In silico studies of natural product-like caffeine derivatives as potential MAO-B inhibitors/AA2AR antagonists for the treatment of Parkinson's disease

Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson's disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies.

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.

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations

Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Several synthetic and natural compounds have been tested for their potency to inhibit the aggregation of α-synuclein. However, the molecular mechanisms responsible for the potency of these drugs to further rationalize their development and optimization are yet to be determined. To enhance our understanding of the structural requirements necessary for modulating the aggregation of α-synuclein fibrils, we retrieved a large dataset of α-synuclein inhibitors with their reported potency from the ChEMBL database to explore their chemical space and to generate QSAR models for predicting new bioactive compounds. The best performing QSAR model was applied to the LOTUS natural products database to screen for potential α-synuclein inhibitors followed by a pharmacophore design using the representative compounds sampled from each cluster in the ChEMBL dataset. Five natural products were retained after molecular docking studies displaying a binding affinity of - 6.0 kcal/mol or lower. ADMET analysis revealed satisfactory properties and predicted that all the compounds can cross the blood-brain barrier and reach their target. Finally, molecular dynamics simulations demonstrated the superior stability of LTS0078917 compared to the clinical candidate, Anle138b. We found that LTS0078917 shows promise in stabilizing the α-synuclein monomer by specifically binding to its hairpin-like coil within the N-terminal region. Our dynamic analysis of the inhibitor-monomer complex revealed a tendency towards a more compact conformation, potentially reducing the likelihood of adopting an elongated structure that favors the formation and aggregation of pathological oligomers. These findings offer valuable insights for the development of novel α-synuclein inhibitors derived from natural sources.

Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections

Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from -7.8 to -7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections.

IMPORTANCE

Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.

Antidepressant-like Effects of Chinese Quince (Chaenomeles sinensis) Fruit Based on In Vivo and Molecular Docking Studies

In this study, we examined the potential antidepressant-like effects of Chinese quince fruit extract (Chaenomeles sinensis fruit extract, CSFE) in an in vivo model induced by repeated injection of corticosterone (CORT)-induced depression. HPLC analysis determined that chlorogenic acid (CGA), neo-chlorogenic acid (neo-CGA), and rutin (RT) compounds were major constituents in CSFE. Male ICR mice (5 weeks old) were orally administered various doses (30, 100, and 300 mg/kg) of CSFE and selegiline (10 mg/kg), a monoamine oxidase B (MAO-B) inhibitor, as a positive control following daily intraperitoneal injections of CORT (40 mg/kg) for 21 days. In our results, mice treated with CSFE exhibited significant improvements in depressive-like behaviors induced by CORT. This was evidenced by reduced immobility times in the tail suspension test and forced swim test, as well as increased step-through latency times in the passive avoidance test. Indeed, mice treated with CSFE also exhibited a significant decrease in anxiety-like behaviors as measured by the elevated plus maze test. Moreover, molecular docking analysis indicated that CGA and neo-CGA from CSFE had stronger binding to the active site of MAO-B. Our results indicate that CSFE has potential antidepressant effects in a mouse model of repeated injections of CORT-induced depression.

Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE

Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many natural products and is reported to display various pharmacological properties. A large plethora of compounds based on the coumarin ring system have been synthesized and were found to possess biological activities such as anticonvulsant, antiviral, anti-inflammatory, antibacterial, antioxidant as well as neuroprotective properties. Despite the wide activity spectrum of coumarins, its naturally occurring derivatives are yet to be investigated in detail. In the current study, a chemical library was created to assemble all chemical information related to naturally occurring coumarins from the literature. Additionally, a multi-stage virtual screening combining QSAR modeling, molecular docking, and ADMET prediction was conducted against monoamine oxidase B and acetylcholinesterase, two relevant targets known for their neuroprotective properties and 'disease-modifying' potential in Parkinson's and Alzheimer's disease. Our findings revealed ten coumarin derivatives that may act as dual-target drugs against MAO-B and AChE. Two coumarin candidates were selected from the molecular docking study: CDB0738 and CDB0046 displayed favorable interactions for both proteins as well as suitable ADMET profiles. The stability of the selected coumarins was assessed through 100 ns molecular dynamics simulations which revealed promising stability through key molecular interactions for CDB0738 to act as dual inhibitor of MAO-B and AChE. However, experimental studies are necessary to evaluate the bioactivity of the proposed candidate. The current results may generate an increasing interest in bioprospecting naturally occurring coumarins as potential candidates against relevant macromolecular targets by encouraging virtual screening studies against our chemical library.Communicated by Ramaswamy H. Sarma.

Piperidine-based natural products targeting Type IV pili antivirulence: A computational approach

Type IV (T4) pilus is among the virulence factors with a key role in serious bacterial diseases. Specifically, in Neisseria meningitidis and Pseudomonas aeruginosa, it determines pathogenicity and causes infection. Here, a computational approach has been pursued to find piperidine-based inhibitor molecules against the elongation ATPase of T4 pili in these two selected pathogens. Using the modeled structures of the PilF and PilB ATPases of N. meningitidis and P. aeruginosa, virtual library screening via molecular docking has returned inhibitor molecule candidates. The dynamics of the best three binders have further been investigated in detail via molecular dynamic simulations. Among these, ligands with COCONUT IDs CNP0030078 and CNP0051517 were found to have higher potential in the inhibition of ATPases based on molecular dynamic simulation analysis and biological activity information. The obtained results will guide future efforts in antivirulence drug development against T4 pili of N. meningitidis and P. aeruginosa.

Data-Driven Approaches Used for Compound Library Design for the Treatment of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015-2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.