Design, synthesis and collagenase inhibitory activity of some novel phenylglycine derivatives as metalloproteinase inhibitors

Immobilization of collagenase in inorganic hybrid nanoflowers with enhanced stability, proteolytic activity, and their anti-amyloid potential

Organic-inorganic hybrid nanomaterials are considered as promising immobilization matrix for enzymes owing to their markedly enhanced stability and reusability. Herein, collagenase was chosen as a model enzyme to synthesize collagenase hybrid nanoflowers (Col-hNFs). Maximum collagenase activity (155.58 μmol min-1 L-1) and encapsulation yield (90 %) were observed in presence of Zn(II) ions at 0.05 mg/mL collagenase, 120 mM zinc chloride and PBS (pH 7.5). Synthesized Col-Zn-hNFs were extensively characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR), circular dichroism (CD), fluorescence spectroscopy, dynamic light scattering (DLS) and zeta potential measurements. SEM images showed flower-like morphology with average size of 5.1 μm and zeta potential of -14.3 mV. Col-Zn-hNFs demonstrated superior relative activity across wide pH and temperature ranges, presence of organic solvents and surfactants as compared to its free form. Moreover, Col-Zn-hNFs exhibited excellent shelf life stability and favorable reusability. Col-Zn-hNFs showed the ability to suppress and eradicate fully developed insulin fibrils in vitro (IC50 = 2.8 and 6.2 μg/mL, respectively). This indicates a promising inhibitory potential of Col-Zn-hNFs against insulin amyloid fibrillation. The findings suggest that the utilization of Col-Zn-hNFs as a carrier matrix holds immense potential for immobilizing collagenase with improved catalytic properties and biomedical applications.

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.

Identification of novel glucocerebrosidase chaperone for potential treatment of Parkinson's disease: An approach using in silico virtual screening, molecular docking and molecular dynamics, and in vitro studies

Glucocerebrosidase (GCase), a GBA1 gene-encoded lysosomal enzyme, is a risk factor for Parkinson's disease (PD). Chaperones that increase GCase activity can potentially be disease-modifying agents in PD. To date, none of the registered treatments has demonstrated disease-modifying effects. Thus, chaperones for GCase were identified using in-silico virtual screening, molecular property filtering, and molecular dynamics and validated by circular dichroism, FT-IR, and Raman spectroscopies. In-vitro enzyme kinetics, thermal denaturation assay (TDA), and cell-line model were used to test their potential for GCase In-silico investigation revealed four compounds as candidate chaperones with adequate brain penetrability and binding energy (BE). Of them, GC466 showed ideal chaperoning characteristics, including potent BE -8.92 ± 0.68 Kcal/mol and binding affinity (K_i) 0.64 ± 0.12 μM against rGCase (Asp146, Phe265, and His329 residues) at pH 7.0 than at 4.5 (BE: -5.06 Kcal/mol, K_i: not found). Spectroscopic results confirmed the stability of GCase by GC466. TDA determined its chaperoning behavior, signified by improved rGCase thermal stabilization with stabilization ratio of 10.20 at 10 μM. In addition, it demonstrated GCase restorative, neurorestorative, and ROS scavenging activity in 6-OHDA treated cell-line model. Therefore, the present study may offer a novel chaperone with the potential to be a disease-modifying agent for PD.

Effect of sulfonamide derivatives of phenylglycine on scopolamine-induced amnesia in rats

Alzheimer's disease is a neurodegenerative disease responsible for dementia and other neuropsychiatric symptoms. In the present study, compounds 30 and 33, developed earlier in our laboratory as selective butyrylcholinesterase inhibitors, were tested against scopolamine-induced amnesia to evaluate their pharmacodynamic effect. The efficacy of the compounds was determined by behavioral experiments using the Y-maze and the Barnes maze and neurochemical testing. Both compounds reduced the effect of scopolamine treatment in the behavioral tasks at a dose of 20 mg/kg. The results of the neurochemical experiment indicated a reduction in cholinesterase activity in the prefrontal cortex and the hippocampus. The levels of antioxidant enzymes superoxide dismutase and catalase were restored compared to the scopolamine-treated groups. The docking study on rat butyrylcholinesterase (BChE) indicated tight binding, with free energies of -9.66 and -10.23 kcal/mol for compounds 30 and 33, respectively. The two aromatic amide derivatives of 2-phenyl-2-(phenylsulfonamido) acetic acid produced stable complexes with rat BChE in the molecular dynamics investigation.

