Combining docking and molecular dynamic simulations in drug design

Isomer-sourced structure iteration methods for in silico development of inhibitors: Inducing GTP-bound NRAS-Q61 oncogenic mutations to an "off-like" state

The NRAS-mutant subset of melanoma represent some of the most aggressive and deadliest types associated with poor overall survival. Unfortunately, for more than 40 years, no therapeutic agent directly targeting NRAS mutations has been clinically approved. In this work, based on microsecond scale molecular dynamics simulations, the effect of Q61 mutations on NRAS conformational characteristics is revealed at the atomic level. The GTP-bound NRAS-Q61R and Q61K mutations show a specific targetable pocket between Switch-II and α-helix 3 whereas the NRAS-Q61L non-polar mutation category shows a different targetable pocket. Moreover, a new isomer-sourced structure iteration method has been developed for the in silico design of potential inhibitor prototypes for oncogenes. We show the possibility of a designed prototype HM-387 to target activated NRAS-Q61R and that it can gradually induce the transition from the activated NRAS-Q61R to an "off-like" state.

7-Amino-[1,2,4]triazolo[1,5-a][1,3,5]triazines as CK1δ inhibitors: Exploring substitutions at the 2 and 5-positions

CK1δ is a serine-threonine kinase involved in several pathological conditions including neuroinflammation and neurodegenerative disorders like Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis. Specifically, it seems that an inhibition of CK1δ could have a neuroprotective effect in these conditions. Here, a series of [1,2,4]triazolo[1,5-a][1,3,5]triazines were developed as ATP-competitive CK1δ inhibitors. Both positions 2 and 5 have been explored leading to a total of ten compounds exhibiting IC50s comprised between 29.1 µM and 2.08 µM. Three of the four most potent compounds (IC50 < 3 µM) bear a thiophene ring at the 2 position. All compounds have been submitted to computational studies that identified the chain composed of at least 2 atoms (e.g., nitrogen and carbon atoms) at the 5 position as crucial to determine a key bidentate hydrogen bond with Leu85 of CK1δ. Most potent compounds have been tested in vitro, resulting passively permeable to the blood-brain barrier and, safe and slight neuroprotective on a neuronal cell model. These results encourage to further structural optimize the series to obtain more potent CK1δ inhibitors as possible neuroprotective agents to be tested on models of the above-mentioned neurodegenerative diseases.

Mechanistic insights into antidiabetic potential of Ficus viren against multi organ specific diabetic targets: molecular docking, MDS, MM-GBSA analysis

Ficus viren has been traditionally used to treat diabetes, and its extract inhibits carbohydrate/lipid metabolism and possesses anti-hyperglycemic potential. However, there is conflicting investigation related to F. viren extract effect on carbohydrate metabolism. Thus, bioactive and mechanism behind its antidiabetic potential is still scanty. This study explored F. viren's anti-diabetic property by identifying potential phytoconstituents and mechanism. A sequential in-silico approach was used i.e., druglikeness, molecular docking, post-docking MM-GBSA, ADMET studies, molecular dynamic simulation (MDS), and post-MDS MM-GBSA. We screened ∼32 phytoconstituents and twelve potential organ-specific diabetic targets (O.S.D.Ts i.e., IR, DPP-4, ppar-γ, ppar-α, ppar-δ, GLP-1R, SIRT-1, AMPK, GSK-3β, RAGE, and AR). Drug likeness study identified 18 druggable candidates among 32 phytoconstituents. K3A, quercetin, scutellarein, sorbifolin, and vogeline J identified as potential ligands from druggable ligands, using IR as the standard target. Subsequently, potential ligands docked with remaining O.S.D.Ts. and data showed that K3A binds strongly with AMPK, ppar-δ, DPP-4, and GSK-3β, while scutellarein binds with AR and ppar-α. Sorbifolin, quercetin, and vogeline J binds with ppar-α, ppar-γ, and RAGE, respectively. Post-docking MM-GBSA data (∆GBind) also depicted potential ligand's strong binding affinities with their corresponding targets. Thereafter, simulation data revealed that only scutellarein and sorbifolin showed dynamic stability with their respective targets, i.e., AR/ppar-α and ppar-α, respectively. Interestingly, post-MDS MM-GBSA revealed that only scutellarein exhibited strong ∆GBind of -55.08 kcal/mol and -75.48 kcal/mol with AR and ppar-α, respectively. Though, collective computational analysis supports antidiabetic potential of F. viren through AR and ppar-α modulation by scutellarein.

Nanomaterial-based therapeutics for enhanced antifungal therapy

The application of nanotechnology in antifungal therapy is gaining increasing attention. Current antifungal drugs have significant limitations, such as severe side effects, low bioavailability, and the rapid development of resistance. Nanotechnology offers an innovative solution to address these issues. This review discusses three key strategies of nanotechnology to enhance antifungal efficacy. Firstly, nanomaterials can enhance their interaction with fungal cells via ingenious surface tailoring of nanomaterials. Effective adhesion of nanoparticles to fungal cells can be achieved by electrostatic interaction or specific targeting to the fungal cell wall and cell membrane. Secondly, stimuli-responsive nanomaterials are developed to realize smart release of drugs in the specific microenvironment of pathological tissues, such as the fungal biofilm microenvironment and inflammatory microenvironment. Thirdly, nanomaterials can be designed to cross different physiological barriers, effectively addressing challenges posed by skin, corneal, and blood-brain barriers. Additionally, some new nanomaterial-based strategies in treating fungal infections are discussed, including the development of fungal vaccines, modulation of macrophage activity, phage therapy, the application of high-throughput screening in drug discovery, and so on. Despite the challenges faced in applying nanotechnology to antifungal therapy, its significant potential and innovation open new possibilities for future clinical antifungal applications.

In Silico Design and In Vitro Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents

QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.

In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyses

Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.

How kelch domain-containing protein 3 distinguishes between the C-end degron of herpesviral protein UL49.5 and its mutants - Insights from molecular dynamics

The C-terminal residues of proteins can function as degrons recognized by ubiquitin ligases for proteasomal degradation. Kelch domain-containing protein 3 (KLHDC3) is a substrate receptor for E3 ubiquitin ligase (Cullin2-RING ligase) that targets the C-terminal degrons. UL49.5 is 96 amino-acid type 1 transmembrane protein from bovine herpesvirus 1. Herpesviruses have evolved highly effective strategies to evade the antiviral immune response. One of these strategies is inhibition of the antigen processing and presentation pathway by MHC I, thereby reducing the presentation of the antigenic peptides on the surface of the infected cell. Recently, it has been demonstrated that UL49.5 triggers TAP degradation via recruiting the E3 ubiquitin ligase to TAP. Moreover, the mutagenesis revealed that the mutations within the UL49.5 C-degron sequence (93RGRG96) affect binding of UL49.5 to KLHDC3. In this work the molecular dynamics of KLHDC3 in complexes with the C-terminal decapeptide of the herpesviral protein UL4.95 and its three mutants has been employed to provide a framework for understanding molecular recognition of UL49.5 by KLHDC3. The findings of this study give insights into the interactions of the various degrons with KLHDC3. During the molecular dynamics, an active RGKG mutant adopts a conformation similar to that of the wild type decapeptide, whereas the conformations of two inactive mutants, KGRG and RGRD are significantly different. Both R93K and G96D mutations impair the interactions of the C-terminal glycine with KLHDC3. The findings of this study expand the existing knowledge about the mechanism of protein recognition by Cullin2-RING ligases thus contributing to the design of antiviral and anticancer drugs that can selectively promote or inhibit degradation of the proteins of interest.

