 3.8 µM on HepG2 and MCF7 cell lines, respectively, compared to the standard drug cisplatin (66.92 ± 1.8 µM and 46.83 ± 1.3 µM, respectively). The anti-inflammatory effect of sulfadiazine was also depicted through its effect on the levels of inflammatory markers and inflammation-related genes (TNFα, IL1b, COX-1, COX-2, 5-LOX). Molecular simulation studies revealed key binding interactions that explain the difference in the activity profiles of sulfadiazine compared to celecoxib. The results suggest that sulfadiazine exhibited balanced inhibitory activity against the 5-LOX/COX-1 enzymes compared to the selective COX-2 inhibitor, celecoxib. These findings highlight the potential of sulfadiazine as a potential anticancer agent through balanced inhibitory activity against the COX/LOX pathway and reduction in the expression of inflammatory genes.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Icariin prevents methylmercury-induced experimental neurotoxicity: Evidence from cerebrospinal fluid, blood plasma, brain samples, and in-silico investigations

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that causes significant neurodegeneration. Methylmercury (MeHg+) is a neurotoxin that induces axonal neurodegeneration and motor nerve degeneration by destroying oligodendrocytes, degenerating white matter, inducing apoptosis, excitotoxicity, and reducing myelin basic protein (MBP). This study examines the inhibition of SIRT-1 (silence information regulator 1), Nrf-2 (nuclear factor E2-related factor 2), HO-1 (heme oxygenase 1), and TDP-43 (TAR-DNA-binding protein 43) accumulation in the context of ALS, as well as the modulation of these proteins by icariin (15 and 30 mg/kg, orally), a glycoside flavonoid with neuroprotective properties. Neuroprotective icariin activates SIRT-1, Nrf-2, and HO-1, mitigating inflammation and neuronal injury in neurodegenerative disorders. In-vivo and in-silico testing of experimental ALS models confirmed icariin efficacy in modulating these cellular targets. The addition of sirtinol 10 mg/kg, an inhibitor of SIRT-1, helps determine the effectiveness of icariin. In this study, we also examined neurobehavioral, neurochemical, histopathological, and LFB (Luxol fast blue) markers in various biological samples, including Cerebrospinal fluid (CSF), blood plasma, and brain homogenates (Cerebral Cortex, Hippocampus, Striatum, mid-brain, and Cerebellum). These results demonstrate that the administration of icariin ameliorates experimental ALS and that the mechanism underlying these benefits is likely related to regulating the SIRT-1, Nrf-2, and HO-1 signaling pathways.

Identifying novel therapeutic inhibitors to target FMS-like tyrosine kinase-3 (FLT3) against acute myeloid leukemia: a molecular docking, molecular dynamics, and DFT study

Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to -11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended.Communicated by Ramaswamy H. Sarma.

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Natural Compounds as Protease Inhibitors in Therapeutic Focus on Cancer Therapy

Proteases are implicated in every hallmark of cancer and have complicated functions. For cancer cells to survive and thrive, the process of controlling intracellular proteins to keep the balance of the cell proteome is essential. Numerous natural compounds have been used as ligands/ small molecules to target various proteases that are found in the lysosomes, mitochondria, cytoplasm, and extracellular matrix, as possible anticancer therapeutics. Promising protease modulators have been developed for new drug discovery technology through recent breakthroughs in structural and chemical biology. The protein structure, function of significant tumor-related proteases, and their natural compound inhibitors have been briefly included in this study. This review highlights the most current frontiers and future perspectives for novel therapeutic approaches associated with the list of anticancer natural compounds targeting protease and the mode and mechanism of proteinase-mediated molecular pathways in cancer.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment.Communicated by Ramaswamy H. Sarma.

