These data demonstrate that phosphorylation of Y342 on Syk following stimulation of either GPVI or CLEC-2 receptors is important for the ability of Syk to transduce a signal.Immune cells express receptors bearing an immune tyrosine activation motif (ITAM) containing two YXXL motifs or hemITAMs containing only one YXXL motif. Phosphorylation of the ITAM/hemITAM is mediated by Src family kinases allowing for the binding and activation of spleen tyrosine kinase (Syk). It is believed that Syk must be phosphorylated on tyrosine residues for activation, and Tyr342, а conserved tyrosine in the interdomain B region, has been shown to be critical for regulating Syk in FcεR1-activated mast cells. Syk is a key mediator of signaling pathways downstream of several platelet pathways including the ITAM bearing glycoprotein VI (GPVI)/Fc receptor gamma chain collagen receptor and the hemITAM containing C-type lectin-like receptor-2 (CLEC-2). Since platelet activation is a crucial step in both hemostasis and thrombosis, we evaluated the importance of Syk Y342 in these processes by producing an Syk Y342F knock-in mouse. When using a CLEC-2 antibody as an agonist, reduced aggregation and secretion were observed in Syk Y342F mouse platelets when compared with control mouse platelets. Platelet reactivity was also reduced in response to the GPVI agonist collagen-related peptide. Signaling initiated by either GPVI or CLEC-2 was also greatly inhibited, including Syk Y519/520 phosphorylation. Hemostasis, as measured by tail bleeding time, was not altered in Syk Y342F mice, but thrombus formation in response to FeCl Immune cells express several receptors that, upon activation, their cytoplasmic tails are phosphorylated on tyrosines in a stretch of domain known as immune tyrosine activation motif (ITAM) containing two YXX(L/I) motifs , 2. RecePlatelets are anucleate cells that are produced by megakaryocytes . Their pThere are two main categories of receptors on the platelet surface: the G protein–coupled receptors and those that signal through a nonreceptor tyrosine kinase. Those that signal through a nonreceptor tyrosine kinase include the ITAM-bearing GPVI receptor and the Fc receptor for immunoglobulin, FcγRIIA, as well as the hemITAM-bearing receptor CLEC-2. Ligand engagement of these receptors results in Src family kinase (SFK) phosphorylation of tyrosine residues within the cytoplasmic domain of the receptor , 17, 18.via its two SH2 domains  region, are involved in the regulation of Syk activity and functions , 42, 43.A. We also confirmed that the genotyping using sequencing of the PCR product (B). To confirm the Y342F mutation at the protein level, we isolated platelets from Syk Y342F and WT littermate control mice and stimulated them with collagen-related peptide (CRP). We performed Western blot analysis to visualize phosphorylation of Y342. We observed a robust band in WT mouse platelets treated with CRP at any concentration, but no band is present at the expected molecular weight in samples obtained from Syk Y342F mouse platelets (C). These data demonstrate that we successfully produced Syk Y342F knock-in mice.Primary mast cells and basophilic cells in culture expressing Syk Y342F as a result of transfection or transduction demonstrated that tyrosine phosphorylation of mutated Syk and proteins important for ITAM-mediated signaling was reduced as compared with cells expressing WT Syk , 41. TheFigure 1 product B. To conlatelets C. These Heterozygous Syk Y342F knock-in mice and homozygous Syk Y342F knock-in mice bred normally and produced pups at expected Mendelian ratios. In contrast, Syk knockout mice are not viable and die perinatally , 46. BloA–C). This was not the case when a high concentration of CLEC-2 antibody was used as an agonist as full aggregation was observed in Y342F platelets although ATP secretion was still reduced (A–C). We also evaluated alpha granule release using P-selectin as a marker by flow cytometry. As can be seen in Figure 3D, alpha granule release was also reduced in Y342F platelets compared with WT control platelets.Syk is crucial for hemITAM-dependent signaling , 33, 47. reduced , A–C. WeA–D). This suggests that the signaling required to enable platelet function is completely blocked with the loss of Y342 phosphorylation on Syk during low levels of CLEC-2 clustering. We further evaluated the role of Y342 phosphorylation on Akt and Erk pathways. As shown in A, E and F, both Akt and Erk phosphorylations were impaired in the Y342F platelets compared with WT control platelets.As we demonstrated that Syk Y342 was necessary for platelet functional responses upon CLEC-2 clustering, we examined the phosphorylation status of Syk and PLCγ2 following stimulation of the CLEC-2 receptor in Syk Y342F and WT littermate control platelets. After treatment with the CLEC-2 antibody, there is phosphorylation of Syk Y519/520, Syk Y346, and PLCγ2 Y1217 in WT platelets, but phosphorylation of any of these sites is dramatically reduced in Syk Y342F platelets , A–D. Thvia GPVI agonists just as it is with CLEC-2 agonists, we evaluated the effect of Syk Y342F on GPVI-mediated aggregation and secretion, using isolated platelets from Syk Y342F and WT littermate control mice with varying concentrations of CRP. We observed pronounced aggregation and secretion in WT platelets (A). However, Syk Y342F platelets had significantly less aggregation and secretion at the lower concentrations of CRP, this defect was not observed when a high concentration of CRP was used (A–C). As can be seen in Figure 5D, alpha granule release was also reduced in Y342F platelets compared with WT control platelets.Because Syk activity is vital to platelet activation latelets A. Howevewas used , A–C. AsA–D). We further evaluated the role of Y342 phosphorylation on Akt and Erk pathways. As shown in A, E and F, both Akt and Erk phosphorylations were impaired in the Y342F platelets compared with WT control platelets. These data collectively indicate that signaling downstream of GPVI is diminished in Syk Y342F platelets.Syk is a vital component of the signaling pathway downstream from hemITAM- as well as ITAM-bearing receptors , 23, 24.A). However, calcium mobilization in Syk Y342F platelets was significantly reduced compared with WT platelets (D). Using the CLEC-2 monoclonal antibody, we detected a weak but measurable signal from WT platelets, but no signal was detectable in the Syk Y342F platelets (B and D). CLEC-2 double crosslinking produced the maximum calcium response in WT and Syk Y342F platelets though calcium mobilization was still significantly reduced in Syk Y342F platelets (C and D). Collectively, these data demonstrate that phosphorylation of Y342 on Syk is important for calcium mobilization in response to both ITAM- and hemITAM-mediated activation. Although these data agree with the notion that platelet functional responses can be reduced by Syk Y342F, there are quantitative differences