Determination of Potential Lead Compound from Magnolia officinalis for Alzheimer's Disease through Pharmacokinetic Prediction, Molecular Docking, Dynamic Simulation, and Experimental Validation.

Amyloid ß protein (Aß) deposition has been implicated as the molecular driver of Alzheimer's disease (AD) progression. The modulation of the formation of abnormal aggregates and their post-translational modification is strongly suggested as the most effective approach to anti-AD. Beta-site APP-cleaving enzyme 1 (BACE1) acts upstream in amyloidogenic processing to generate Aß, which rapidly aggregates alone or in combination with acetylcholinesterase (AChE) to form fibrils. Accumulated Aß promotes BACE1 activation via glycogen synthase kinase-3ß (GSK-3ß) and is post-translationally modified by glutaminyl cyclase (QC), resulting in increased neurotoxicity. A novel multi-target inhibitor as a potential AD agent was identified using an in silico approach and experimental validation. Magnolia officinalis, which showed the best anti-AD activity in our preliminary study, was subjected to analysis, and 82 compounds were studied. Among 23 compounds with drug-likeness, blood-brain barrier penetration, and safety, honokiol emerged as a lead structure for the inhibition of BACE1, AChE, QC, and GSK-3ß in docking and molecular dynamics (MD) simulations. Furthermore, honokiol was found to be an excellent multi-target inhibitor of these enzymes with an IC50 of 6-90 µM, even when compared to other natural single-target inhibitors. Taken together, the present study is the first to demonstrate that honokiol acts as a multiple enzyme inhibitor with an excellent pharmacokinetic and safety profile which may provide inhibitory effects in broad-range areas including the overproduction, aggregation, and post-translational modification of Aß. It also provides insight into novel structural features for the design and discovery of multi-target inhibitors for anti-AD.

Computational identification of promising therapeutics via BACE1 Targeting: Implications for Alzheimer's disease.

Alzheimer's disease (AD) is a significant global healthcare challenge, particularly in the elderly population. This neurodegenerative disorder is characterized by impaired memory and progressive decline in cognitive function. BACE1, a transmembrane protein found in neurons, oligodendrocytes, and astrocytes, exhibits varying levels across different neural subtypes. Abnormal BACE1 activity in the brains of individuals with AD leads to the formation of beta-amyloid proteins. The complex interplay between myelin sheath formation, BACE1 activity, and beta-amyloid accumulation suggests a critical role in understanding the pathological mechanisms of AD. The primary objective of this study was to identify molecular inhibitors that target Aß. Structure-based virtual screening (SBVS) was employed using the MCULE database, which houses over 2 million chemical compounds. A total of 59 molecules were selected after the toxicity profiling. Subsequently, five compounds conforming to the Egan-Egg permeation predictive model of the ADME rules were selected and subjected to molecular docking using AutoDock Vina on the Mcule drug discovery platform. The top two ligands and the positive control, 5HA, were subjected to molecular dynamics simulation for five nanoseconds. Toxicity profiling, physiochemical properties, lipophilicity, solubility, pharmacokinetics, druglikeness, medicinal chemistry attributes, average potential energy, RMSD, RMSF, and Rg analyses were conducted to identify the ligand MCULE-9199128437-0-2 as a promising inhibitor of BACE1.

Could Mushrooms' Secondary Metabolites Ameliorate Alzheimer Disease? A Computational Flexible Docking Investigation.

Herein, we highlight the significance of molecular modeling approaches prior to in vitro and in vivo studies; particularly, in diseases with no recognized treatments such as neurological abnormalities. Alzheimer disease is a neurodegenerative disorder that causes irreversible cognitive decline. Toxicity and ADMET studies were conducted using the Qikprop platform in Maestro software and Discovery Studio 2.0, respectively, to select the promising skeletons from more than 45 reviewed compounds isolated from mushrooms in the last decade. Using rigid and flexible molecular docking approaches such as induced fit docking (IFD) in the binding sites of ß-secretase (BACE1) and acetylcholine esterase (ACHE), promising structures were screened through high precision molecular docking compared with standard drugs donepezil and (2E)-2-imino-3-methyl-5,5-diphenylimidazolidin-4-one (OKK) using Maestro and Cresset Flare platforms. Molecular interactions, binding distances, and RMSD values were measured to reveal key interactions at the binding sites of the two neurodegenerative enzymes. Analysis of IFD results revealed consistent bindings of dictyoquinazol A and gensetin I in the pocket of 4ey7 while inonophenol A, ganomycin, and fornicin fit quite well in 4dju demonstrating binding poses very close to native ligands at ACHE and BACE1. Respective key amino acid contacts manifested the least steric problems according to their Gibbs free binding energies, Glide XP scores, RMSD values, and molecular orientation respect to the key amino acids. Molecular dynamics simulations further confirmed our findings and prospected these compounds to show significant in vitro results in their future pharmacological studies.

Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships.

This study investigates the clustering patterns of human ß-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

Heterologous expression of BACE1 and its interaction with Codonopsis pilosula polysaccharides and Lobetyolin.

BACE1, a crucial enzyme in the amyloid-ß deposition theory of Alzheimer's disease (AD), is targeted by Codonopsis pilosula, a traditional tonic believed to impede AD onset. However, the specific active compounds responsible for its effects remain elusive. Our prior network pharmacology research identified C. pilosula polysaccharides (CPPS) and Lobetyolin may serve as potential inhibitors of AD by suppressing amyloidogenesis. Here, we recombinantly expressed BACE1 under varied conditions and assessed its activity using Fluorescence Resonance Energy Transfer technology. Through spectroscopy, molecular docking, and dynamics, we elucidated the interactions of CPPS, Lobetyolin, and BACE1. Optimal BACE1 expression occurred at 22 °C with 0.4 mM IPTG for 6 h, yielding a 72 kDa protein. Enzyme kinetics displayed a maximum rate of 4096 µmol/min and a Michaelis constant of 16 mg/mL for BACE1. Spectroscopic analysis revealed differing binding affinities of the compounds at various temperatures, peaking at 293 K. Lobetyolin exhibited superior binding to BACE1 compared to CPPS, driven by hydrophobic and electrostatic forces. Molecular docking and dynamics highlighted hydrophobic amino acids' role in BACE1 interactions with Lobetyolin and CPPS, with binding energy < -1.2 kcal/mol signifying strong affinities. Notably, Lobetyolin and CPPS showed higher BACE1 affinity than APP, with the Lobetyolin-BACE1 complex being the most stable.

Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system.

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

In vitro cleavage of tumor necrosis factor α (TNFα) by Signal-Peptide-Peptidase-like 2b (SPPL2b) resembles mechanistic principles observed in the cellular context.

Members of the Signal Peptide-Peptidase (SPP) and Signal Peptide-Peptidase-like (SPPL) family are intramembrane aspartyl-proteases like their well-studied homologs, the presenilins, which comprise the catalytically active subunit within the γ-secretase complex. The lack of in vitro cleavage assays for SPPL proteases limited their biochemical characterization as well as substrate identification and validation. So far, SPPL proteases have been analyzed exclusively in intact cells or membranes, restricting mechanistic analysis to co-expression of enzyme and substrate variants colocalizing in the same subcellular compartments. We describe the details of developing an in vitro cleavage assay for SPPL2b and its model substrate TNFα and analyzed the influence of phospholipids, detergent supplements, and cholesterol on the SPPL2b in vitro activity. SPPL2b in vitro activity resembles mechanistic principles that have been observed in a cellular context, such as cleavage sites and consecutive turnover of the TNFα transmembrane domain. The novel in vitro cleavage assay is functional with separately isolated protease and substrate and amenable to a high throughput plate-based readout overcoming previous limitations and providing the basis for studying enzyme kinetics, catalytic activity, substrate recognition, and the characteristics of small molecule inhibitors. As a proof of concept, we present the first biochemical in vitro characterization of the SPPL2a and SPPL2b specific small molecule inhibitor SPL-707.

Assessment of fragment docking and scoring with the endothiapepsin model system.

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Evaluating a Retro-Inverso Type Inhibitor of HTLV-1 Protease as a Competitive Inhibitor.

The inhibition mode of a retro-inverso (RI) inhibitor containing a hydroxyethylamine dipeptide isostere against the human T-cell leukemia virus type-1 (HTLV-1) protease was examined. Enzymatic evaluation of the RI-modified inhibitor containing a D-allo-Ile residue revealed that HTLV-1 was competitively inhibited. IC50 values of the RI-modified inhibitor and pepstatin A, a standard inhibitor of aspartic proteases, were nearly equivalent.

BACE1 Inhibition Utilizing Organic Compounds Holds Promise as a Potential Treatment for Alzheimer's and Parkinson's Diseases.