Developing a new multi-featured chitosan-quinoline Schiff base with potent antibacterial, antioxidant, and antidiabetic activities: design and molecular modeling simulation

A new chitosan Schiff base was developed via the reaction of chitosan (CH) with 2-chloro-3-formyl-7-ethoxy quinoline (Q) derivative. The alteration in the chemical structure and morphology of CHQ derivative was confirmed by 1H NMR, FT-IR spectroscopy and SEM analysis. The antibacterial activity was considerably promoted with increasing quinoline concentration up to 1 M with maximal inhibition reached 96 and 77% against Staphylococcus haemolyticus and Escherichia coli, respectively. Additionally, CHQ derivative afforded higher ABTS·+ radical scavenging activity reached 59% compared to 13% for native chitosan, approving its acceptable antioxidant activity. Moreover, the developed CHQ derivative can stimulate the glucose uptake in HepG-2 and yeast cells, while better inhibition of α-amylase and α-glucosidase was accomplished with maximum values of 99.78 and 92.10%, respectively. Furthermore, the molecular docking simulation clarified the binding mode of CHQ derivative inside the active site of α-amylase and α-glucosidase, suggesting its potential use as diabetes mellitus drug. The DFT calculations indicated an improvement in the electronic properties of CHQ with a lower energy band gap reached 4.05eV compared to 5.94eV for CH. The cytotoxicity assay revealed the safety of CHQ towards normal HSF cells, hypothesizing its possible application as non-toxic antibacterial, antioxidant, and antidiabetic agent for biomedical applications.

Predicting Plasmodium falciparum kinase inhibitors from antimalarial medicinal herbs using computational modeling approach

Malaria remains a significant public health challenge, with resistance to available drugs necessitating the development of novel therapies targeting invasion-dependent proteins. Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK-1) is essential for host erythrocyte invasion and parasite asexual development. This study screened a library of 490 compounds using computational methods to identify potential PfCDPK-1 inhibitors. Three compounds; 17-hydroxyazadiradione, Picracin, and Epicatechin-gallate derived from known antimalarial botanicals, showed potent inhibitory effects on PfCDPK-1. These compounds exhibited better binding affinities (-8.8, -9.1, -9.3 kCal/mol respectively), pharmacokinetics, and physicochemical properties than the purported inhibitory standard of PfCDPK-1, Purfalcamine. Molecular dynamics simulations (50 ns) and molecular mechanics analyses confirmed the stability and binding rigidity of these compounds at the active pocket of PfCDPK-1. The results suggest that these compounds are promising pharmacological targets with potential therapeutic effects for malaria treatment/management without undesirable side effects. Therefore, this study provides new insights into the development of effective antimalarial agents targeting invasion-dependent proteins, which could help combat the global malaria burden.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00175-z.

Unlocking therapeutic potential of trigonelline through molecular docking as a promising approach for treating diverse neurological disorders

Neurological disorders pose significant challenges in terms of treatment options, necessitating the exploration of novel therapeutic approaches. Trigonelline, a naturally occurring alkaloid found in various plants, has emerged as a potential treatment option. It has also been reported that trigonelline is involved in several pathways like; Oxidative Stress and Antioxidant, Inflammatory, Neuroprotection and Neurotrophic, Mitochondrial Function and Energy Metabolism. This study aims to investigate the therapeutic potential of trigonelline for diverse neurological disorders using a molecular docking approach. Molecular docking simulations were performed to predict the binding affinity and interaction between trigonelline and target proteins implicated in neurological disorders. The structural requirements for effective binding were also explored. The molecular docking results revealed strong binding interactions and favorable binding affinities between trigonelline and the target proteins involved in diverse neurological disorders like Alzheimer's disease, Parkinson's disease, epilepsy, and depression etc. The predicted binding modes provided insights into the key molecular interactions governing the ligand-protein complexes. The findings suggest that trigonelline holds promise as a therapeutic approach for several neurological disorders. The molecular docking approach employed in this study provides a valuable tool for rational drug design and optimization of trigonelline-based compounds. Further experimental validation and preclinical studies are warranted to confirm the efficacy and safety of trigonelline as a potential treatment option, paving the way for the development of more effective and targeted therapies for neurological disorders.