between functional and calcium flux data; Syk Y342F reduces calcium responses even under conditions of strong stimulation, when it exerts no significant effect on aggregation.A hallmark of platelet activation is the mobilization of intracellular calcium. Therefore, we measured calcium flux in Syk Y342F and WT littermate control platelets after stimulation with different agonists. In response to CRP, we observed a robust calcium signal in WT platelets A. Howevelatelets D. Using latelets , B and Dlatelets , C and DA). These data suggest that Syk Y342 phosphorylation is not essential for hemostasis. To determine whether thrombus formation is impacted by the impaired phosphorylation of Y342 on Syk, we injured the carotid arteries of Syk Y342F and WT littermate control mice with 7.5% FeCl3 for 90 s and monitored occlusion time. WT mice formed stable occlusions in just under 10 min on average, whereas Syk Y342F mice took significantly longer to form stable occlusions (B and C). These data suggest that phosphorylation of Y342 on Syk positively regulates thrombus formation in vivo.To determine the impact of Syk Y342F on hemostasis, we performed tail bleeding assay on litter-matched WT, heterozygous, and homozygous Syk Y342F mice. We found no differences in the bleeding times A. These clusions , B and Cvia hemITAM receptors is not known. Focusing our effort of the Y342 site, we clearly demonstrated that platelet functional responses induced by CLEC-2 signaling were greatly reduced in Syk Y342F knock-in mice compared with WT littermates. Consistent with this result, phosphorylation of Syk Y519/520 and PLCγ2 Y1217 and calcium responses were also greatly reduced. Syk Y346 phosphorylation was likewise greatly reduced by Syk Y342F; this result is consistent with the effect of Y342F on phosphorylation of Y346 that we have demonstrated previously in the reconstituted cell system transfected to express Syk Y342F  are mutated to phenylalanine . Furtherse assay . In the se assay . These dSyk Y342 phosphorylation is crucial to transduction of a weak signal downstream of GPVI, but it may not be essential for transmitting stronger signals. Our data show that Syk Y342F platelets have a greatly diminished response to low concentrations of CRP . HoweverSyk Y342F mice have reduced thrombus formation in a ferric chloride injury model but do not have a bleeding phenotype. This is likely linked to dense granule secretion. It has been postulated that secretion defects, which manifest at low agonist concentrations, but recover at high agonist doses (like Syk Y342F mouse platelets treated with CRP), are more likely to correlate with alterations in thrombosis than alterations in hemostasis . These dIn conclusion, we demonstrate that Y342 on Syk positively regulates ITAM- and hemITAM-mediated signaling using an Syk Y342F knock-in mouse model, specifically at low agonist concentrations. Physiologically, Syk Y342 appears to positively regulate thrombus formation, without affecting bleeding times.All reagents were purchased from Thermo Fisher Scientific unless otherwise stated. Collagen and chronolume, used for the detection of secreted ATP, were purchased from Chrono-Log Corporation. The CLEC-2 activating antibody was purchased from BioLegend, and the donkey antirat immunoglobulin was purchased from Novex. Anti-pSyk Y525/526 (mouse Y519/520) and anti-pPLCγ2 (Y1217) were purchased from Cell Signaling Technology. Anti-pSyk Y352 (Y346 in mouse) and anti-pSyk Y348 (Y342 in mice) were purchased from Abcam. Anti-Syk and anti-PLCγ2 were purchased from Santa Cruz Biotechnology. Ibrutinib was purchased from Selleckchem. Odyssey blocking buffer and secondary antibodies IRDye 800CW goat anti-rabbit and IRDye 680LT goat antimouse were purchased from Li-Cor. CRP-XL was purchased from Dr Richard Farndale at the University of Cambridge. AYPGKF was purchased from GenScript.Mice were housed in a pathogen-free facility, and all animal procedures were approved by the Temple University Institutional Animal Care and Use Committee (protocol #4864). Syk Y342F mice were produced by Cyagen on a fee-for-service basis.8 cells/ml in N-2-hydroxyethylpiperazine–N′-2-ethanesulfonic acid–buffered (pH 7.4) Tyrode’s solution containing 0.2 U/ml apyrase.Mouse blood was collected, and platelets were isolated as previously described . The resAll platelet aggregation and secretion experiments were carried out using a Lumi-Aggregometer (Chrono-Log) at 37 °C under stirring conditions. Platelet aggregation was measured using light transmission, and ATP secretion was measured using Chrono-Lume (a luciferin/luciferase reagent).4 and washed with water prior to the addition of sample loading buffer. Platelet protein samples were then boiled for 5 min prior to resolution by SDS-PAGE and transferred to nitrocellulose membranes. The membranes were then blocked using Odyssey blocking buffer and incubated overnight with primary antibodies against the indicated protein. The membranes were then washed with Tris-buffered saline containing 0.1% Tween-20 prior to incubation with appropriate secondary antibodies for 1 h at room temperature. The membranes were washed again and imaged using a Li-Cor Odyssey infrared imaging system.Western blotting procedures were performed as described previously . BrieflySurface exposure of P-selectin (antiFITC-P-selectin) in murine platelets was determined as previously described . The samg for 10 min. The resulting platelet-rich plasma was collected and diluted with two volumes in the aforementioned Pipes Tyrode’s buffer. Platelets were then centrifuged at 800g for 10 min at room temperature, and the resulting pellet resuspended in 1 ml Pipes Tyrode’s buffer and incubated with 5 μM FURA-2 AM for 45 min at room temperature. The samples were then diluted with 1 ml Pipes Tyrode’s buffer and centrifuged at 400g for 10 min. The platelet pellet was then resuspended with N-2-hydroxyethylpiperazine–N′-2-ethanesulfonic acid–buffered Tyrode’s buffer, adjusted to 1.5 × 108 ml, and allowed to rest for 30 min prior to calcium measurements. Calcium was measured using a Duetta fluorescence and absorbance spectrometer from Horiba using the following equation: [Ca2+]i = (Kd(f-fmin)/(fmax-f)).Measurement of intracellular calcium was conducted as described previously with minor modifications . Whole bMouse tail bleeding was conducted as previously described . Mice ag3 was used to injure the carotid artery as previously described (3 for 90 s. The filter paper was removed, and blood flow was recorded.FeClescribed . Mice agt test where p < 0.05 was considered statistically significant. All the data are presented as means ± SD of at least three independent experiments.All statistical analyses were performed using Microsoft Excel, and data were analyzed using a Student’s All data concerning this report are available in the article.The authors declare that they have no conflicts of interest with the contents of this article.