Background: Neurological disorders like Alzheimer's disease (AD) and Parkinson's disease (PD) manifest through gradually deteriorating cognitive functions. An encouraging strategy for addressing these disorders involves the inhibition of precursor-cleaving enzyme 1 (BACE1). Objectives: In the current research, a virtual screening technique was employed to identify potential BACE1 inhibitors among selected herbal isolates. Methods: This study evaluated 79 flavonoids, anthraquinones (AQs), and cinnamic acid derivatives for their potential blood-brain barrier (BBB) permeability. Using the AutoDock 4.0 tool, molecular docking analysis was conducted to determine the binding affinity of BBB permeable compounds to the BACE1 active site. Molecular dynamics (MD) simulations were performed to assess the stability of the docked poses of the most potent inhibitors. The interactions between the most effective plant-based inhibitors and the residues within the BACE1 catalytic site were examined before and after MD simulations. Results: Ponciretin, danthron, chrysophanol, and N-p-coumaroyltyramine were among the highest-ranking BACE1 inhibitors, with inhibition constant values calculated in the nanomolar range. Furthermore, during 10 ns simulations, the docked poses of these ligands were observed to be stable. Conclusion: The findings propose that ponciretin, danthron, chrysophanol, and N-p-coumaroyltyramine might serve as potential choices for the treatment of AD and PD, laying the groundwork for the creation of innovative BACE1 inhibitors.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations.

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin.

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors.

Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.

Structure and function of SPP/SPPL proteases: insights from biochemical evidence and predictive modeling.

More than 20 years ago, signal peptide peptidase (SPP) and its homologues, the signal peptide peptidase-like (SPPL) proteases have been identified based on their sequence similarity to presenilins, a related family of intramembrane aspartyl proteases. Other than those for the presenilins, no high-resolution structures for the SPP/SPPL proteases are available. Despite this limitation, over the years bioinformatical and biochemical data have accumulated, which altogether have provided a picture of the overall structure and topology of these proteases, their localization in the cell, the process of substrate recognition, their cleavage mechanism, and their function. Recently, the artificial intelligence-based structure prediction tool AlphaFold has added high-confidence models of the expected fold of SPP/SPPL proteases. In this review, we summarize known structural aspects of the SPP/SPPL family as well as their substrates. Of particular interest are the emerging substrate recognition and catalytic mechanisms that might lead to the prediction and identification of more potential substrates and deeper insight into physiological and pathophysiological roles of proteolysis.

Synthesis of beta-site amyloid precursor protein-cleaving enzyme 1 inhibitors BI 1147560 and BI 1181181 labeled with carbon-14 and deuterium.

The generation of amyloid beta peptides that aggregate in the brain is believed to play a major role in Alzheimer's disease. In theory, the inhibition of beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1), which catalyzes the initial rate-limiting step in amyloid beta production, may slow or stop Alzheimer's disease. Herein, we report the preparation of two potent BACE1 inhibitors, BI 1147560 (1) and BI 1181181 (2), labeled with carbon-14 and with deuterium. The use of advanced key chiral intermediates like 3 and 5 shortened the carbon-14 syntheses of these two compounds to five and six steps, respectively, and helped in preparing them with very high chemical purity and enantiomeric excess without deviating from the process chemistry route. For the deuterium synthesis, oxetan-3-ylmethanamine [2 H6 ]-7 and 2-fluoro-2-methylpropan-1-amine [2 H6 ]-9 were prepared then used with the chiral intermediate 5 to furnish deuterium labeled 1 and 2, respectively.

Pepstatin-Based Probes for Photoaffinity Labeling of Aspartic Proteases and Application to Target Identification.

Aspartic proteases are a small class of proteases implicated in a wide variety of human diseases. Covalent chemical probes for photoaffinity labeling (PAL) of these proteases are underdeveloped. We here report a full on-resin synthesis of clickable PAL probes based on the natural product inhibitor pepstatin incorporating a minimal diazirine reactive group. The position of this group in the inhibitor determines the labeling efficiency. The most effective probes sensitively detect cathepsin D, a biomarker for breast cancer, in cell lysates. Moreover, through chemical proteomics experiments and deep learning algorithms, we identified sequestosome-1, an important player in autophagy, as a direct interaction partner and substrate of cathepsin D.

Repurposing antiviral drugs against HTLV-1 protease by molecular docking and molecular dynamics simulation.