Drug-Target Interaction Prediction Based on an Interactive Inference Network

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Phytochemical on-line screening and in silico study of Helianthemum confertum: antioxidant activity, DFT, MD simulation, ADME/T analysis, and xanthine oxidase binding

Seven components from the methanol extract of the aerial part of the endemic species Helianthemum confertum were isolated and identified for the first time. Investigating this species and its separated components chemical make-up and radical scavenging capacity, was the main goal. Using an online HPLC-ABTS˙+ test, ORAC, and TEAC assays, the free radical scavenging capacity of the ethyl acetate extract was assessed. The fractionation of these extracts by CC, TLC, and reverse-phase HPLC was guided by the collected data, which was corroborated by TEAC and ORAC assays. Molecular docking studies, DFT at the B3LYP level, and an examination of the ADME/T predictions of all compounds helped to further clarify the phytochemicals' antioxidant potential. Isolation and identification of all components were confirmed through spectroscopy, which revealed a mixture (50-50%) of para-hydroxybenzoic acid 1 and methyl gallate 2, protocatechuic acid 3, astragalin 4, trans-tiliroside 5, cis-tiliroside 6, contaminated by trans-tiliroside and 3-oxo-α-ionol-β-d-glucopyranoside 7, as well as two new compounds for the genus Helianthemum (2 and 7). With a focus on compounds 1, 2, 3, and 4, the results clearly showed that the extract and the compounds tested from this species had a high antioxidant capacity. Within the xanthine oxidase enzyme's pocket, all of the components tested showed strong and stable binding. In light of these findings, the xanthine oxidase/methyl gallate 2 complex was simulated using the Desmond module of the Schrodinger suite molecular dynamics (MD) for 100 ns. Substantially stable receptor-ligand complexes were observed following 1 ns of MD simulation.

The Sources, Structures and Cytotoxicity of Animal-Derived Bisindole Compounds

Bisindole compounds constitute a significant class of natural compounds distinguished by their characteristic bisindole structure and renowned for their anticancer properties. Over the past four decades, researchers have isolated 229 animal-derived bisindole compounds (ADBCs) from various animals. These compounds demonstrate a wide range of pharmacological properties, including cytotoxicity, antibacterial, antifungal, antiviral, and other activities. Notably, among these activities, cytotoxicity emerges as the most prominent characteristic of ADBCs. This review also summarizes the structureactivity relationship (SAR) studies associated with the cytotoxicity of these compounds and explores the druggability of these compounds. In summary, our objective is to provide an overview of the research progress concerning ADBCs, with the aim of fostering their continued development and utilization.

SepM mutation in Streptococcus mutans clinical isolates and related function analysis

BACKGROUND

Streptococcus mutans (S. mutans) is an important pathogenic bacterium that causes dental caries, while Streptococcus gordonii (S. gordonii) is a non-cariogenic bacterium that inhibits the growth of S. mutans. The SepM protein can promote the inhibitory ability of S. mutans against S. gordonii by cleaving CSP-21 and activating the ComDE two-component system. This study was designed to explore sepM mutation in S. mutans clinical isolates and related function in the regulation of interactions with S. gordonii.

METHODS

The S. mutans clinical strains that can inhibit the growth of S. gordonii constitute the inhibitory group. 286 C-serotype S. mutans strains were categorized into S. gordonii inhibitory (n = 114) and non-inhibitory bacteria (n = 172). We detected sanger sequencing of sepM gene, the expression levels of related genes and proteins in clinical isolates, obtained prokaryotic expression and purification of mutated proteins, and analyzed the effect of the target mutations on the binding between SepM and CSP-21.

RESULTS

We found that C482T, G533A, and G661A missense mutations were presented at significantly higher frequency in the inhibitory group relative to the non-inhibitory group. There was no significant difference in the expression of the sepM gene between selected clinical isolates harboring the G533A mutation and the control group. The expression levels of SepM, phosphorylated ComD, and ComE in the mutation group were significantly higher than those in the control group. SepM_control and SepM_D221N (G661A at the gene level) were found to contain two residues close to the active center while SepM_G178D (G533A at the gene level) contained three residues close to the active center. At 25 °C and a pH of 5.5, SepM_D221N (G661A) exhibited higher affinity for CSP-21 (KD = 8.25 µM) than did the SepM control (KD = 33.1 µM), and at 25 °C and a pH of 7.5, SepM_G178D (G533A) exhibited higher affinity (KD = 3.02 µM) than the SepM control (KD = 15.9 µM). It means that it is pH dependent.

CONCLUSIONS

Our data suggest that increased cleavage of CSP-21 by the the mutant SepM may be a reason for the higher inhibitory effect of S. mutans on S. gordonii .

Exploring Novel Antimalarial Compounds Targeting Plasmodium falciparum Enoyl-ACP Reductase: Computational and Experimental Insights

Malaria, caused by Plasmodium protozoa with Plasmodium falciparum as the most virulent species, continues to pose significant health challenges. Despite the availability of effective antimalarial drugs, the emergence of resistance has heightened the urgency for developing novel therapeutic compounds. In this study, we investigated the enoyl-ACP reductase enzyme of P. falciparum (PfENR) as a promising target for antimalarial drug discovery. Through a comprehensive analysis, we conducted a comparative evaluation of two lead compounds, LD1 (CID: 44405336, lead compounds 1) and LD2 (CID: 72703246, lead compounds 2), obtained from the PubChem/NCBI ligand database, to serve as reference molecules in the identification of potential derivatives using virtual screening assays. Among the newly identified candidates, Ligand 1 (LG1) and Ligand 2 (LG2) exhibited intriguing characteristics and underwent further investigation through docking and molecular dynamics simulations. Ligand 1 (LG1) demonstrated interactions similar to LD1, including hydrogen bonding with Asp218, while Ligand 2 (LG2) displayed superior binding energy comparable to LD1 and LD2, despite lacking hydrogen bonding interactions observed in the control compounds triclosan and its derivative 7-(4-chloro-2-hydroxyphenoxy)-4-methyl-2H-chromen-2-one (CHJ). Following computational validation using the MM/GBSA method to estimate binding free energy, commercially acquired LG1 and LG2 ligands were subjected to in vitro testing. Inhibition assays were performed to evaluate their potential as PfENR inhibitors alongside triclosan as a control compound. LG1 exhibited no inhibitory effects, while LG2 demonstrated inhibitory effects like triclosan. In conclusion, this study contributes valuable insights into developing novel antimalarial drugs by identifying LG2 as a potential ligand and employing a comprehensive approach integrating computational and experimental methodologies.