Refinement with molecular dynamic (MD) simulations shows that the identified non-catalytic sites are potential allostery sites for compound 1. The allostery and binding mechanism of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest that further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric BACE1 inhibitors.In over a century since its discovery, Alzheimer’s disease (AD) has continued to be a global health concern due to its incurable nature and overwhelming increase among older people. In this paper, we give an overview of the efforts of researchers towards identifying potent BACE1 exosite-binding antibodies and allosteric inhibitors. Herein, we apply computer-aided drug design (CADD) methods to unravel the interactions of some proposed psychotic and meroterpenoid BACE1 allosteric site inhibitors. This study is aimed at validating the allosteric potentials of these selected compounds targeted at BACE1 inhibition. Molecular docking, molecular dynamic (MD) simulations, and post-MD analyses are carried out on these selected compounds, which have been experimentally proven to exhibit allosteric inhibition on BACE1. The SwissDock software enabled us to identify more than five druggable pockets on the BACE1 structural surface using docking. Besides the active site region, a melatonin derivative (compound  Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder discovered in November 1906 . The extAlzheimer’s disease facts and figures 2021) showed that about 6.2 million people in the United States of America are living with AD-related dementia and this incidence might increase to about 13 million by the year 2050  showed t,9. GlobaThe early symptoms of AD include disruption of daily life activities due to memory loss; difficulty in understanding visual images and completing known tasks; challenges with identification, speaking, and writing new words; disorganization; and constant mood swings ,13. AvaiSince its discovery in 1999, BACE1 has had a primary focus on the reduction of β-amyloid plaques in the brain. Although there is no approved BACE1 inhibitor , evidencBACE1 is a family of the aspartate protease with a characteristic catalytic dyad (Asp32/228) at its active site, required for substrate cleavage ,24. The O-β-d-glucoside [OM99-2 [12 with protein data bank (PDB) [50 value of 0.5 mM, which is significant. Although the interaction pose occurred close to the BACE1 active site region, we assume this mechanism as a potential allosteric inhibition.Allosteric inhibition is a form of noncompetitive inhibition that occurs at another protein site different from the active site . In allolucoside , loganinlucoside , and gamlucoside  have beelucoside  applied lucoside . Refinemlucoside  competit [OM99-2 . X-ray ank (PDB)  code 5MXnk (PDB)  showed a1 (a melatonin derivative) with 88% inhibitory activity at 5 μM BACE1 concentration [1) at the BACE1 binding region to establish if these sites are transient or long-lived using several metrics. Understanding how potent small inhibitors modulate and inhibit BACE1 at the molecular level would enable us to manipulate synthetic enzymes or design drugs for AD treatment.Herein, we apply molecular modeling methods, including molecular docking and molecular dynamics (MD) simulations to unravel the interaction of some potential allosteric inhibitors with BACE1. Readers may be puzzled by the uniqueness of the present work since there exist investigations on BACE1 allosterism. Indeed, there are propositions on BACE1 allosteric or secondary sites inhibition, there is still a knowledge gap at the molecular level using computational methods, like MD simulations. Besides, Pietro et al.’s (2017) study of conformational ensemble and binding mode analysis of some multisite inhibitors using MD and docking method , studiesntration  showed aBACE1 X-ray 3D structures are available as several complexes in the PDB repositories. We selected 6PZ4  to maintDocking is a molecular modeling method that involves predicting the preferred orientation of one molecule when bound to another molecule . It requ−2 constraint to restrict nonphysical movements of heavy atoms during minimization.We used SwissDock online docking software ,57 to seMD MD simulation is an approach in computational modeling to explore the conformational dynamics of molecules . MoleculWe performed MD simulations on the AMBER 18 program package integrated with graphics processing units (GPU)  using th−2 harmonic restraint on all heavy atoms to relax the system and remove potential atom clashes. We also run a full minimization with another steepest and conjugate gradient descents at 5000 steps each without constraints. Systems heating was from 0 K to 300 K for 300 ps using Langevin dynamics, 1 ps collision frequency, and a 5 kcal/mol.Å−2 applied harmonic restraints at a constant volume. We subsequently equilibrated for 500 ps at 300 K under constant pressure and temperature (NPT) ensemble. The final step is MD simulation for 120 ns at 2 fs time step without any restraints at 300 K and 1 atm in the NPT ensemble switching on the Langevin temperature scaling [The partial minimization was at 10,000 steepest descent steps with 10 kcal/mol.Å scaling  and Bere scaling  algorith scaling  to consti as Cα coordinates in v at the time i, and wi is the coordinates of Cα atom in w at the time i.We measured system stability through root-mean-square deviation (RMSD) calculation. The RMSD trajectory of the protein backbone alpha carbon (Cα) generated with the CPPTRAJ  module ui represents the atomic position, and rm signifies the mean position of all atoms.The radius of gyration (RoG) is the moment inertia of atoms from their center of mass. RoG is often applicable in quantifying the molecular rigidity of a system . The RoGi(j) represents the i-th Cα atom position in the j-th model structure, and (xi) denotes the averaged location of the i-th Cα backbone atom in all models.We estimate the root-mean-square fluctuation (RMSF) to predict the conformational changes on a per-residue basis. Equation (3) shows the RMSF equation whereby xFurther analyses include secondary structure prediction with the definition secondary structure of protein (DSSP) approach of Kabsch and Sander  implemenLY2811376 poses on the BACE1 flap tip as the only identified allostery site. The observation is consistent with previous reports [LY2811376 modulatory and inhibitory potentials. All the predicted sites are feasible subsites as suggested in a survey of ligand-binding modes of co-crystallized BACE1 structures [Blind molecular docking with SwissDock generated different binding poses at various sites on the BACE1 protein structure. All the compounds  show fav reports ,39 on LYructures .1 shows an interaction at eight different subsites on the BACE1 scaffold  captioned as scaffold , therebyscaffold  with unier Waals .1 has the most favored full fitness of its moieties in the BACE1 active site. Its binding energy to other binding regions within the enzyme ranges from −6.12 to 7.10 kcal/mol with favorable fitness scores  to identify the effect of the studied molecule at the various regions on BACE1 per residue. RMSF is a crucial concept to evaluate protein dynamics and individual residue flexibility [1 binding  at residues 164–167.We propose that the allostery mechanism of compound  1.348 Å , with al7 and 8 with average RoG of 21.5 and 21.4 Å, the approximate average RoG value is 21.2 Å (7 facilitates slightly higher RoG (7 BACE1 model.The mass-weighted radius of gyration (RoG) is a moment of atom inertia from their center of mass . The cals 