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.

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.

Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors.

GPCRs are a family of transmembrane receptors that are profoundly linked to various neurological disorders, among which is Parkinson's disease (PD). PD is the second most ubiquitous neurological disorder after Alzheimer's disease, characterized by the depletion of dopamine in the central nervous system due to the impairment of dopaminergic neurons, leading to involuntary movements or dyskinesia. The current standard of care for PD is Levodopa, a dopamine precursor, yet the chronic use of this agent can exacerbate motor symptoms. Recent studies have investigated the effects of combining A2AR antagonist and 5-HT1A agonist on dyskinesia and motor complications in animal models of PD. It has been proved that the drug combination has significantly improved involuntary movements while maintaining motor activity, highlighting as a result new lines of therapy for PD treatments, through the regulation of both receptors. Using a combination of ligand-based pharmacophore modelling, virtual screening, and molecular dynamics simulation, this study intends on identifying potential dual-target compounds from IBScreen. Results showed that the selected models displayed good enrichment metrics with a near perfect receiver operator characteristic (ROC) and Area under the accumulation curve (AUAC) values, signifying that the models are both specific and sensitive. Molecular docking and ADMET analysis revealed that STOCK2N-00171 could be potentially active against A2AR and 5-HT1A. Post-MD analysis confirmed that the ligand exhibits a stable behavior throughout the simulation while maintaining crucial interactions. These results imply that STOCK2N-00171 can serve as a blueprint for the design of novel and effective dual-acting ligands targeting A2AR and 5-HT1A.Communicated by Ramaswamy H. Sarma.

Exploring natural products as multi-target-directed drugs for Parkinson's disease: an in-silico approach integrating QSAR, pharmacophore modeling, and molecular dynamics simulations.

Parkinson's disease is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the midbrain. Current treatments provide limited symptomatic relief without halting disease progression. A multi-targeting approach has shown potential benefits in treating neurodegenerative diseases. In this study, we employed in silico approaches to explore the COCONUT natural products database and identify novel drug candidates with multi-target potential against relevant Parkinson's disease targets. QSAR models were developed to screen for potential bioactive molecules, followed by a hybrid virtual screening approach involving pharmacophore modeling and molecular docking against MAO-B, AA2AR, and NMDAR. ADME evaluation was performed to assess drug-like properties. Our findings revealed 22 candidates that exhibited the desired pharmacophoric features. Particularly, two compounds: CNP0121426 and CNP0242698 exhibited remarkable binding affinities, with energies lower than -10 kcal/mol and promising interaction profiles with the chosen targets. Furthermore, all the ligands displayed desirable pharmacokinetic properties for brain-targeted drugs. Lastly, molecular dynamics simulations were conducted on the lead candidates, belonging to the dihydrochalcone and curcuminoid class, to evaluate their stability over a 100 ns timeframe and compare their dynamics with reference complexes. Our findings revealed the curcuminoid CNP0242698 to have an overall better stability with the three targets compared to the dihydrochalcone, despite the high ligand RMSD, the curcuminoid CNP0242698 showed better protein stability, implying ligand exploration of different orientations. Similarly, AA2AR exhibited higher stability with CNP0242698 compared to the reference complex, despite the high initial ligand RMSD due to the bulkier active site. In NMDAR, CNP0242698 displayed good stability and less fluctuations implying a more restricted conformation within the smaller active site of NMDAR. These results may serve as lead compounds for the development and optimization of natural products as multi-target disease-modifying natural remedies for Parkinson's disease patients. However, experimental assays remain necessary to validate these findings.Communicated by Ramaswamy H. Sarma.

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.

Structural exploration of selected C6 and C7-substituted coumarin isomers as selective MAO-B inhibitors.

Monoamine Oxidase B is considered a successful target for developing antiparkinsonian drugs. Due to the side effects of current MAO-B inhibitors, there's an urgent need for novel potent and highly selective MAO-B inhibitors. A recent study has shown that coumarins tend to be more selective towards MAO-B than MAO-A when connected to a hex-5-ynyloxy chain at position 6 in contrast to their C7-isomers. The present study describes the mode of interaction of the C6 and C7-substituted coumarin isomers characterized by their difference in selectivity towards MAO-B through molecular docking and molecular dynamics simulations in an effort to elucidate the structural components and molecular interactions that may be responsible for MAO-B selectivity. Three isomeric coumarin pairs connected to ether chain at position 6 or 7 were taken from the literature and modelled according to their IUPAC nomenclature. Molecular docking study revealed one π- π stacking interaction with Tyr-326 in common between the selective coumarin C6-isomers. Resulting complexes of one isomeric coumarin pair that displayed the highest selectivity shift towards MAO-B were subject to 100 ns molecular dynamics simulations study to analyze the stability of the docked complexes. Molecular dynamics revealed that the C7-isomer is relatively stable in both MAO isoforms through the simulation duration, whereas the C6-isomer deemed unstable for MAO-A which may be due to the bulky Phe-208 residue in MAO-A. Our results might be applied for further development and optimization of coumarin derivatives into a successful drug against Parkinson's disease.Communicated by Ramaswamy H. Sarma.

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

Parkinson's disease is considered the second most frequent neurodegenerative disease. It is described by the loss of dopaminergic neurons in the mid-brain. For many decades, L-DOPA has been considered as the gold standard for treating Parkinson's disease motor symptoms, however, due to the decrease of efficacy, in the long run, there is an urgent need for novel antiparkinsonian drugs. Caffeine derivatives have been reported several times for their neuroprotective properties and dual blockade of monoamine oxidase (MAO) and adenosine A2A receptors (AA2AR). Natural products are currently attracting more focus due to structural diversity and safety in contrast to synthetic drugs. In the present work, computational studies were conducted on natural product-like caffeine derivatives to search for novel potent candidates acting as dual MAO-B inhibitors/AA2AR antagonists for Parkinson's disease. Our findings revealed two natural products among the top hits: CNP0202316 and CNP0365210 fulfill the requirements of drugs acting on the brain. The selected lead compounds were further studied using molecular dynamics simulation to assess their stability with MAO-B. Current findings might shift the interest towards natural-based compounds and could be exploited to further optimize caffeine derivatives into a successful dual-target-directed drug for managing and halting the neuronal damage in Parkinson's disease patients.