Quercetin increases the antidepressant-like effects of sclareol and antagonizes diazepam in thiopental sodium-induced sleeping mice: A possible GABAergic transmission intervention

Quercetin is the most common polyphenolic flavonoid present in fruits and vegetables demonstrating versatile health-promoting effects. This study aimed to examine the effects of quercetin (QR) and sclareol (SCL) on the thiopental sodium (TS)-induced sleeping and forced swimming test (FST) mouse models. SCL (1, 5, and 10 mg/kg, p.o.) or QR (50 mg/kg, p.o.) and/or diazepam (DZP) (3 mg/kg, i.p.) were employed. After 30 min of TS induction, individual or combined effects on the animals were checked. In the FST test, the animals were subjected to forced swimming after 30 min of administration of the test and/or controls for 5 min. In this case, immobility time was measured. In silico studies were conducted to evaluate the involvement of GABA receptors. SCL (5 and 10 mg/kg) significantly increased the latency and decreased sleeping time compared to the control in the TS-induced sleeping time study. DZP (3 mg/kg) showed a sedative-like effect in animals in both sleeping and FST studies. QR (50 mg/kg) exhibited a similar pattern of activity as SCL. However, its effects were more prominent than those of SCL groups. SCL (10 mg/kg) altered the DZP-3-mediated effects. SCL-10 co-treated with QR-50 significantly (p < 0.05) increased the latency and decreased sleep time and immobility time, suggesting possible synergistic antidepressant-like effects. In silico studies revealed that SCL and QR demonstrated better binding affinities with GABAA receptor, especially α2, α3, and α5 subunits. Both compounds also exhibited good ADMET and drug-like properties. In animal studies, the both compounds worked synergistically to provide antidepressant-like effects in a slightly different fashion. As a conclusion, the combined administration of SCL and QR may be used in upcoming neurological clinical trials, according to in vivo and in silico findings. However, additional investigation is necessary to verify this behavior and clarify the potential mechanism of action.

Antioxidant and antibacterial activities of 5-mercapto(substitutedthio)-4-substituted-1,2,4-triazol based on nalidixic acid: A comprehensive study on its synthesis, characterization, and In silico evaluation

This study introduces a series of novel Alkyl thio-1,2,4-triazole (4a-p) and mercapto-1,2,4-triazole (3a-d) compounds derived from nalidixic acid. The synthesis was streamlined, involving interactions between nalidixic acid hydrazide and various isothiocyanates to yield cyclic and alkyl(aryl) sulfide compounds, characterized using 1H NMR, 13C NMR, IR, and elemental analysis. Antioxidant capabilities were quantified through DPPH and ABTS assays, highlighting significant potential, especially for compound 3d, which demonstrated an ABTS IC50 value of 0.397 μM, on par with ascorbic acid (IC50 = 0.87 μM). Antibacterial efficacy was established through MIC assessments against a broad spectrum of Gram-positive and Gram-negative bacteria, including Candida albicans. Compounds 3b, 4e, 4h, 4j, 4i, 4m, and 4o showed broad-spectrum activity, with 4k and 4m exhibiting pronounced potency against E. coli. Molecular docking studies validated the antibacterial potential, with compounds 4f and 4h showing high binding affinities (docking scores of -9.8 and -9.6 kcal/mol, respectively), indicating robust interactions with the bacterial enzyme targets. These scores underscore the compounds' mechanistic basis for their antibacterial action and support their therapeutic promise. Furthermore, compounds 3b, 4i, and 4m, identified through drug-likeness and toxicity predictions, were highlighted for their favorable profiles, suggesting their suitability for oral antibiotic therapies. This comprehensive study, blending synthetic, in vitro, and in silico approaches, emphasizes the triazole derivatives' potential as future candidates for antibiotic and antioxidant applications, particularly spotlighting compounds 3b, 4i, and 4m due to their promising efficacy and safety profiles.

Gold(I) and Silver(I) Complexes Containing Hybrid Sulfonamide/Thiourea Ligands as Potential Leishmanicidal Agents

Leishmaniasis is a group of parasitic diseases with the potential to infect more than 1 billion people; however, its treatment is still old and inadequate. In order to contribute to changing this view, this work consisted of the development of complexes derived from MI metal ions with thioureas, aiming to obtain potential leishmanicidal agents. The thiourea ligands (HLR) were obtained by reactions of p-toluenesulfohydrazide with R-isothiocyanates and were used in complexation reactions with AgI and AuI, leading to the formation of complexes of composition [M(HLR)2]X (M = Ag or Au; X = NO3- or Cl-). All compounds were characterized by FTIR, 1H NMR, UV-vis, emission spectroscopy and elemental analysis. Some representatives were additionally studied by ESI-MS and single-crystal XRD. Their properties were further analyzed by DFT calculations. Their cytotoxicity on Vero cells and the extracellular leishmanicidal activity on Leishmania infantum and Leishmania braziliensis cells were evaluated. Additionally, the interaction of the complexes with the Old Yellow enzyme of the L. braziliensis (LbOYE) was examined. The biological tests showed that some compounds present remarkable leishmanicidal activity, even higher than that of the standard drug Glucantime, with different selectivity for the two species of Leishmania. Finally, the interaction studies with LbOYE revealed that this enzyme could be one of their biological targets.

Polymeric membrane potentiometric antibiotic sensors using computer-aided screening of supramolecular macrocyclic carriers

Carrier-based polymeric membrane potentiometric sensors are an ideal tool for detecting ionic species. However, in the fabrication of these sensors, the screening of carriers still relies on empirical trial- and error-based optimization, which requires tedious and time-consuming experimental verification. In this work, computer-aided screening of carriers is applied in the preparation of polymeric membrane potentiometric sensors. Molecular docking is used to study the host-guest interactions between receptors and targets. Binding energies are employed as the standard to screen the appropriate carrier. As a proof-of-concept experiment, the antibiotic ciprofloxacin is selected as the target model. A series of supramolecular macrocyclic receptors including cyclodextrins, cucurbiturils and calixarenes are chosen as potential receptors. The proposed sensor based on the receptor calix[4]arene screened by molecular docking shows a lower detection limit of 0.5 μmol L-1 for ciprofloxacin. It can be expected that the proposed computer-aided screening technique of carriers can provide a simple but highly efficient method for the fabrication of carrier-based electrochemical and optical sensors.

Machine learning-based virtual screening and molecular modelling studies for identification of butyrylcholinesterase inhibitors as anti-Alzheimer's agent

Butyrylcholinesterase (BChE) is a hydrolase involved in the metabolism and detoxification of specific esters in the blood. It is also implicated in the progression of Alzheimer's disease, a type of dementia. As the disease progresses, the level of BChE tends to increase, opting for a major role as an acetylcholine-degrading enzyme and surpassing the role of acetylcholinesterase. Hence, the development of BChE inhibitors could be beneficial for the latter stages of the disease. In the present study, machine learning (ML) models were developed and employed to identify new BChE inhibitors. Further, the identified molecules were subjected to molecular property filters. The filtered ligands were studied through molecular modelling techniques, viz. molecular docking and molecular dynamics (MD). Support vector machine-based ML models resulted in the identification of 3291 compounds that would have predicted IC50 values less than 200 nM. The docking study showed that compounds ART13069594, ART17350769 and LEG19710163 have mean binding energies of -9.62, -9.26 and -8.93 kcal/mol, respectively. The MD study displayed that all the selected ligands showed stable complexes with BChE. The trajectories of all the ligands were stable similar to the standard BChE inhibitors.Communicated by Ramaswamy H. Sarma.