21.2 Å . These vgher RoG  like itsgher RoG . The dat7 shows the highest data in all the parameters, while site 5 shows the least mean RoG score  bind through several other BACE1 residues. The allostery and binding mechanisms of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors, while exploring the opportunities in the design of allosteric BACE1 inhibitors. We further suggest that diverting attention from the conventional active site through a detailed computer-aided drug design approach would assist in designing a potential BACE1 inhibitor that might attain the approval stage for AD management.Allosteric inhibition is a type of noncompetitive inhibition occurring at another protein site different from the active site. In this mechanism, the inhibitor binds to a site(s) other than the active site, thereby rendering the active site unfit for the substrate to bind. In allosteric inhibition, it is a case in which the moiety gets to the enzyme or protein first, which blocks the other from binding. As a sequel to our previous review study on some identified allosteric inhibitors and exosite-binding antibodies of BACE1 over the last 8 years (2013–2020), we, hereby, in this study applied CADD methods to further validate the claims as contained in the reviewed study. We selected six compounds that were reported to bind on sites different from the BACE1 active site. Some of these compounds are classified as meroterpenoids (Sargahydroquinoic acid and Gamma-Linolenic acid) and psychotic drugs (Anisoperidone). Good knowledge of the inhibiting potentials and properties of BACE1 inhibitors also serves as a prerequisite for the development of molecules with high selectivity and specificity. We show in this study that besides the active site, BACE1 targeting at other subsites is plausible towards AD therapy. From the results of the molecular docking, molecular dynamic (MD) simulations, and subsequent post-MD analyses, the identified regions correspond with some predicted subsites and the small molecules (like compound


Phosphorylation of cytoskeletal proteins regulates the dynamics of polymerization, stability, and disassembly of the different types of cytoskeletal polymers. These control the ability of cells to migrate and divide. Mutations and alterations of the expression levels of multiple protein kinases are hallmarks of most forms of cancer. Thus, altered phosphorylation of cytoskeletal proteins is observed in most cancer cells. These alterations potentially control the ability of cancer cells to divide, invade and form distal metastasis. This review highlights the emergent role of phosphorylation in the control of the function of the different cytoskeletal polymers in cancer cells. It also addresses the potential effect of targeted inhibitors in the normalization of cytoskeletal function. Cytoskeletal proteins form the backbone of the different types of structural polymers found in every eukaryotic cell. Such polymers include microfilaments (MF), mini-filaments, microtubules (MT) and intermediate filaments (IF). Each polymer has a relatively homogeneous composition. Monomeric cytoskeletal proteins bind in a head-to-tail manner to form long chains of different geometries and biophysical properties. These monomers include actin (which forms MF), myosin (mini-filaments), tubulin (MT), and various families of IF proteins, including keratins, desmins, glial fibrillary acidic protein (GFAP), peripherin, vimentin, internexins, nestins and others (reviewed in ). MFs anLike every protein in the eukaryotic proteome, cytoskeletal proteins are substrates of diverse protein kinases. Phosphorylation changes their interactive and dynamic properties with respect to their non-phosphorylated forms. In the specific case of cytoskeletal proteins, phosphorylation controls their assembly and disassembly affinity and dynamics, as well as the biochemical and biophysical properties of the polymers themselves. Phosphorylation also modulates their interactome, which also affects the stability and function of the polymers.Genetic modifications affecting protein kinases are very frequent in cancer . The mosThese and other examples found in the latter sections of the present work highlight the fact that alterations of the phosphorylation of cytoskeletal proteins caused by cancer-related mutations may change the dynamics, architecture, and function of the different cytoskeletal polymers. These events cause aberrant molecular behaviors that may confer specific properties to cancer cells, e.g., increased cell migration, invasion, division, mechanosensing, etc.The study of these modifications is complicated by the existence of multiple isoforms of these proteins, some of which have non-overlapping functions. There are many isoforms of actin, tubulin and myosin, and multiple forms and variants of IF. How phosphorylation impacts every isoform of every cytoskeletal protein is not only impossible to describe, but mostly unknown. Because of this, we describe only the current state of the art regarding the phosphorylation of selected isoforms of actin, myosin II, tubulin and vimentin. We focus on the functional effect of these phosphorylations, and what the consequences would be if the extent of these phosphorylations was altered in the context of cancer progression. While phosphorylation has proven crucial for the function of some of these filament-forming polymers, e.g., myosin II, the function of many of the phosphorylations described here is still unexplored. Thus, a major goal of this work is to provide a wide, yet incomplete, perspective of the field, identifying potential hotspots that may be amenable to specific targeting to treat diverse forms of cancer.Actin forms MFs by adenosine triphosphate (ATP)-dependent polymerization. Together with myosin II mini-filaments, MFs constitute the contractile apparatus of animal cells. A vast array of nucleators, cross-linkers and other binding partners regulate multiple aspects of its ability to form filaments and the multiple cellular functions they enable , 7.http://phosphosite.org, search term = ACTB, Ser33). Its phosphorylation lies downstream of polo-like kinase 1 (PLK1) since the specific inhibitor BI 4834 abrogates it  and four light chains, two regulatory (RLC) and two structural . There are three genes that encode MHCII isoforms: MYH9 , MYH10  and MYH14 ; three genes that encode RLC: MYL9 , MYL12A  and MYL12B ; and one gene that encodes ELC, MYL6 . The typical structure of NMII involves MHCII from the same gene forming a central homodimer (they do not heterodimerize). Each heavy chain contains two tandem IQ motifs that bind to ELC and RLC. These binding sites define the “neck” of the hexamer, which is flexible and enables the conformational movement that generates mechanical work upon ATP hydrolysis when the hexamer is bound to actin . Actin bMYH9/10/14).Whereas binding to calcium-sensitive proteins controls muscle myosin II function, NMII is largely controlled by phosphorylation. In fact, RLC phosphorylation is essential for the conformational extension that is required for NMII hexamers to form mini-filaments . LikewisRLC is the most important regulatory hotspot of myosin II by phosphorylation. Several residues have been described, including Ser1/2. Their phosphorylation inhibits NMII function, as they decrease ATP catalysis on the NMII hexamer head . Althougin vitro effect boosting ATP catalysis of the bound MHCII, Thr18 is considered a synergy site with Ser19. It also has increases the half-life of NMII mini-filaments, which is essential during cell migration [Conversely, Ser19 phosphorylation mediates the conversion of folded, assembly incompetent into extended, assembly competent NMII hexamers. Extended hexamers whose RLC is phosphorylated in Ser19 immediately form bipolar filaments that grow by lateral association, as outlined below . Ser19 pigration . Severaligration .Very recently, we have identified Tyr155 phosphorylation downstream of EGFR. However, this phosphorylation only occurs when RLC is not bound to NMII. Tyr155 phosphorylation prevents the association of RLC with NMII, thus de facto decreasing the amount of NMII available to form filaments .On the other hand, the role of ELC phosphorylation in cellular physiology or NMII function remains practically unexplored. Tyr29 appears more phosphorylated in many types of cancer, but the kinase remains unidentified . ConversRegarding the heavy chains, there are isoform-specific differences regarding heavy chain phosphorylation that have potential effects on diverse types of filaments depending on their molecular composition. In general, head domain phosphorylations have the potential to control actin binding and ATPase activity of myosin II. However, this has been poorly explored. Conversely, phosphorylations of the coiled coil domains of MHCII-A/B/C are better characterized. These regions are important for dimerization, and phosphorylation has been shown to decrease the stability of the hexamers and hinders their lateral association with other hexamers to form filaments. This revealed that lateral interactions are highly dependent on the net charge of the interacting regions . PhosphoFinally, phosphorylations at the end of the coiled-coil or into the NHT domain of MHCII decrease mini-filament formation. One such residue is Ser1916 in MHCII-A. Its phosphorylation by PKCβ reduces filament stability , 45 as iThere are three isoforms of tubulin, each one including several variants. For the sake of brevity, and due to the grouped homology among them , we focuOn the other hand, tubulin phosphorylation in Ser165 by PKCα increases MT dynamics, cell motility and acquisition of a mesenchymal phenotype characterized by expression of neural (N)-cadherin . This stFinally, phosphorylation of centrosomal γ-tubulin in Ser131/385 is mediated by SadB. This phosphorylation is involved in centrosome duplication during mitosis, a key event during cell division. A phospho-mimetic form induces spontaneous centrosome duplication, whereas a non-phosphorylatable mutant impairs centrosome duplication . SadB alVimentin (UniProt # P08670) is located on human chromosome 10p13. Vimentin forms very stable IF. Unlike actin, myosin and tubulin, IFs are not very dynamic, but they endow cells with structural stability. Importantly, vimentin is an IF typical of mesenchymal and hematopoietic cells, and a signature gene of the epithelial-mesenchymal transition (EMT) . As suchN-terminus domain [Vimentin undergoes extensive phosphorylation, which potentially controls its cellular function. The best characterized phospho-residues are grouped in the non-helical s domain   protects vimentin from proteolysis and enhances tumor growth and metastasis  by alterOn the other hand, Ser56 is phosphorylated by the cyclin dependent kinase (CDK) 1. This phosphorylation recruits PLK1, which enhances phosphorylation in Ser82 . Consequ+ T cells distinguish between non-phosphorylated vs. phosphorylated vimentin peptides [Importantly, phosphorylation of Ser39, Ser72 and Ser83 is preserved when vimentin is processed and peptides presented associated to major histocompatibility complexes. CD4peptides . Since tpeptides , they coTubulin phosphorylation is arguably the least understood of cytoskeletal phosphorylations. The apparent lack of interest in the field may be due to the fact that early use of anti-tubulin polymerization drugs such as colchicine or vincas; or MT turnover inhibitors, e.g., taxanes, was very successful to inhibit mitosis in cancer cells . The scaAn intriguing possibility is to target tubulin phosphorylation to complement vinca or taxane treatments, particularly to counteract the development of resistance, which is observed in many forms of advanced cancer. The assumption is that taxane-resistant cells develop mechanisms to undergo mitosis in the presence of these inhibitors, overcoming G2/M arrest, e.g., in the presence of hypoxia . An earlCompared to their central role in cell division, the role of MTs in cell migration is more controversial. Thus, whether inhibiting their phosphorylation could have an effect on invasion is uncertain. MTs control cell polarity  and presin vitro to favor growth and prevent differentiation [Actin and myosin are also important for mitosis. Different studies have shown that inhibiting MYH10 expression promotes multinucleation due to cytokinesis failure , 87. Sinntiation –93). ThiOn the other hand, targeting NMII (and actin) phosphorylation could prevent tumor cell dissemination. Different lines of evidence have suggested that elevated NMII phosphorylation and activity in the cortex changes the cellular phenotype from epithelial, or mesenchymal, into amoeboid , 95. AmoHowever, the different levels of regulation dependent on NMII phosphorylation are only beginning to be understood. This is a very active line of investigation in our lab.Vimentin knockout mice are viable and fertile, displaying only minor developmental defects . Other tVimentin is prominently expressed in cells that are (or become) motile, including fibroblasts, mesenchymal cells, leukocytes and invasive cancer cells. This has suggested that vimentin could be targeted to prevent cancer cell motility. Indeed, vimentin is upregulated during EMT, which is a common occurrence in many carcinomas, and its repression in these cells decrease breast and colon tumor cell migration, as shown by siRNA depletion of vimentin in migrating mesenchymal cells and overexpression of vimentin in epithelioid, non-migrating tumor cells , 103. InRegarding vimentin phosphorylation, some sites favor IF assembly, whereas others promote disassembly, hence it is difficult to make general statements regarding the effect of its phosphorylation in cancer progression. An interesting fact is that many mitotic kinases, e.g., CDKs, PLK1, AURK, induce vimentin phosphorylation, hence its phosphorylation is likely to favor cell division. A model emerges in which elevated vimentin phosphorylation promotes IF disassembly, favoring cancer cell division. Likewise, recent evidence indicates that IFs may promote tumor cell migration , hence iWhile a few cytoskeletal components, e.g., myosin II, have well-defined kinomes, most of them lack specific kinases. However, most of the reported effects on actin, tubulin and vimentin phosphorylation in cancer cells emerge from studies using kinase-specific drugs. Hence, we cannot rule out that some specific phenotypes caused by current treatments are related to cytoskeletal phosphorylations. A key example is that of paclitaxel, which induces mitotic arrest at G2/M, promoting vimentin phosphorylation in Ser56 via CDK1 .+ALL) with several potential targets [A few years ago, we demonstrated that dasatinib, a Tyr kinase inhibitor used to treat CML and Philadelphia chromosome-positive acute lymphoblastic leukemia  are very contractile, likely displaying elevated levels of phosphorylated NMII. NMII phosphorylation in these cells induces the formation of stress fibers, which predicts contact guidance for surrounding breast cancer cells . TherapiTargeting cytoskeletal phosphorylation has the potential to dramatically alter the mechano-chemical properties of tumor cells, inhibiting their ability to develop the cancer program, at least at a preclinical level. However, these approaches may also have potentially severe side effects, as every cell, normal or cancerous, requires the cytoskeleton. MT inhibitors, discovered over 60 years ago, offered early promise as MT-targeted therapies that improved the outcome of many types of cancer by inhibiting tumor cell division. These therapies are still the first line of chemotherapeutic treatment in many types of cancer. Hence, it is evident that phosphorylation of cytoskeletal components is altered when patients are treated with kinase-targeted therapies. We have only scratched the surface in characterizing these effects. Many of them will be unavoidable consequences of treatments aimed at other fundamental processes, potentially causing side effects that will have to be dealt with. But the potential exists to discover that some cytoskeletal phosphorylation-specific effects underlie unexpected and important effects of current and future targeted therapies.