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors

Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC₅₀ values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC₅₀ values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.

Identification of sulfonamide-based butyrylcholinesterase inhibitors using machine learning

Aim: This study reports the designing of BChE inhibitors through machine learning (ML), followed by in silico and in vitro evaluations. Methodology: ML technique was used to predict the virtual hit, and its derivatives were synthesized and characterized. The compounds were evaluated by using various in vitro tests and in silico methods. Results: The gradient boosting classifier predicted N-phenyl-4-(phenylsulfonamido) benzamide as an active BChE inhibitor. The derivatives of the inhibitor, i.e., compounds 34, 37 and 54 were potent BChE inhibitors and displayed blood-brain barrier permeability with no significant AChE inhibition. Conclusion: The ML prediction was effective, and the synthesized compounds showed the BChE inhibitory activity, which was also supported by the in silico studies.

Identification of sulfonamide based butyrylcholinesterase inhibitors through scaffold hopping approach

Butyrylcholinesterase (BChE), a hydrolytic enzyme, is responsible for the termination of the action of acetylcholine besides acetylcholinesterase (AChE) in the synaptic cleft of the brain. The alteration in the enzyme level, in patients with the progression of Alzheimer's disease, makes it a therapeutic target. In the present study, we developed BChE inhibitors through scaffold hopping by exploring two previously reported compounds, i.e., 1,4-bis((4-chlorophenyl) sulfonyl)-3,6-diphenylpiperazine-2,5-dione and N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide, to afford scaffold and pharmacophore fragments, respectively. The N,2-diphenyl-2-(phenylsulfonamido)acetamide derivatives, thus designed, were synthesised and screened for the inhibition of AChE and BChE enzymes. Compounds 30 and 33 were found to be most active against BChE among the derivatives, with IC₅₀ values of 7.331 ± 0.946 and 10.964 ± 0.936 μM, respectively. The compounds displayed a non-competitive mode of inhibition along with BBB permeability and good cell viability on SH-SY5Y cell line. The molecular docking analysis of the compounds with BChE showed interactions with Trp82, Trp231, Leu286, and His438. The molecular dynamics study revealed the stability of the protein-ligand complexes.

Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors

Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced [Formula: see text] and [Formula: see text] values of 0.94, 0.635 and 0.634, respectively.

In vitro evaluation of the anti-proteolytic and cross-linking effect of mussel-inspired monomer on the demineralized dentin matrix

OBJECTIVES

To evaluate the anti-proteolytic and cross-linking effect of N-(3,4-dihydroxyphenethyl)methacrylamide (DMA) on the demineralized dentin matrix in vitro.

METHODS

Four experimental solutions were selected: 50% ethanol/water solution (Control); 1, 5, and 10 mmol/L DMA dissolved in 50% ethanol/water solution. Sound human molars were sectioned to produce dentin beams with dimension of 1×1×6 mm. The dentin beams were demineralized with 10% phosphoric acid for 8 h to remove the apatite. The demineralized specimens were randomly separated into four groups and immersed in the four experimental solutions for 1 h. After the treatment, the ultimate tensile strength (UTS), loss of dry mass and the release of hydroxyproline by storing the treated specimens in 0.1 mg/mL collagenase solution for 24 h were assessed. The swelling ratio of another ten specimens from each group were evaluated. The interaction between DMA with dentin matrix was observed under Field Emission Scanning Electron Microscopy (FESEM). UTS data was analyzed by two-way ANOVA followed by Tukey test, and the other data was analyzed by one-way ANOVA followed by Tukey test (α = 0.05).