In silico description of the adsorption of cell signaling pathway proteins ovalbumin, glutathione, LC3, TLR4, ASC PYCARD, PI3K and NF-Kβ on 7.0 nm gold nanoparticles: obtaining their Lennard-Jones-like potentials through docking and molecular mechanics

The impact of vaccination on the world's population is difficult to calculate. For developing different types of vaccines, adjuvants are substances added to vaccines to increase the magnitude and durability of the immune response and the effectiveness of the vaccine. This work explores the potential use of spherical gold nanoparticles (AuNPs) as adjuvants. Thus, we employed docking techniques and molecular mechanics to describe how a AuNP 7.0 nm in diameter interacts with cell signaling pathway proteins. Initially, we used X-ray crystallization data of the proteins ovalbumin, glutathione, LC3, TLR4, ASC PYCARD, PI3K, and NF-Kβ to study the adsorption with an AuNP through molecular docking. Therefore, interaction energies were obtained for the AuNP complexes and individual proteins, as well as the AuNP and OVA complex (AuNP@OVA) with each cellular protein, respectively. Results showed that AuNPs had the highest affinity for OVA individually, followed by glutathione, ASC PYCARD domain, LC3, PI3K, NF-Kβ, and TLR4. Furthermore, when evaluating the AuNP@OVA complex, glutathione showed a greater affinity with more potent interaction energy when compared to the other studied systems.

Synthesis, characterization, DNA binding interactions, DFT calculations, and Covid-19 molecular docking of novel bioactive copper(I) complexes developed via unexpected reduction of azo-hydrazo ligands

In this work, we focused on the 3rd goal of the sustainable development plan: achieving good health and supporting well-being. Two redox-active hydrazo ligands namely, phenylcarbonohydrazonoyldicyanide (PCHD) and pyridin-4-ylcarbonohydrazonoyl-dicyanide (PyCHD), and their copper(I) complexes have been synthesized and characterized. The analytical data indicates the formation of copper(I) complexes despite starting with copper(II) perchlorate salt. The 1H-NMR and UV-visible spectral studies in DMSO revealed that PyCHD mainly exists in its azo-form, while PCHD exists in azo ↔ hydrazo equilibrium form, and confirmed the copper(I) oxidation state. XPS, spectral and electrochemistry data indicated the existence of copper(I) valence of both complexes. Cyclic voltammetry of PCHD and its copper(I) complex supported the reduction power of the ligand. The antimicrobial activity, cytotoxicity against the mammalian breast carcinoma cell line (MCF7), and DNA interaction of the compounds are investigated. All compounds showed high antimicrobial, and cytotoxic activities, relative to the standard drugs. Upon studying the wheat DNA binding, PCHD and PyCHD were found to bind through external contacts, while both [Cu(PCHD)2]ClO4.H2O and [Cu(PyCHD)2]ClO4.H2O were intercalated binding. In-silico molecular docking simulations against Estrogen Receptor Alpha Ligand Binding Domain (ID: 6CBZ) were performed on all produced compounds and confirmed the invitro experimentally best anticancer activity of [Cu(PyCHD)2]ClO4.H2O. The molecular docking tests against SARS-CoV-2 main protease (ID: 6 WTT) showed promising activity in the order of total binding energy values: [Cu(PCHD)2]ClO4.H2O > [Cu(PyCHD)2]ClO4.H2O > PCHD > PyCHD.