Detecting small moving targets against a cluttered background in visual data is a challenging task. The main problems include spatio-temporal target contrast enhancement, background suppression and accurate target segmentation. When targets are at great distances from a non-stationary camera, the difficulty of these challenges increases. In such cases the moving camera can introduce large spatial changes between frames which may cause issues in temporal algorithms; furthermore targets can approach a single pixel, thereby affecting spatial methods. Previous literature has shown that biologically inspired methods, based on the vision systems of insects, are robust to such conditions. It has also been shown that the use of divisive optic-flow inhibition with these methods enhances the detectability of small targets. However, the location within the visual pathway the inhibition should be applied was ambiguous. In this paper, we investigated the tunings of some of the optic-flow filters and use of a nonlinear transform on the optic-flow signal to modify motion responses for the purpose of suppressing false positives and enhancing small target detection. Additionally, we looked at multiple locations within the biologically inspired vision (BIV) algorithm where inhibition could further enhance detection performance, and look at driving the nonlinear transform with a global motion estimate. To get a better understanding of how the BIV algorithm performs, we compared to other state-of-the-art target detection algorithms, and look at how their performance can be enhanced with the optic-flow inhibition. Our explicit use of the nonlinear inhibition allows for the incorporation of a wider dynamic range of inhibiting signals, along with spatio-temporal filter refinement, which further increases target-background discrimination in the presence of camera motion. Extensive experiments shows that our proposed approach achieves an improvement of 25% over linearly conditioned inhibition schemes and 2.33 times the detection performance of the BIV model without inhibition. Moreover, our approach achieves between 10 and 104 times better detection performance compared to any conventional state-of-the-art moving object detection algorithm applied to the same, highly cluttered and moving scenes. Applying the nonlinear inhibition to other algorithms showed that their performance can be increased by up to 22 times. These findings show that the application of optic-flow- based signal suppression should be applied to enhance target detection from moving platforms. Furthermore, they indicate where best to look for evidence of such signals within the insect brain.The online version contains supplementary material available at 10.1007/s00422-022-00950-9. Small target detection in visual scenes has attracted significant research attention owing to its applications in a wide range of areas such as search and track . The possible key stages are selected based on our careful examination of their responses to the inhibition signal under simulated camera motion. Additionally, we also look at adding a new layer of nonlinear conditioning to the motion inhibition signal as well as tuning some of the optic flow filters specifically for the purpose of small target detection. Our explicit use of a compressive nonlinearity allows for the incorporation of a wider dynamic range in the inhibiting signal along with spatio-temporal refinement which further increases target-background discrimination in the presence of camera motion. Finally, we look at using the conditioned motion signal and apply it as an inhibitor to the output of other algorithms to see how their performance can also be improved.Background subtraction methods are often used to find larger moving objects within an environment; however when large amounts of motion are induced by a moving platform, performance can degrade   and lamina monopolar cell (LMC), both based off work by Van Hateren  cells, based on work by Hassenstein and Reichardt , and the medulla lobula interneuron (MLI) cells. While not physically modelled these stages have strong neurophysiological support for their existence , based on work by Jansonius and Van Hateren , and the elementary small target motion detector (ESTMD) neurons, based on a modified EMD. The RTC is one of the most important neurons in the target detection pipeline and, following electro-physiological recordings from fly brains, was originally modelled explicitly for this purpose  panoramic environments were chosen. The HDR images ensured that the data being worked with was representative of the real-world environment without any quantisation or compression artefacts, which often occur in images designed for human viewing (a creative process), and exist in the majority of currently available datasets. This allows the BIV’s native information enhancing compression techniques of the PRC and LMC to be used to full effect.The 20 natural images  had varf2 Field . The bacEach background had a different quantity of high-frequency spatial clutter, which is hypothesised to be one of the main effectors in pixel sized target detection. For this research, only the green channel of the panoramas was used as this closely represents the luminance in a scene and aligns with previous work. 6 of the backgrounds were used as a training set to find the best operating parameters for the feedback. These 6 backgrounds plus 2 others for reference can be seen in Fig. The background imagery was down-sampled to a size of i) basis using Eq. c) was chosen to be the mean of the local motion output (0.05) at a background and target speed of 29 pixels/s as it was expected this would provide an initial operating point for all speeds (faster and slower). A gain (G) was used to observe the effects of under- and over-saturating the top end roll-off of the saturating nonlinearity. The conditioned inhibiting signal was subtracted from unity in order to produce a signal that approached 1 when there was no recorded local motion and approached 0 when there was a large amount of local motion, and hence higher probability of false detections. This inhibition map was then multiplied with the signal at the corresponding inhibition location (x) to give the inhibited output (y)  of the RTC to adjust the derivative threshold based on the local clutter level but before rectification at the end of the RTC. Since location B served as an adjustment to the thresholding operation, it was used in conjunction with inhibition at