Human T-cell leukemia virus type I (HTLV-1) belongs to the delta retrovirus family and the etiological agent of adult T-cell leukemia (ATL(. While the current HTLV-1 therapy, relies on using Zidovudine plus IFN-γ, there is no FDA approved drugs against it. In silico drug repurposing is a fast and accurate way for screening US-FDA approved drugs to find a therapeutic option for the HTLV-1 infection. So that, this research aims to analyze a dataset of approved antiviral drugs as a potential prospect for an anti-viral drug against HTLV-1 infection. Molecular docking simulation was performed to identify interactions of the antiviral drugs with the key residues in the HTLV-1 protease binding site. Then, molecular dynamics simulation was also performed for the potential protein-ligand complexes to confirm the stable behavior of the ligands inside the binding pocket. The best docking scores with the target was found to be Simeprevir, Atazanavir, and Saquinavir compounds which indicate that these drugs can firmly bind to the HTLV-1 protease. The MD simulation confirmed the stability of Simeprevir-protease, Atazanavir-Protease, and Saquinavir-Protease interactions. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against HTLV-1 infection.Communicated by Ramaswamy H. Sarma.

In silico Study of Acetylcholinesterase and Beta-secretase Inhibitors: Potential Multitarget Anti-Alzheimer's Agents.

BACKGROUND: Alzheimer's disease is a progressive neurodegenerative process with multifactorial characteristics. This disease follows the natural aging process, affecting mainly people over 65 years. Pharmacotherapeutic treatment currently combats symptoms related to cognitive function. Several targets have begun to attract the interest of the scientific community to develop new drug candidates which have better pharmacokinetic and lower toxicity parameters. OBJECTIVE: The present study aims to design new candidates for acetylcholinesterase/ß-secretase (AChE/BACE1) multitarget inhibitor drugs. METHODS: 17 natural products were selected from the literature with anticholinesterase activity and 1 synthetic molecule with inhibitory activity for BACE1. Subsequently, the molecular docking study was performed, followed by the derivation of the pharmacophoric pattern and prediction of pharmacokinetic and toxicological properties. Finally, the hybrid prototype was designed. RESULTS: All selected molecules showed interactions with their respective target enzymes. Derivation of the pharmacophoric pattern from molecules that interacted with the AChE enzyme resulted in 3 pharmacophoric regions: an aromatic ring, an electron-acceptor region and a hydrophobic region. The molecules showed good pharmacokinetic and toxicological results, showing no warnings of mutagenicity and/or carcinogenicity. After the hybridization process, three hybrid molecules were obtained, which showed inhibitory activity for both targets. CONCLUSION: It is concluded that research in the field of medicinal chemistry is advancing towards the discovery of new drug candidates that bring a better quality of life to patients with AD.

Exploring Aspartic Protease Inhibitor Binding to Design Selective Antimalarials.

Selectivity is a major issue in the development of drugs targeting pathogen aspartic proteases. Here, we explore the selectivity-determining factors by studying specifically designed malaria aspartic protease (plasmepsin) open-flap inhibitors. Metadynamics simulations are used to uncover the complex binding/unbinding pathways of these inhibitors and describe the critical transition states in atomistic resolution. The simulation results are compared with experimentally determined enzymatic activities. Our findings demonstrate that plasmepsin inhibitor selectivity can be achieved by targeting the flap loop with hydrophobic substituents that enable ligand binding under the flap loop, as such a behavior is not observed for several other aspartic proteases. The ability to estimate the selectivity of compounds before they are synthesized is of considerable importance in drug design; therefore, we expect that our approach will be useful in selective inhibitor designs against not only aspartic proteases but also other enzyme classes.

In Silico and In Vitro Antimalarial Screening and Validation Targeting Plasmodium falciparum Plasmepsin V.

Malaria chemotherapy is greatly threatened by the recent emergence and spread of resistance in the Plasmodium falciparum parasite against artemisinins and their partner drugs. Therefore, it is an urgent priority to develop new antimalarials. Plasmepsin V (PMV) is regarded as a superior drug target for its essential role in protein export. In this study, we performed virtual screening based on homology modeling of PMV structure, molecular docking and pharmacophore model analysis against a library with 1,535,478 compounds, which yielded 233 hits. Their antimalarial activities were assessed amongst four non-peptidomimetic compounds that demonstrated the promising inhibition of parasite growth, with mean IC50 values of 6.67 µM, 5.10 µM, 12.55 µM and 8.31 µM. No significant affection to the viability of L929 cells was detected in these candidates. These four compounds displayed strong binding activities with the PfPMV model through H-bond, hydrophobic, halogen bond or π-π interactions in molecular docking, with binding scores under -9.0 kcal/mol. The experimental validation of molecule-protein interaction identified the binding of four compounds with multiple plasmepsins; however, only compound 47 showed interaction with plasmepsin V, which exhibited the potential to be developed as an active PfPMV inhibitor.