Matrine mediated neuroprotective potential in experimental multiple sclerosis: Evidence from CSF, blood markers, brain samples and in-silico investigations

Multiple sclerosis (MS) is a debilitating, inflammatory, and demyelinating disease of the central nervous system influenced by environmental and genetic factors. Around 2.8 million people worldwide are affected by MS due to its challenging diagnosis and treatment. Our study investigates the role of the JAK/STAT and PPAR-gamma signaling pathways in the progression of multiple sclerosis. Inflammation and demyelination can be caused by dysregulation of these pathways. Modulating the STAT-3, mTOR, and PPAR-gamma signaling pathways may offer therapeutic potential for multiple sclerosis. Matrine (40 and 80 mg/kg, i.p.), a quinolizidine alkaloid derived from Sophora flavescens, has been investigated for its therapeutic potential in our laboratory. Matrine has been studied for its neuroprotective effect in neurodegenerative diseases. It inhibits inflammatory responses and promotes regeneration of damaged myelin sheaths, indicating its potential efficacy in treating multiple sclerosis. Matrine exerts its neuroprotective effect by inhibiting STAT-3 and mTOR and promoting PPAR-gamma expression.GW9662, a PPAR-gamma antagonist (2 mg/kg, i.p.), was administered to evaluate the involvement of PPAR-gamma and to compare the efficacy of matrine's potential neuroprotective effect. Matrine's interaction with the STAT-3, mTOR, and PPAR-gamma pathways in multiple Sclerosis was also validated and confirmed through insilico investigation. In addition, matrine altered the CBC profile, intensifying the clinical presentation of multiple sclerosis. In addition, we evaluated the diagnostic potential of various biological samples, including CSF, blood plasma, and brain homogenates (striatum, cortex, hippocampus, and midbrain). These samples were used to evaluate the neurochemical changes caused by neurobehavioral alterations during the progression of multiple sclerosis. These results indicate that matrine treatment ameliorated multiple sclerosis and that the mechanism underlying these effects may be closely related to the modulation of the STAT-3/mTOR/PPAR-gamma signaling pathway.

Cytotoxicity Enhancement of α-Mangostin with Folate-Conjugated Chitosan Nanoparticles in MCF-7 Breast Cancer Cells

α-mangostin (AM) is a promising natural anticancer agent that can be used in cancer research. However, its effectiveness can be limited by poor solubility and bioavailability. To address this issue, chitosan-based nanoparticles (CSNPs) have been investigated as a potential delivery system to enhance the cytotoxicity to cancer cells and improve selectivity against normal cells. In this study, we developed folate-conjugated chitosan nanoparticles (F-CS-NPs) using a carbodiimide-based conjugation method to attach folate to chitosan (CS), which have different molecular weights. The NPs were crosslinked using tripolyphosphate (TPP) via ionic gelation. To characterize the F-CS-NPs, we utilized various analytical techniques, including transmission electron microscopy (TEM) to evaluate the particle size and morphology, Fourier-transform infrared spectroscopy (FTIR) to confirm the presence of functional groups, and ultraviolet-visible spectroscopy (UV-Vis) to measure the absorption spectrum and confirm the presence of folate. The particle size of AM-F-CS-NPs ranged from 180 nm to 250 nm, with many having favorable charges ranging from +40.33 ± 3.4 to 10.69 ± 1.3 mV. All NPs exhibited the same spherical morphology. The use of F-CS-NPs increased drug release, followed by a sustained release pattern. We evaluated the cytotoxicity of AM, AM-F-CS-HMW, and AM-F-CS-LMW NPs against MCF-7 cells and found IC50 values of 8.47 ± 0.49, 5.3 ± 0.01, and 4.70 ± 0.11 µg/mL, respectively. These results confirm the improved cytotoxicity of AM in MCF-7 cells when delivered via F-CS-NPs. Overall, our in vitro study demonstrated that the properties of F-CS-NPs greatly influence the cytotoxicity of AM in MCF-7 breast cancer cells (significantly different (p < 0.05)). The use of F-CS-NPs as a drug-delivery system for AM may have the potential to develop novel therapies for breast cancer.

Synthesis of Pyrrolo[3,4-b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma

An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.