Rapid discovery of a new antifoulant: From in silico studies targeting barnacle chitin synthase to efficacy against barnacle settlement

Due to the adverse environmental impacts of toxic heavy metal-based antifoulants, the screening of environmentally friendly antifoulants has become important for the development of marine antifouling technology. Compared with the traditional lengthy and costly screening method, computer-aided drug design (CADD) offers a promising and efficient solution that can accelerate the screening process of green antifoulants. In this study, we selected barnacle chitin synthase (CHS, an important enzyme for barnacle settlement and development) as the target protein for docking screening. Three CHS genes were identified in the barnacle Amphibalanus amphitrite, and their encoded proteins were found to share a conserved glycosyltransferase domain. Molecular docking of 31,561 marine natural products with AaCHSs revealed that zoanthamine alkaloids had the best binding affinity (-11.8 to -12.6 kcal/mol) to AaCHSs. Considering that the low abundance of zoanthamine alkaloids in marine organisms would limit their application as antifoulants, a marine fungal-derived natural product, mycoepoxydiene (MED), which has a similar chemical structure to zoanthamine alkaloids and the potential for large-scale production by fermentation, was selected and validated for stable binding to AaCHS2L2 using molecular docking and molecular dynamics simulations. Finally, the efficacy of MED in inhibiting cyprid settlement of A. amphitrite was confirmed by a bioassay that demonstrated an EC50 of 1.97 μg/mL, suggesting its potential as an antifoulant candidate. Our research confirmed the reliability of using AaCHSs as antifouling targets and has provided insights for the efficient discovery of green antifoulants by CADD.

Antifungal activity against Candida albicans of methyl 3,5-dinitrobenzoate loaded nanoemulsion

The objective of this study was to evaluate the antifungal activity of free methyl 3,5 dinitrobenzoate (MDNB) and its nanoemulsion (MDNB-NE) against strains of Candida albicans. Additionally, a molecular modeling study was also carried out to propose the mechanism of action and toxicity of MDNB. These results demonstrated the MDNB-NE presented a droplet size of 181.16 ± 3.20 nm and polydispersity index of 0.30 ± 0.03. MDNB and MDNB-NE inhibited the growth of all strains with minimum inhibitory concentrations of 0.27-1.10 mM. The biological results corroborated the molecular model, which pointed to a multi-target antifungal mechanism of action for MDNB in C. albicans. The study could serve as a basis for further research involving compounds with nitro groups with antifungal.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

Computational and physicochemical insight into 4-hydroxy-2-nonenal induced structural and functional perturbations in human low-density lipoprotein

Lipid peroxidation (LPO) is a biological process that frequently occurs under physiological conditions. Undue oxidative stress increases the level of LPO; which may further contribute to the development of cancer. 4-Hydroxy-2-nonenal (HNE), one of the principal by-products of LPO, is present in high concentrations in oxidatively stressed cells. HNE rapidly reacts with various biological components, including DNA and proteins; however, the extent of protein degradation by lipid electrophiles is not well understood. The influence of HNE on protein structures will likely have a considerable therapeutic value. This research elucidates the potential of HNE, one of the most researched phospholipid peroxidation products, in modifying low-density lipoprotein (LDL). In this study, we tracked the structural alterations in LDL by HNE using various physicochemical techniques. To comprehend the stability, binding mechanism and conformational dynamics of the HNE-LDL complex, computational investigations were carried out. LDL was altered in vitro by HNE, and the secondary and tertiary structural alterations were examined using spectroscopic methods, such as UV-visible, fluorescence, circular dichroism and fourier transform infrared spectroscopy. Carbonyl content, thiobarbituric acid-reactive-substance (TBARS) and nitroblue tetrazolium (NBT) reduction assays were used to examine changes in the oxidation status of LDL. Thioflavin T (ThT), 1-anilinonaphthalene-8-sulfonic (ANS) binding assay and electron microscopy were used to investigate aggregates formation. According to our research, LDL modified by HNE results in changes in structural dynamics, oxidative stress and the formation of LDL aggregates. The current investigation must characterize HNE's interactions with LDL and comprehend how it can change their physiological or pathological functions.Communicated by Ramaswamy H. Sarma.

Unveiling the molecular interaction of hepatitis B virus inhibitor, entecavir with human serum albumin through computational, microscopic and spectroscopic approaches

Molecular docking, molecular dynamics (MD) simulation, atomic force microscopy (AFM) and multi-spectroscopic techniques were selected to unveil the molecular association between the hepatitis B virus (HBV) inhibitor, entecavir (ETR), and the major blood plasma transporter, human serum albumin (HSA). The entire docking and simulation analyses recognized ETR binding to subdomain IIA (Site I) of HSA through hydrogen bonds, hydrophobic and van der Waals forces while maintaining the complex's stability throughout the 100 ns. A gradual lessening in the Stern-Volmer quenching constant (K sv ) with rising temperatures registered ETR-induced quenching of HBV fluorescence as static quenching, thus advising complexation between ETR and HSA. The further advocation of this conclusion was seen from a larger value of the biomolecular quenching rate constant ((kq ) > 1010 M-1s-1), changes in the spectra (UV-Vis absorption) of HSA following ETR inclusion and ETR-induced swelling of HSA in the AFM results. The ETR appeared to bind to HSA with moderate affinity (K a = 1.87 - 1.19 × 10 4   M-1) at 290, 300 and 310 K. Significant alterations in the protein's secondary and tertiary structures, including changes in the protein's Tyr/Trp microenvironment, were also detected by circular dichroism and three-dimensional fluorescence spectra when the protein was bound to ETR. The findings of the drug displacement study backed the docking results of Site I as ETR's preferred binding site in HSA.Communicated by Ramaswamy H. Sarma.

Computational Approach to Elucidating Insulin-Protamine Binding Interactions and Dynamics in Insulin NPH Formulations

Insulin NPH is an intermediate-acting insulin. Its protracted action profile is due to the formation of microcrystalline suspensions when insulin is complexed with a basic peptide protamine, zinc ion, and phenolic ligands. Despite advancements in analytical techniques, the binding epitope and binding mode of the protamine in the insulin-protamine complex are still unknown. In this study, we used bioinformatics tools such as molecular docking and molecular dynamics (MD) simulations to compute the binding sites and energetics of the insulin-protamine complex. We have taken four naturally occurring protamine peptides that are independently docked with the insulin R6 hexamer and subjected them to 200 ns MD simulations to observe the dynamics of the complexes and estimate the binding energies. The arginine-rich protamine peptides were found to bind on the surface of the insulin hexamer through hydrogen bonding, hydrophobic, and electrostatic interactions well supported by the calculated negative binding energies. The overall structure of the insulin hexamer was retained upon binding, highlighting its dynamic stability in the complex. Furthermore, the residues at the termini of the protamine peptides in the complex were seen to be highly dynamic, which stabilize toward the end of the simulation.

Computer aided aptamer selection for fabrication of electrochemical sensor to detect Aflatoxin B1

Aflatoxin B1 (AFB1) is a naturally occurring toxin produced by Aspergillus flavus and Aspergillus parasiticus. The AFB1 is classified as a potent carcinogen and poses significant health risks both to humans and animals. Early detection of the toxin in post-harvest agricultural products will save lives and promote healthy food production. In this study, stratified docking approach was utilized to screen and identify potential aptamers that can bind to AFB1. ssDNA sequences were acquired from the Mendeley dataset, secondary and tertiary structures were predicted through a series of bioinformatics pipelines. Further, the final DNA tertiary structures were minimized and SiteMap algorithm was used to probe and locate binding cavities. According to the final XP docking result, a 34 nt sequence (5'-ATCCTGTGAGGAATGCTCATGCATAGCAAGGGCT-3') aptamer with a docking score of -5.959 kcal/mol was considered for 200 ns MD Simulation. Finally, the screened DNA-aptamer was immobilized over the gold surface based on Au-S chemistry and utilized for the detection of AFB1. The fabricated DNA-aptamer electrode demonstrated a good analytical performance including wide linear range (1.0 to 1000 ng L-1), detection limit (1.0 ng L-1), high stability, and reproducibility.Communicated by Ramaswamy H. Sarma.