RESULTS

The two-way ANOVA factors, different solutions (p < 0.001), collagenase degradation (p < 0.001) and their interactions (p < 0.001) all significantly affected the UTS. The 10 mM DMA treatment significantly decreased the percentage of loss of dry mass, release of hydroxyproline and swelling ratio of demineralized dentin matrix compared to other treatment groups (p < 0.05). The FESEM observation depicted that with increasing concentration of DMA, the structure of dentin matrix was protected and the porosity within dentin collagen network was decreased.

CONCLUSIONS

The treatment by 10 mM DMA/ethanol solution for 1 hour is capable of enhancing the mechanical properties of demineralized dentin matrix against collagenase degradation and may be clinically useful to improve the durability of hybrid layer.

CLINICAL SIGNIFICANCE

The 10 mM DMA/ethanol primer may offer an alternative choice for dentists to strengthen the mechanical properties of demineralized dentin matrix and resist its degradation by collagenase.

Novel Method for the Quantitative Analysis of Protease Activity: The Casein Plate Method and Its Applications

No simple methods are used for the quantitative analysis of the protease activity in colored food up till now. Thus, this study aims to establish a new and simple method for the quantitative detection of protease activity, especially in colored food. The detection accuracy, detection limit, and repeatability of the casein plate method were analyzed. Then, the application of the casein plate method in sample detection and recovery was further evaluated. The results showed that the casein plate method for the quantitative detection of protease activity has high accuracy, high precision, and low detection limit. The recoveries of eight kinds of colored samples were in the range of 92.26-97.84%, and the relative standard deviation (RSD) was in the range of 3.56-10.88%. The results of the casein plate method exhibited high accuracy. This indicated that the method was suitable for the detection of colored samples. The casein plate method for the quantitative detection of protease activity is simple. The newly constructed casein plate method has broad potential application value in food industry, especially for the detection of dark food.

THE USE OF MASS SPECTROMETRY TO STUDY ZN-METALLOPROTEASE-SUBSTRATE INTERACTIONS

Zinc metalloproteases (ZnMPs) participate in diverse biological reactions, encompassing the synthesis and degradation of all the major metabolites in living organisms. In particular, ZnMPs have been recognized to play a very important role in controlling the concentration level of several peptides and/or proteins whose homeostasis has to be finely regulated for the correct physiology of cells. Dyshomeostasis of aggregation-prone proteins causes pathological conditions and the development of several different diseases. For this reason, in recent years, many analytical approaches have been applied for studying the interaction between ZnMPs and their substrates and how environmental factors can affect enzyme activities. In this scenario, mass spectrometric methods occupy a very important role in elucidating different aspects of ZnMPs-substrates interaction. These range from identification of cleavage sites to quantitation of kinetic parameters. In this work, an overview of all the main achievements regarding the application of mass spectrometric methods to investigating ZnMPs-substrates interactions is presented. A general experimental protocol is also described which may prove useful to the study of similar interactions. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Differential Biodegradation Kinetics of Collagen Membranes for Bone Regeneration

Native collagen-based membranes are used to guide bone regeneration; but due to their rapid biodegradation, this treatment is often unpredictable. The purpose of this study was to investigate the biodegradability of natural collagen membranes. Three non-cross-linked resorbable collagen barrier membranes were tested: Derma Fina (porcine dermis), Evolution Standard (equine pericardium) and Duo-Teck (equine lyophilized collagen felt). 10 × 10 mm² pieces of membranes were submitted to three different degradation procedures: (1) hydrolytic degradation in phosphate buffer solution, (2) enzyme resistance, using a 0.25% porcine trypsin solution, and (3) bacterial (Clostridium histolyticum) collagenase resistance test. Weight measurements were performed with an analytic microbalance. Thickness was measured with a digital caliper. Membranes were analyzed at different time-points, up to 21 d of immersion. A stereomicroscope was used to obtain membranes’ images. ANOVA and Student Newman Keuls were used for mean comparisons (p < 0.05), except when analyzing differences between time-points within the same membrane and solution where pair-wise comparisons were applied (p < 0.001). Derma Fina attained the highest resistance to all degradation challenges. Duo-Teck was the most susceptible membrane to degradation, complete degradation occurred as soon as 8 h. The bacterial collagenase solution performed as the most aggressive test as all membranes presented 100% degradation before 21 d.