Nuclear receptor superfamily structural diversity in pacific oyster: In silico identification of estradiol binding candidates

The increasing presence of anthropogenic contaminants in aquatic environments poses challenges for species inhabiting contaminated sites. Due to their structural binding characteristics to ligands that inhibit or activate gene transcription, these xenobiotic compounds frequently target the nuclear receptor superfamily. The present work aims to understand the potential interaction between the hormone 17-β-estradiol, an environmental contaminant, and the nuclear receptors of Crassostrea gigas, the Pacific oyster. This filter-feeding, sessile oyster species is subject to environmental changes and exposure to contaminants. In the Pacific oyster, the estrogen-binding nuclear receptor is not able to bind this hormone as it does in vertebrates. However, another receptor may exhibit responsiveness to estrogen-like molecules and derivatives. We employed high-performance in silico methodologies, including three-dimensional modeling, molecular docking and atomistic molecular dynamics to identify likely binding candidates with the target moecule. Our approach revealed that among the C. gigas nuclear receptor superfamily, candidates with the most favorable interaction with the molecule of interest belonged to the NR1D, NR1H, NR1P, NR2E, NHR42, and NR0B groups. Interestingly, NR1H and NR0B were associated with planktonic/larval life cycle stages, while NR1P, NR2E, and NR0B were associated with sessile/adult life stages. The application of this computational methodological strategy demonstrated high performance in the virtual screening of candidates for binding with the target xenobiotic molecule and can be employed in other studies in the field of ecotoxicology in non-model organisms.

Structure-based computational design of novel covalent binders for the treatment of sickle cell disease

The quest in finding an everlasting panacea to the pernicious impact of sickle cell disease (SCD) in the society hit a turn of success since the recent discovery of a small molecule reversible covalent inhibitor, Voxelotor. A drug that primarily promotes the stability of oxygenated hemoglobin and inhibit the polymerization of HbS by enhancing hemoglobin's affinity for oxygen has opened a new frontier in drug discovery and development. Despite eminent efforts made to reproduce small molecules with better therapeutic targets, none has been successful. To this end, we employed the use of structure-based computational techniques with emphasis on the electrophilic warhead group of Voxelotor to harness novel covalent binders that could elicit better therapeutic response against HbS. The PubChem database and DataWarrior software were used to design random molecules using Voxelotor's electrophilic functionality. Following the compilation of these chemical entities, a high-throughput covalent docking-based virtual screening campaign was conducted which revealed three (Compound_166, Compound_2301, and Compound_2335) putative druglike candidates with higher baseline energy value compared to the standard drug. Subsequently, in silico ADMET profiling was carried out to evaluate their pharmacokinetics and pharmacodynamics properties, and their stability was evaluated for 1 μs (1 μs) using molecular dynamics simulation. Finally, to prioritize these compounds for further development in drug discovery, MM/PBSA calculations was employed to evaluate their molecular interactions and solvation energy within the HbS protein. Despite the admirable druglike and stability properties of these compounds, further experimental validations are required to establish their preclinical relevance for drug development.

Collaborative analysis for drug discovery by federated learning on non-IID data

With the increasing availability of large-scale QSAR (Quantitative Structure-Activity Relationship) datasets, collaborative analysis has become a promising approach for drug discovery. Traditional centralized analysis which typically concentrates data on a central server for training faces challenges such as data privacy and security. Distributed analysis such as federated learning offers a solution by enabling collaborative model training without sharing raw data. However, it may fail when the training data in the local devices are non-independent and identically distributed (non-IID). In this paper, we propose a novel framework for collaborative drug discovery using federated learning on non-IID datasets. We address the difficulty of training on non-IID data by globally sharing a small subset of data among all institutions. Our framework allows multiple institutions to jointly train a robust predictive model while preserving the privacy of their individual data. We leverage the federated learning paradigm to distribute the model training process across local devices, eliminating the need for data exchange. The experimental results on 15 benchmark datasets demonstrate that the proposed method achieves competitive predictive accuracy to centralized analysis while respecting data privacy. Moreover, our framework offers benefits such as reduced data transmission and enhanced scalability, making it suitable for large-scale collaborative drug discovery efforts.