Trapping a non-cognate nucleotide upon initial binding for replication fidelity control in SARS-CoV-2 RNA dependent RNA polymerase

The RNA dependent RNA polymerase (RdRp) in SARS-CoV-2 is a highly conserved enzyme responsible for viral genome replication/transcription. To understand how the viral RdRp achieves fidelity control during such processes, here we computationally investigate the natural non-cognate vs. cognate nucleotide addition and selectivity during viral RdRp elongation. We focus on the nucleotide substrate initial binding (RdRp active site open) to the prechemical insertion (active site closed) of the RdRp. The current studies were first carried out using microsecond ensemble equilibrium all-atom molecular dynamics (MD) simulations. Due to the slow conformational changes (from open to closed) during nucleotide insertion and selection, enhanced or umbrella sampling methods have been further employed to calculate the free energy profiles of the nucleotide insertion. Our studies find notable stability of noncognate dATP and GTP upon initial binding in the active-site open state. The results indicate that while natural cognate ATP and Remdesivir drug analogue (RDV-TP) are biased toward stabilization in the closed state to facilitate insertion, the natural non-cognate dATP and GTP can be well trapped in off-path initial binding configurations and prevented from insertion so that to be further rejected. The current work thus presents the intrinsic nucleotide selectivity of SARS-CoV-2 RdRp for natural substrate fidelity control, which should be considered in antiviral drug design.

Chemical profiling and dermatological and anti-aging properties of Syzygium jambos L. (Alston): evidence from molecular docking, molecular dynamics, and in vitro experiments

The phytoconstituents of the aqueous extract from Syzygium jambos L. (Alston) leaves were defined using HPLC-PDA-MS/MS and the antioxidant, anti-aging, antibacterial, and anti-biofilm activities of the extract were in silico and in vitro investigated. The antioxidant activities were performed using in vitro DPPH and FRAP assays as well as H2-DCFDA assay in HaCaT cells in which oxidative stress was induced by UVA radiation. Anti-aging activity was tested in vitro, using aging-related enzymes. The antibacterial, anti-biofilm and inhibitory effects on bacterial mobilities (swarming and swimming) were assessed against Pseudomonas aeruginosa. Results showed that S. jambos aqueous extract contained 28 phytochemicals belonging to different metabolite classes, mainly phenolic acids, gallic acid derivatives, flavonoids, and ellagitannins. Mineral content analysis showed that S. jambos leaves contained moderate amounts of nitrogen, potassium, manganese, magnesium, and zinc, relatively low amounts of phosphorus and copper, and high concentration of calcium and iron. The extract displayed strong antioxidant activities in vitro and inhibited UVA-induced oxidative stress in HaCaT cells. Docking the major compounds identified in the extract into the four main protein targets involved in skin aging revealed an appreciable inhibitory potential of these compounds against tyrosinase, elastase, hyaluronidase, and collagenase enzymes. Moreover, molecular dynamic simulations were adopted to confirm the binding affinity of some selected compounds towards the target enzymes. The extract exhibited pronounced in vitro anti-aging effects, compared to kojic acid and quercetin (the reference compounds). It also inhibited the growth of P. aeruginosa, counteracted its ability to form biofilm, and impeded its swarming and swimming mobilities. Altogether, these findings strongly propose S. jambos leaves as a promising source of bioactive metabolites for the development of natural cosmeceutical and dermatological agents.

Pinpointing top inhibitors for GSK3β from pool of indirubin derivatives using rigorous computational workflow and their validation using molecular dynamics (MD) simulations

Glycogen synthase kinase-3β (GSK3β) is a pivotal protein kinase implicated in a spectrum of debilitating diseases, encompassing cancer, diabetes, and neurodegenerative disorders. While the therapeutic potential of GSK3β inhibition is widely recognized, there remains an unmet need for a rigorous, systematic analysis probing the theoretical inhibition dynamics of a comprehensive library of indirubin derivatives against GSK3β using advanced computational methodologies. Addressing this gap, this study embarked on an ambitious endeavor, leveraging indirubin-a renowned scaffold-as a template to curate a vast library of 1000 indirubin derivatives from PubChem. These were enriched with varied substitutions and modifications, identified via a structure similarity search with a Tanimoto similarity threshold of 85%. Harnessing a robust virtual screening workflow, we meticulously identified the top 10 contenders based on XP docking scores. Delving deeper, we gauged the binding free energy differentials (ΔGBind) of these hits, spotlighting the top three compounds that showcased unparalleled binding prowess. A comparative pharmacophore feature mapping with the reference inhibitor OH8, co-crystallized with GSK3β (PDB ID: 6Y9R), was undertaken. The binding dynamics of these elite compounds were further corroborated with 100 ns molecular dynamics simulations, underlining their stable and potent interactions with GSK3β. Remarkably, our findings unveil that these indirubin derivatives not only match but, in certain scenarios, surpass the binding affinity and specificity of OH8. By bridging this research chasm, our study amplifies the therapeutic promise of indirubin derivatives, positioning them as frontrunners in the quest for groundbreaking GSK3β inhibitors, potentially revolutionizing treatments for a myriad of ailments.

A Review on Molecular Docking As an Interpretative Tool for Molecular Targets in Disease Management

One of the most often utilized methods for drug discovery is molecular docking. With docking, one may discover new therapeutically relevant molecules by targeting the molecule and predicting the target-ligand interactions as well as different conformation of ligand at various positions. The prediction signifies the effectiveness of the molecule or the developed molecule having different affinity with target. Drug discovery plays an important role in the development of a new drug molecule of different moiety attached to it, which leads us in the management of several diseases. In silico approach led us to identification of numerous diseases caused by virus, fungi, bacteria, protozoa, and other microorganisms that affect human health. By means of computational approach, we can categorize disease symptoms and use the drugs available for such types of warning signs. After the docking process, molecular dynamics computational technique helps in the simulation of the physical movement of atoms and molecules for a fixed period of time, giving a view of the dynamic evaluation of the system. This review is an attempt to illustrate the role of molecular docking in drug development.

Monotropein Induced Apoptosis and Suppressed Cell Cycle Progression in Colorectal Cancer Cells

OBJECTIVE

To determine whether monotropein has an anticancer effect and explore its potential mechanisms against colorectal cancer (CRC) through network pharmacology and molecular docking combined with experimental verification.

METHODS

Network pharmacology and molecular docking were used to predict potential targets of monotropein against CRC. Cell counting kit assay, plate monoclonal assay and microscopic observation were used to investigate the antiproliferative effects of monotropein on CRC cells HCT116, HT29 and LoVo. Flow cytometry and scratch assay were used to analyze apoptosis and cell cycle, as well as cell migration, respectively in HCT116, HT29, and LoVo cells. Western blotting was used to detect the expression of proteins related to apoptosis, cell cycle, and cell migration, and the expression of proteins key to the Akt pathway.