location A. As the inhibition at location A could suppress the signals, the change over time from a target may no longer be large enough to trigger the fast adaptation for rising signals due to the static threshold. To adapt to this, adjusting the threshold using inhibition at location B allowed those smaller changes to produce the correct rising responses through the RTC.Location C placed the inhibition after the output of the RTC and at the input to the ESTMD. This suppressed the two rectified channels, differing from location A as two impulses that follow closely would interact at full strength, suppressing any secondary impulse. Inhibition at A had the ability to suppress the initial impulse before it interacted with the secondary impulse, reducing the amount of suppression on the secondary impulse which may have been a target. At location C the inhibition also has to be applied to the two rectified channels separately, potentially reducing efficiency.Location D applied the inhibition to the output of the BIV model following the correlation of the two processing branches, effectively a post-processing method, i.e. it acted as a variable local threshold for determining what is a possible target and what is not. Inhibition at location D was equivalent to that used in . This made the dark targets bright and allowed the algorithm to be used without further modification. This sequence was repeated for all 20 backgrounds, 4 target speeds and 4 background speeds, for a total of 320 scenarios for each algorithm. To observe the effects of inhibition on FKRW and LCM, the statically conditioned inhibition signal was obtained from the MLI, subtracted from unity, and then multiplied with the method’s raw result on a frame-wise basis.Strictly speaking, all algorithms used really performed target enhancement, not target detection. A subsequent thresholding operation was required to take the saliency maps produced by the algorithms and determine what components would be classified as targets. In order to perform this thresholding operation a winner takes all algorithm was used with a 7To measure and compare the target detection performance of the algorithms, the AUROC curve was used  performance for each background can be seen in Fig. Initial results from the tuning data showed positive increases in performance. Figure c was expected as indoor scenes typically have sharp  edges that come from manufactured objects such as walls and windows, while much of the environment is uniform and flat, resulting in lower local optic flow signals overall. Thus, to make the most of the available dynamic range, a steeper slope on the conditioning Naka-Rushton is required. Scenes without man-made objects performed best when Separating the scenes with manufactured structures from those without gave distinct operating points for best performance. Scenes with manufactured structures received the best AUROC score with Without local optic flow estimations, the algorithms were only able to separate targets from the background in a single dimension, using a threshold on the salience maps. Introducing an inhibiting signal based on local area motion allowed for a second dimension to help separate targets from the background. Figure  A in Fig. A can be seen in Fig. Traditionally, all background and target detections would exist on the x-axis, the cumulative distribution function (CDF) for which can be seen in Fig. A and B together  to adjust the threshold which determines whether a temporal change over a pixel requires a fast or slow adaptation state whenever inhibition occurs earlier in the model. Without inhibition at locations A and B, some target signals were suppressed to the point where they were smaller than the required threshold for a fast adaptation state within the VLPF and no longer produced the transients required for small target detection.An examination of the pooled performance based on using the different inhibition locations and the entire training set of backgrounds showed that the best median performance was obtained by applying inhibition at both locations see Fig. . Median A and B as a benchmark and comparing results for each condition, paired t tests were performed on the results  except for BIV ’22 with inhibition at location D, where no significant difference was measured. However, the p value was only non-significant following a Bonferroni post hoc correction indicating that there may be a slight difference between the two. The mean difference between the two datasets on a per-simulation basis, suggests that the performance of BIV ’22 with inhibition at A and B is generally larger than BIV ’22 with inhibition at D but further investigation is required to determine if this difference has any practical relevance.Using BIV ’22 with inhibition at location c was observed. Figure c value with a G value of 1.1. The line of best fit is also shown and resulted in Eq. c based on an estimate of global motion.From individual training background results, a trend between background speed and the value of Over the training backgrounds, tuning the MLI corner frequency showed large performance improvements over the original published value (0.453 Hz). Performance improvements began to taper off with a corner frequency value between 3.0 and 4.0 Hz, to a 18% and 19% performance increase from the original value, respectively. Figure A Fig. The introduction of nonlinear inhibition showed great improvement in detection performance, especially for backgrounds containing physically large man-made features. Figure A further improved performance, obtaining 187 TPs: a 73% increase over BIV ’08 and 32% increase over BIV ’19; and an insight into the effects using linear versus nonlinear conditioning.BIV ’08 obtained 108 TPs, while BIV ’19 obtained 142 TPs: a 31% improvement. BIV ’22 with inhibition at location A and B obtaining 189 TPs. The inclusion of the tuned MLI temporal filter (corner frequency of 4.0 Hz) further improved the number of detections to 218 TPs: an 15% increase over BIV ’22 without MLI tuning, and a 102% and 53% increase over BIV ’08 and BIV ’19, respectively.Figure Figure Comparing the maps of linear and nonlinear inhibition  can be beneficial as the background signals dominate the target signals. As a result, an increase in suppression is more likely to reduce FPs than TPs. However, in a real-world application it may be difficult to know what the target speed is ahead of time, making it hard to optimise such criteria. When targets are moving much faster than the background, such as targets at 50 pixels/s and background at 10 pixels/s, BIV ’08 performs best. This is because the temporal energy of the targets alone is sufficient to separate them from the background. Under this condition, BIV ’22 with dynamic inhibition performed the worst out of all BIV methods due to its reliance on background velocity estimation.Performance of BIV ’08 was larger than FKRW, and LCM under most conditions , BIV ’08 provided the best detection rates. This is because the background signals interfere less with the target signals when compared to more cluttered scenes. In other words, any BIV ’22 inhibition introduced likely suppressed the targets more than the false positives. Nonlinear inhibition improved performance most on scenes containing man-made elements Table , noting In general, when not including target speeds of 10 pixels/s, BIV ’22 with dynamic inhibition performed the best. When aggregating results for all backgrounds Tables  and 4 BIA and B with MLI tuning performs significantly better than all other methods