Prediction of Phytochemicals for Their Potential to Inhibit New Delhi Metallo β-Lactamase (NDM-1)

The effectiveness of all antibiotics in the β-lactam group to cure bacterial infections has been impaired by the introduction of the New Delhi Metallo-β-lactamase (NDM-1) enzyme. Attempts have been made to discover a potent chemical as an inhibitor to this enzyme in order to restore the efficacy of antibiotics. However, it has been a challenging task to develop broad-spectrum inhibitors of metallo-β-lactamases. Lack of sequence homology across metallo-β-lactamases (MBLs), the rapidly evolving active site of the enzyme, and structural similarities between human enzymes and metallo-β-lactamases, are the primary causes for the difficulty in the development of these inhibitors. Therefore, it is imperative to concentrate on the discovery of an effective NDM-1 inhibitor. This study used various in silico approaches, including molecular docking and molecular dynamics simulations, to investigate the potential of phytochemicals to inhibit the NDM-1 enzyme. For this purpose, a library of about 59,000 phytochemicals was created from the literature and other databases, including FoodB, IMPPAT, and Phenol-Explorer. A physiochemical and pharmacokinetics analysis was performed to determine possible toxicity and mutagenicity of the ligands. Following the virtual screening, phytochemicals were assessed for their binding with NDM-1using docking scores, RMSD values, and other critical parameters. The docking score was determined by selecting the best conformation of the protein-ligand complex. Three phytochemicals, i.e., butein (polyphenol), monodemethylcurcumin (polyphenol), and rosmarinic acid (polyphenol) were identified as result of pharmacokinetics and molecular docking studies. Furthermore, molecular dynamics simulations were performed to determine structural stabilities of the protein-ligand complexes. Monodemethylcurcumin, butein, and rosmarinic acid were identified as potential inhibitors of NDM-1 based on their low RMSD, RMSF, hydrogen bond count, average Coulomb-Schrödinger interaction energy, and Lennard-Jones-Schrödinger interaction energy. The present investigation suggested that these phytochemicals might be promising candidates for future NDM-1 medication development to respond to antibiotic resistance.

Antioxidant, LC-MS Analysis, and Cholinesterase Inhibitory Potentials of Phoenix dactylifera Cultivar Khudari: An In Vitro Enzyme Kinetics and In Silico Study

We evaluated the therapeutic potentials of Khudari fruit pulp, a functional food and cultivar of Phoenix dactylifera, against neurological disorders. Our results demonstrate a good amount of phytochemicals (total phenolic content: 17.77 ± 8.21 µg GA/mg extract) with a high antioxidant potential of aqueous extract (DPPH assay IC50 = 235.84 ± 11.65 µg/mL) and FRAP value: 331.81 ± 4.56 µmol. Furthermore, the aqueous extract showed the marked inhibition of cell-free acetylcholinesterase (electric eel) with an IC50 value of 48.25 ± 2.04 µg/mL, and an enzyme inhibition kinetics study revealed that it exhibits mixed inhibition. Thereafter, we listed the 18 best-matched phytochemical compounds present in aqueous extract through LC/MS analysis. The computational study revealed that five out of eighteen predicted compounds can cross the BBB and exert considerable aqueous solubility. where 2-{5-[(1E)-3-methylbuta-1,3-dien-1-yl]-1H-indol-3-yl}ethanol (MDIE) indicates an acceptable LD50. value. A molecular docking study exhibited that the compounds occupied the key residues of acetylcholinesterase with ΔG range between -6.91 and -9.49 kcal/mol, where MDIE has ∆G: -8.67 kcal/mol, which was better than that of tacrine, ∆G: -8.25 kcal/mol. Molecular dynamics analyses of 100 ns supported the stability of the protein-ligand complexes analyzed through RMSD, RMSF, Rg, and SASA parameters. TRP_84 and GLY_442 are the most critical hydrophobic contacts for the complex, although GLU_199 is important for H-bonds. Prime/MM-GBSA showed that the protein-ligand complex formed a stable confirmation. These findings suggest that the aqueous extract of Khudari fruit pulp has significant antioxidant and acetylcholinesterase inhibition potentials, and its compound, MDIE, forms stably with confirmation with the target protein, though this fruit of Khudari dates can be a better functional food for the treatment of Alzheimer's disease. Further investigations are needed to fully understand the therapeutic role of this plant-based compound via in vivo study.