RESULTS

The Gene Ontology and Reactome enrichment analyses indicated that the anticancer potential of monotropein against CRC might be involved in multiple cancer-related signaling pathways. Among these pathways, RAC-beta serine/threonine-protein kinase (Akt1, Akt2), cyclin-dependent kinase 6 (CDK6), matrix metalloproteinase-9 (MMP9), epidermal growth factor receptor (EGFR), cell division control protein 42 homolog (CDC42) were shown as the potential anticancer targets of monotropein against CRC. Molecular docking suggested that monotropein may interact with the 6 targets (Akt1, Akt2, CDK6, MMP9, EGFR, CDC42). Subsequently, cell activity of HCT116, HT29 and LoVo cell lines were significantly suppressed by monotropein (P<0.05). Furthermore, our research revealed that monotropein induced cell apoptosis by inhibiting Bcl-2 and increasing Bax, induced G1-S cycle arrest in colorectal cancer by decreasing the expressions of CyclinD1, CDK4 and CDK6, inhibited cell migration by suppressing the expressions of CDC42 and MMP9 (P<0.05), and might play an anticancer role through Akt signaling pathway.

CONCLUSION

Monotropein exerts its antitumor effects primarily by arresting the cell cycle, causing cell apoptosis, and inhibiting cell migration. This indicates a high potential for developing novel medication for treating CRC.

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).

Unearthing the inhibitory potential of phytochemicals from Lawsonia inermis L. and some drugs against dengue virus protein NS1: an in silico approach

Dengue has received the status of an epidemic and endemic disease, with countless number of infections every year. Due to the unreliability of vaccines and non-specificity of drugs, it becomes necessary to find plant-based alternatives, with less harmful side effects. Lawsonia inermis L., is the sole source of dye, Mehendi. The rich repertoire of phytochemicals makes it useful, medicinally. The main objectives of the study are to explore the anti-dengue properties of the phytochemicals from Lawsonia inermis, and to shortlist potential candidates in curing the disease. Phytochemicals from the plant, and a set of drugs were screened and docked against NS1 protein, a less explored drug target, needed for maintenance of virus life cycle. Ligand screening and docking analysis concluded gallic acid, and chlorogenic acid to be good candidates, exhibiting high binding affinity and extensive interactions with the protein. From among the shortlisted drugs, only Vibegron showed effective binding affinity with NS1 protein with zero violations to the Lipinski's Rule of 5. Molecular dynamic simulations, executed for a time period of 100 nanoseconds, reveal the performance of a ligand within a solvated system. Chlorogenic and gallic acid, formed more stable and compact complexes with protein, with stable energy parameters and strong binding affinity. This was further validated with snapshots taken every 50 nanoseconds, showing no change in binding site between the ligand and protein, within the stipulated time frame. It was interesting to see that, a phenol (chlorogenic acid), served as a better drug candidate, against the NS1 protein.Communicated by Ramaswamy H. Sarma.

Identification of molecular targets of Trigonelline for treating breast cancer through network pharmacology and bioinformatics-based prediction

Breast cancer, a highly prevalent and fatal cancer that affects the female population worldwide, stands as a significant health challenge. Despite the abundance of chemotherapy drugs, the adverse side effects associated with them have initiated an investigation into natural plant-based compounds. Trigonelline, an alkaloid found in Trigonella foenum-graecum, was previously reported for its anticancer properties by the researchers. In this present study, we have identified the molecular targets of Trigonelline in breast cancer and predicted its drug-like properties and toxicity. By analyzing breast cancer targets from databases including TTD, TCGA, Gene cards, and Trigonelline targets obtained from CTD, we identified 14 specific targets of Trigonelline in the context of breast cancer. The protein-protein interaction (PPI) network of the 14 Trigonelline targets provided insights into the complex relationships between different genes and targets. Heatmap analysis demonstrated the expression patterns of these 14 genes at the protein and RNA levels in breast cancer cells and breast tissues. Notably, four genes, namely EGF, BAX, EGFR, and MTOR, were enriched in the breast cancer pathway. At the same time, PARP1, DDIT3, BAX, and TNF were associated with the apoptosis pathway according to KEGG pathway enrichment analyses. Molecular docking studies between Trigonelline and target proteins from the Protein Data Bank (PDB) revealed favorable binding affinity. Furthermore, mutation analysis of target genes within a dataset of 1918 samples from cBioPortal revealed the absence of mutations. Remarkably, Trigonelline also exhibited binding affinity towards two mutant proteins, and based on these findings, we predicted that Trigonelline could be utilized to target breast cancer genes and their mutants through network pharmacology. Additionally, this was supported by molecular dynamic simulation studies. As our study is preliminary, further validation through in vitro and in vivo studies is essential to confirm the efficacy of Trigonelline in breast cancer treatment.

Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

BACKGROUND

The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery.

RESULTS

The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets.

CONCLUSIONS

The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.

Exploring molecular docking with MM-GBSA and molecular dynamics simulation to predict potent inhibitors of cyclooxygenase (COX-2) enzyme from terpenoid-based active principles of Zingiber species

Cyclooxygenase 2 (COX-2), the key enzyme involved in prostaglandin (PGs) production, is known to take part in inflammatory and immune responses. Though COX-2 inhibitors are therapeutically effective anti-inflammatory drugs, they deficit anti-thrombotic activity thus leading to increased cardiovascular diseases. Therefore, COX-2 inhibitors with improved therapeutic efficacy and tolerance are still needed. In recent years, traditional medicine systems have paid attention to the essential oil of genus Zingiber, particularly for the treatment of various inflammatory illnesses, with lesser side effects. Thus, the present study aims to explore the anti-inflammatory activity of Zingiber essential oil through computational-biology approaches. In this regard, virtual screening, molecular docking, and simulations were carried out on 53 compounds derived from the essential oil of Zingiber species in order to provide mechanistic insights into COX-2 inhibition and identify the most actively potent anti-inflammatory compounds. Among all the docked ligands, epi-cubenol, δ-cadinene, γ-eudesmol, cubenol, and α-terpineol were found to be powerful bioactive compounds with an increased binding affinity towards COX-2 along with favorable physiochemical properties. Additionally, MD simulation in DPPC lipid bilayers was studied to examine the intrinsic dynamics and adaptability of the chosen ligands and COX-2-complexes. The findings showed that the selected five components interacted steadily with the COX-2 active site residues throughout the simulation via different bondings. The integrative-computational approach showed that the identified natural compounds may be taken into further consideration for potential in vitro and in vivo evaluation as COX-2 inhibitors, which would lead to the development of more potent and efficient anti-inflammatory drugs.Communicated by Ramaswamy H. Sarma.

Combining computational tools and experimental studies towards endocrine disruptors mitigation: A review of biocatalytic and adsorptive processes

The endocrine disruptors (EDCs) are an important group of emerging contaminants, and their mitigation has been a huge challenge due to their chemistry complexity and variety of these compounds. The traditional treatments are inefficient to completely remove EDCs, and adsorptive processes are the major alternative investigated on their removal. Also, the use of EDCs degrading enzymes has been encouraged due to ecofriendly approach of biocatalytic processes. This paper highlights the occurrence, classification, and toxicity of EDCs with special focus in the use of enzyme-based and adsorptive technologies in the elimination of EDCs from ambiental matrices. Numerous prior reviews have focused on the discussions toward these technologies. However, the literature lacks theoretical discussions about important aspects of these methods such as the mechanisms of EDCs adsorption on the adsorbent surface or the interactions between degrading enzymes - EDCs. In this sense, theoretical calculations combined to experimental studies may help in the development of more efficient technologies to EDCs mitigation. In this review, we point out how computational tools such as molecular docking and molecular dynamics have to contribute to the design of new adsorbents and efficient catalytic processes towards endocrine disruptors mitigation.

In Silico, in Vitro and in Vivo Study of Substituted Imidazolidinone Sulfonamides as Antibacterial Agents

New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested in vitro. Six studied compounds have shown the highest in vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The in vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35 mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.

ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions

Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners.

The cross-linking and protective effect of artemisinin and its derivatives on collagen fibers of demineralized dentin surface

OBJECTIVE

To investigate the cross-linking and protective effect of artemisinin (ART), dihydroartemisinin (DHA), and artesunate (AST) on collagen fibers of demineralized dentin surface.

METHODS

Molecular docking was used to predict potential interactions of ART, DHA, and AST with dentin type I collagen. Human third molars without caries were completely demineralized and treated with different solutions for 1 min. The molecular interactions and cross-linking degree of ART and its derivatives with dentin collagen were evaluated by FTIR spectroscopy, total extractable protein content, and a ninhydrin assay. Scanning electron microscopy, hydroxyproline release, and ultimate microtensile strength tests (μUTS) were employed to confirm the mechanical properties and anti-collagenase degradation properties of dentin collagen fibers.

RESULTS

ART, DHA, and AST combined with dentin type I collagen mainly through hydrogen bonding and hydrophobic interactions, and the cross-linking reaction sites were mainly C=O and CN functional groups. Compared to the control group, ART and its derivatives significantly increased the degree of cross-linking. Additionally, significant increases were observed in resistance to enzymatic digestion and mechanical properties of the artemisinin and its derivatives group.

CONCLUSION

ART, DHA, and AST could cross-link with demineralized dentin collagen, through improving the mechanical properties and anti-collagenase degradation properties.

CLINICAL SIGNIFICANCE

The study endorses the potential use of ART and its derivatives as a prospective collagen cross-linking agent for degradation-resistant and long-period dentin bonding in composite resin restorations.

In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma

Aromatase enzyme plays a fundamental role in the development of estrogen receptors, and due to this functionality, the enzyme has gained significant attention as a therapeutic for reproductive disorders and cancer diseases. The currently employed aromatase inhibitors have severe side effects whereas our novel aromatase inhibitor is more selective and less toxic, therefore has greater potential to be developed as a drug. The research framework of this study is to identify a potent inhibitor for the aromatase target by profiling molecular descriptors of the ligand and to find a functional pocket in the target by docking and MD simulations. For assessing cellular and metabolic activities as indicators of cell viability and cytotoxicity, in-vitro studies were performed by using the colorimetric MTT assay. Aromatase activities were determined by a fluorometric method. Cell morphology was assessed by phase-contrast light microscopy. Flow cytometry and Annexin V-FITC/PI staining assay determined cell cycle distribution and apoptosis. This study reports that CHEMBL708 (Ziprasidone) is the most promising compound that showed excellent aromatase inhibitory activity. By using better drug design methods and experimental studies, our study identified a novel compound that could be effective as a high-potential drug candidate against aromatase enzyme. We conclude that the compound ziprasidone effectively blocks the cell cycle at the G1-S phase and induces cancer cell death. Further, in-vivo studies are vital for developing ziprasidone as an anticancer agent. Lastly, our research outcomes based on the results of the in-silico experiments may pave the way for identifying effective drug candidates for therapeutic use in breast cancer.

In Silico Strategies to Predict Anti-aging Features of Whey Peptides

We have analysed the in silico potential of bioactive peptides from cheese whey, the most relevant by-product from the dairy industry, to bind into the active site of collagenase and elastase. The peptides generated from the hydrolysis of bovine β-lactoglobulin with three proteases (trypsin, chymotrypsin, and subtilisin) were docked onto collagenase and elastase by molecular docking. The interaction models were ranked according to their free binding energy using molecular dynamics simulations, which showed that most complexes presented favourable interactions. Interactions with elastase had significantly lower binding energies than those with collagenase. Regarding the interaction site, it was found that four bioactive peptides were positioned in collagenase's active site, while six were found in elastase's active site. Among these, the most we have found one promising collagen-binding peptide produced by chymotrypsin and two for elastase, produced by subtilisin and chymotrypsin. These in silico results can be used as a tool for designing further experiments aiming at testing the in vitro potential of the peptides found in this work.

Virtual screening indicates potential inhibitors of the P2X7 receptor

Anti-inflammatory agents can be synthetic or natural compounds and are often used to attenuate different levels of inflammation. Inflammatory diseases, due to the involvement of multiple systems, are becoming difficult to treat, involve long durations of therapy where applicable, have a high cost of management and have a deleterious impact on public health. The search for natural and synthetic compounds with anti-inflammatory activity is an important strategy in drug design. Bioactive synthetic drugs may be repurposed for other pharmacological applications, and natural product chemical structures offer unlimited opportunities for new drug discoveries due to the unparalleled availability of chemical diversity. Virtual screening of 2774 molecules on the mouse P2X7 protein showed that potential ligands are composed of five flavonoids (narirutin, diosmin, complanatuside, hesperidin, and oroxin B) and other drugs such as velpatasvir, itacitinib and lifitegrast. In vitro studies in mouse cells confirmed the inhibitory activity of the indicated ligands on the P2X7 receptor by applying virtual screening. The behavior of protein bonded to the ligands was verified by analysis of the molecular dynamic simulation trajectories for four of the most potent inhibitor compounds, indicating that the ligands velpatasvir, itacitinib, lithospermic acid and narirutin remained in the binding site indicated by molecular docking.

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction

BACKGROUND

Prediction of drug-target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug-target interacting parties, which may lead to insufficient feature representation.

MOTIVATION

In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI.

RESULTS

To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug-target interaction for drug-target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results.

CONCLUSIONS

MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug-drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.

Vanadium complex as a potential modulator of the autophagic mechanism through proteins PI3K and ULK1: development, validation and biological implications of a specific force field for [VO(bpy)2Cl]

The modulation of autophagy has been presented as a very useful strategy in anticancer treatments. In this sense, the vanadium complex (VC) bis(2,2'-bipyridine)chlorooxovanadium(IV), [VO(bpy)2Cl], is known for its ability to induce autophagy in triple-negative breast cancer cells (TNBC). An excellent resource to investigate the role of VC in the induction of autophagy is to make use of Molecular Dynamics (MD) simulations. However, until now, the scarcity of force field parameters for the VC prevented a reliable analysis. The autophagy signaling pathway starts with the PI3K protein and ends with ULK1. Therefore, in the first stage of this work, we developed a new AMBER force field for the VC (VCFF) from a quantum structure, obtained by DFT calculations. In the second stage, the VCFF was validated through structural analyses. From this, it was possible to investigate, through docking and MD (200 ns), the performance of the PI3K-VC and ULK1-VC systems (third stage). The analyses of this last stage involved RMSD, hydrogen bonds, RMSF and two pathways for the modulation of autophagy. In general, this work fills in the absence of force field parameters (FF) for VC by proposing an efficient and new FF, in addition to investigating, at the molecular level, how VC is able to induce autophagy in TNBC cells. This study encourages new parameterizations of metallic complexes and contributes to the understanding of the duality of autophagic processes.Communicated by Ramaswamy H. Sarma.