Multifunctional hybrid sulfonamides as novel therapeutic agents for Alzheimer’s disease

Aim: A breakthrough in modern medicine, in terms of treatment of Alzheimer’s disease, is yet to be seen, as the scene is currently plagued with numerous clinical trial failures. Here, we are exploring multifunctional hybrid sulfonamides for their anti-Alzheimer activity due to the complex nature of the disease. Results & methodology: Compound 41 showed significant inhibition of MMP-2 (IC50: 18.24 ± 1.62 nM), AChE (IC50: 4.28 ± 0.15 μM) and BuChE (IC50: 1.32 ± 0.02 μM). It also exhibited a metal-chelating property, as validated by an in vitro metal-induced Aβ aggregation assay using confocal fluorescence imaging. Whereas, MTT and DPPH assays revealed it to be nontoxic and neuroprotective with substantial antioxidant property. Conclusion: The present study puts forth potent yet nontoxic lead molecules, which foray into the field of multitargeted agents for the treatment of Alzheimer’s disease.

Identifying potential GluN2B subunit containing N-Methyl-D-aspartate receptor inhibitors: an integrative in silico and molecular modeling approach

N-methyl-D-aspartate receptors (NMDARs), a class of ligand-gated ion channels, are involved in non-selective cation transport across the membrane. These are contained in glutamatergic synapse and produce excitatory effects leading to synaptic plasticity and memory function. GluN1-GluN2B, a subtype of NMDAR(s), has significant role in neurodegeneration, amyloid β (Aβ) induced synaptic dysfunction and loss. Thus, targeting and inhibiting GluN1-GluN2B may be effective in the management of neurodegenerative diseases including Alzheimer's disease. In the present study, ligand and structure-based approaches were tried to identify the inhibitors. The pharmacophore, developed from co-crystallised ifenprodil, afforded virtual hits, which were further subjected through drug likeliness and PAINS filters to remove interfering compounds. Further comprehensive docking studies, free energy calculations and ADMET studies resulted in two virtual leads. The leads, ZINC257261614 and ZINC95977857 displayed good docking scores of -12.90 and -12.20 Kcal/mol and free binding energies of -60.83 and -61.83 Kcal/mol, respectively. The compounds were having acceptable predicted ADMET profiles and were subjected to molecular dynamic (MD) studies. The MD simulation produced stable complexes of these ligands with GluN1-GluN2B subunit having protein and ligand RMSD in acceptable limit. AbbreviationsADAlzheimer's diseaseADMEAbsorption distribution metabolism and excretionATDAmino terminal domainBBBBlood-brain barrierCNSCentral nervous systemCREBcAMP response element binding proteinCTDCarboxy-terminal domainGluGlutamateGMQEGlobal model quality estimationHTVSHigh throughput virtual screeningHIAHuman intestinal absorptionLGALamarckian genetic algorithmMDMolecular dynamicsMM-GBSAMolecular mechanics, the Generalised Born model for Solvent AccessibilityNMDARN-methyl-D-aspartate receptorsPAINSPan assay interference compoundsRMSDRoot-mean square deviationRMSFRoot-mean-square fluctuationSMARTSSMILES arbitrary target specificationSPstandard precisionXPextra precisionCommunicated by Ramaswamy H. Sarma.