Aminochalcones Attenuate Neuronal Cell Death under Oxidative Damage via Sirtuin 1 Activity

Encouraged by the lack of effective treatments and the dramatic growth in the global prevalence of neurodegenerative diseases along with various pharmacological properties of chalcone pharmacophores, this study focused on the development of aminochalcone-based compounds, organic molecules characterized by a chalcone backbone (consisting of two aromatic rings connected by a three-carbon α,β-unsaturated carbonyl system) with an amino group attached to one of the aromatic rings, as potential neuroprotective agents. Thus, the aminochalcone-based compounds in this study were designed by bearing a -OCH3 moiety at different positions on the ring and synthesized by the Claisen-Schmidt condensation. The compounds exhibited strong neuroprotective effects against hydrogen peroxide-induced neuronal death in the human neuroblastoma (SH-SY5Y) cell line (i.e., by improving cell survival, reducing reactive oxygen species production, maintaining mitochondrial function, and preventing cell membrane damage). The aminochalcone-based compounds showed mild toxicity toward a normal embryonic lung cell line (MRC-5) and a human neuroblastoma cell line, and were predicted to have preferable pharmacokinetic profiles with potential for oral administration. Molecular docking simulation indicated that the studied aminochalcones may act as competitive activators of the well-known protective protein, SIRT1, and provided beneficial knowledge regarding the essential key chemical moieties and interacting amino acid residues. Collectively, this work provides a series of four promising candidate agents that could be developed for neuroprotection.

Assessment of antidiabetic activity of three Phenylspirodrimanes from fungus Stachybotrys chartarum MUT 3308 by ADME, QSAR, molecular docking and molecular dynamics simulation studies against protein tyrosine phosphatase 1B (PTP1B)

Phenylspirodrimanes (PSD) are the sesquiterpene quinone type meroterpenoids found in nature. PSDs are found to exhibit inhibitory activity against immunocomplex diseases, and tyrosine kinase receptors. Three of the different PSDs C1, C2, and C3 that have been reported to be isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308 are selected for studying their possible inhibitory effect against type 2 diabetes mellitus. Mechanistically, blocking protein tyrosine phosphatase 1B (PTP1B) helps to reduce the insulin resistance induction caused by the high expression of PTP1B. The QSAR, ADME, toxicity (T) study was carried out to predict the pharmacokinetic properties and the biological activities of the PSDs. PASS prediction web tool was used to find and select the target proteins 1NNY, and 2HNP. According to the molecular docking simulations, C1 and C2 showed better binding affinity of -8.5 kcal/mol, and -8.1 kcal/mol respectively against 1NNY compared to the control ligand. RMSD, RMSF, Rg, and SASA analysis revealed that both C1, and C2 showed better stability, minor conformational changes, and minor fluctuation upon binding to PTP1B. Protein contact analysis was carried out to validate the residues that are in contact with the ligands according to molecular docking studies. Overall, C1, and C2 could be proposed as novel natural hits to be developed and small modifications of these PSDs could result in inducing the binding affinity significantly, although experimental validation is required for further evaluation of the work.Communicated by Ramaswamy H. Sarma.