Computational study of SENP1 in cancer by novel natural compounds and ZINC database screening

Introduction: Sentrin-specific protease 1 (SENP1) is a protein whose main function is deSUMOylation. SENP1 inhibits apoptosis, and increases angiogenesis, estrogen and androgen receptor transcription and c-Jun transcription factor, proliferation, growth, cell migration, and invasion of cancer. The in vivo and in vitro studies also demonstrated which natural compounds, especially phytochemicals, minerals, and vitamins, prevent cancer. More than 3,000 plant species have been reported in modern medicine. Natural compounds have many anti-cancerous andanti-turmeric properties such as antioxidative, antiangiogenic, antiproliferative, and pro-apoptotic properties. Methods: In this study, we investigated the interaction of some natural compounds with SENP1 to inhibit its activity. We also screened the ZINC database including natural compounds. Molecular docking was performed, and toxicity of compounds was determined; then, molecular dynamics simulation (MDS) and essential dynamics (ED) were performed on natural compounds with higher free binding energies and minimal side effects. By searching in a large library, virtual screening of the ZINC database was performed using LibDock and CDOCKER, and the final top 20 compounds were allowed for docking against SENP1. According to the docking study, the top three leading molecules were selected and further analyzed by MDS and ED. Results: The results suggest that resveratrol (from the selected compounds) and ZINC33916875 (from the ZINC database) could be more promising SENP1 inhibitory ligands. Discussion: Because these compounds can inhibit SENP1 activity, then they can be novel candidates for cancer treatment. However, wet laboratory experiments are needed to validate their efficacy as SENP1 inhibitors.

In vitro antitumor activity, molecular dynamics simulation, DFT study, ADME prediction, and Eg5 binding of enastron analogues

The objective of this study is to evaluate a series of molecules based on cyclosulfamide as potential anticancer agents. Additionally, the study aims to analyze the obtained results through in silico studies; by conducting experiments and utilizing theoretical methods. In this context, we investigated the cytotoxic activity of enastron analogues on three human cell lines PRI (lymphoblastic cell line) derived from B-cell lymphoma. JURKAT (ATCC TIB-152) acute T cell leukaemia and K562 (ATCC CLL-243) is a chronic myelogenous leukaemia. Most of the tested compounds showed good inhibitory activity compared with the reference ligand (chlorambucil). The 5a derivative demonstrated the strongest effect against all cancer cells used. Furthermore, molecular docking simulations of the Eg5-enastron analogue complex revealed that studied molecules have the ability to inhibit the Eg5 enzyme, as evidenced by their calculated docking score. Following the promising results from the molecular docking study, the complex Eg5-4a underwent a 100 ns molecular dynamics simulation using Desmond. During the simulation, the receptor-ligand pairing demonstrated substantial stability after the initial 70 ns. In addition, we used DFT calculations to analyze the electronic and geometric characteristics of the studied compounds. The HOMO and LUMO band gap energies, and the molecular electrostatic potential surface were also deducted for the stable structure of each compound. Also, we studied the prediction of absorption, distribution, metabolism and excretion (ADME) of the compounds.

Targeting RNA Structure to Inhibit Editing in Trypanosomes

Mitochondrial RNA editing in trypanosomes represents an attractive target for developing safer and more efficient drugs for treating infections with trypanosomes because this RNA editing pathway is not found in humans. Other workers have targeted several enzymes in this editing system, but not the RNA. Here, we target a universal domain of the RNA editing substrate, which is the U-helix formed between the oligo-U tail of the guide RNA and the target mRNA. We selected a part of the U-helix that is rich in G-U wobble base pairs as the target site for the virtual screening of 262,000 compounds. After chemoinformatic filtering of the top 5000 leads, we subjected 50 representative complexes to 50 nanoseconds of molecular dynamics simulations. We identified 15 compounds that retained stable interactions in the deep groove of the U-helix. The microscale thermophoresis binding experiments on these five compounds show low-micromolar to nanomolar binding affinities. The UV melting studies show an increase in the melting temperatures of the U-helix upon binding by each compound. These five compounds can serve as leads for drug development and as research tools to probe the role of the RNA structure in trypanosomal RNA editing.

Exploring Pyrrolo-Fused Heterocycles as Promising Anticancer Agents: An Integrated Synthetic, Biological, and Computational Approach

Five new series of pyrrolo-fused heterocycles were designed through a scaffold hybridization strategy as analogs of the well-known microtubule inhibitor phenstatin. Compounds were synthesized using the 1,3-dipolar cycloaddition of cycloimmonium N-ylides to ethyl propiolate as a key step. Selected compounds were then evaluated for anticancer activity and ability to inhibit tubulin polymerization in vitro. Notably, pyrrolo[1,2-a]quinoline 10a was active on most tested cell lines, performing better than control phenstatin in several cases, most notably on renal cancer cell line A498 (GI₅₀ 27 nM), while inhibiting tubulin polymerization in vitro. In addition, this compound was predicted to have a promising ADMET profile. The molecular details of the interaction between compound 10a and tubulin were investigated through in silico docking experiments, followed by molecular dynamics simulations and configurational entropy calculations. Of note, we found that some of the initially predicted interactions from docking experiments were not stable during molecular dynamics simulations, but that configurational entropy loss was similar in all three cases. Our results suggest that for compound 10a, docking experiments alone are not sufficient for the adequate description of interaction details in terms of target binding, which makes subsequent scaffold optimization more difficult and ultimately hinders drug design. Taken together, these results could help shape novel potent antiproliferative compounds with pyrrolo-fused heterocyclic cores, especially from an in silico methodological perspective.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

In-silico study for the identification of potential destabilizers between the spike protein of SARS-CoV-2 and human ACE-2

The emergence of the new SARS-CoV-2 virus, which causes the disease known as COVID-19, has generated a pandemic that has plunged the world into a health crisis. The infection process is triggered by the direct binding of the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) of the host cell. In the present study, virtual screening techniques such as molecular docking, molecular dynamics, calculation of free energy using the GBSA method, prediction of drug similarity, pharmacokinetic, and toxicological properties of various ligands interacting with the RBD-ACE2 complex were applied. The ligands radotinib, hinokiflavone, and ginkgetin were identified as potential destabilizers of the RBD-ACE2 interaction, which could produce their pharmacological effect by interacting at an allosteric site of ACE2, with affinity energy values of -10.2 ± 0.1, -9.8 ± 0.0, and -9.4 ± 0.0 kcal/mol, indicating strong receptor affinity. The complex with hinokiflavone showed the highest conformational stability and rigidity of the dynamic simulation and also obtained the best binding free energy of the three molecules, with an energy of -215.86 kcal/mol.

Flavonoid as possible therapeutic targets against COVID-19: a scoping review of in silico studies

OBJECTIVES

This scoping review aims to present flavonoid compounds' promising effects and possible mechanisms of action on potential therapeutic targets in the SARS-CoV-2 infection process.

METHODS

A search of electronic databases such as PubMed and Scopus was carried out to evaluate the performance of substances from the flavonoid class at different stages of SARS-CoV-2 infection.

RESULTS

The search strategy yielded 382 articles after the exclusion of duplicates. During the screening process, 265 records were deemed as irrelevant. At the end of the full-text appraisal, 37 studies were considered eligible for data extraction and qualitative synthesis. All the studies used virtual molecular docking models to verify the affinity of compounds from the flavonoid class with crucial proteins in the replication cycle of the SARS-CoV-2 virus (Spike protein, PLpro, 3CLpro/ MPro, RdRP, and inhibition of the host's ACE II receptor). The flavonoids with more targets and lowest binding energies were: orientin, quercetin, epigallocatechin, narcissoside, silymarin, neohesperidin, delphinidin-3,5-diglucoside, and delphinidin-3-sambubioside-5-glucoside.

CONCLUSION

These studies allow us to provide a basis for in vitro and in vivo assays to assist in developing drugs for the treatment and prevention of COVID-19.