except for BIV ’22 with dynamic inhibition, for which there is no significant difference in performance . Unfortunately, FKRW does not retain such information. That said, it is believed that using optic-flow inhibition after the facet kernel, but before the adaptive threshold, could produce better results. LCM’s improved performance comes from every pixel having a probability associated with it based on its degree of contrast, which allows the inhibition to allocate higher probability to targets in areas of little clutter while reducing it for those in regions of high clutter.Biological explorations have previously looked at global motion feedback within the vision of insects Egelhaaf ; howeverIn this study, applying a biologically plausible, dynamic, nonlinear inhibition mechanism further improved the small target detection capabilities of the BIV model relative to our earlier work. The current model has a general median AUROC 2.33 times that of BIV ’08 and 1.25 times that of BIV ’19. Performance over other methods were consistent with previous studies  is preferred as this offers consistent global motion estimates over the entirety of a scene. This suggests that, not only are the target speeds an important factor in the performance of the BIV, but so too is the internal construction of a scene. Additional analysis is needed to determine what in a scene’s structure could be used to generate a more robust dynamic inhibition signal. It may also be possible to obtain further performance increases by modifying the spatial filter in the MLI. However, it is expected any such improvements would likely be minor compared to tuning the temporal filter as smaller kernels are expected to suppress targets more in areas of low clutter while larger kernels may not create a feedback map with sufficient detail to suppress smaller areas of clutter. As this study focused on analysing inhibition locations and the effects over entire scenes, further studies may also be warranted to look at the inhibition location performance based on localised areas of clutter. This could give a deeper understanding of what happens to the detection of both targets and clutter in these regions, leading to new inhibition schemes.Initial tuning of the inhibiting signal found that for more natural scenes a larger value of Inhibition on other algorithms (such as LCM) can be beneficial. However, this relies on it being applied before decisions are made on what is or is not a target. In other words, optic-flow inhibition could be applied to almost any algorithm to provide improved performance so long as the algorithm provides the probability of a target’s existence , and not just a binary outcome regarding detection. Without this, inhibition will likely offer little or no benefit.A + B). It also showed that combining these two feedback locations offered greater performance over the individual locations tested in this study. However, the performance difference between BIV ’22 with inhibition at locations A and B and BIV ’22 with inhibition at location D was not found to be significant. Neurologically, both locations are be plausible; however, further studies would need to be undertaken to see if evidence for either one is supported biologically. The authors believe that BIV ’22 with inhibition at locations A and B may be the most logical location as all information is available at the start of the RTC, allowing for more a higher degree of sensitivity, whereas later in the model, decisions have been made and information reduced.This study investigated local motion feedback points within the BIV model. It showed that performance gains could be obtained by introducing inhibition concurrently into the beginning of the RTC and the VLPF in the RTC  and global (as a measure of ego-motion) optic flow can be nonlinearly processed and used to suppress false positives when attempting to detect pixel sized targets in cluttered scenes from moving platforms.Supplementary file 1 (pdf 5171 KB)Below is the link to the electronic supplementary material.


Dryopteris crassirhizoma—exhibit various pharmacological effects, such as antiviral, antioxidant, and antidiabetic activities. This study evaluated 30 phloroglucinols isolated from the rhizomes of D. crassirhizoma for their inhibitory activity on β-glucuronidase via in vitro assays. Among them, dimeric phloroglucinols 13–15 moderately inhibited β-glucuronidase, and trimeric phloroglucinols 26–28 showed strong inhibitory effects, with IC50 values ranging from 5.6 to 8.0 μM. Enzyme kinetic analysis confirmed all six active compounds to be in a competitive mode of inhibition. Molecular docking simulations revealed the key binding interactions with the active site of β-glucuronidase protein and the binding mechanisms of these active metabolites. Our results suggest that the rhizomes of D. crassirhizoma and trimeric compounds 26–28 may serve as potential candidates for discovering and developing new β-glucuronidase inhibitors.Phloroglucinols—one of the major secondary metabolites in  The glucuronides of several clinical drugs, such as indomethacin and irinotecan, may be degraded by β-glucuronidase from bacteria in the intestinal tract to produce aglycones with considerable toxicity and cause significant gastrointestinal side effects [β-glucuronidase inhibitors has now focused on treating irinotecan-induced gastrointestinal diarrhea. In addition, a previous report revealed that the occurrence and development of tumor cells, including survival, proliferation, invasion, and metastasis, are closely correlated with β-glucuronidase activity [β-glucuronidase is higher in tumor cells than in normal cells, including breast, ovary, lung, and colon tumor cells [β-glucuronidase has also been considered a potential biomarker for the clinical diagnosis and treatment of cancers.tabolism . The Caronidases . β-Glucue system . The fir in 1996 , followe in 2010 . β-Glucuor cells . Thus, βDryopteris crassirhizoma, a member of the Dryopteridaceae family, is a pteridophyte. Its rhizomes are one the most widely used traditional herbal medicines in Northeast Asia and China. They have been used to treat parasite infestation, hemorrhage, and common cold [D. crassirhizoma was originally employed as an antihelminthic herbal treatment against Diphyllobothrium latum and as an anti-infection agent to cure common cold and flu [D. crassirhizoma has recently been reported to exhibit antiviral, antiplatelet, antioxidant, antibacterial, and antitumor activities [D. crassirhizoma are phloroglucinols and their derivatives, which demonstrate various pharmacological effects. Approximately 50 phloroglucinols have been isolated and structurally elucidated from D. crassirhizoma, and can be divided into five groups based on the number of monomers in their chemical structures [50 values of 6.3–20.9 μM [50 values of 35.7 and 38.9 μM, respectively [50 values of 29.6 and 51.5 μM, respectively [D. crassirhizoma have been studied, the inhibitory activity of phloroglucinols on β-glucuronidase has not been reported.From ancient times to the present day, natural products have played a key role in the discovery of new drugs, and the research, investigation, and development of active metabolites from natural herbal products are and will continue to be important sources of potential therapeutic agents for use against various diseases ,10. Dryomon cold . D. cras and flu ,13. D. ctivities ,16,17,18ructures . Among tectively . In receectively . Furtherectively . AlthougD. crassirhizoma rhizomes [1), 1--propan-1-one (2), phlorobutyrophenone (3), 5-acetyl-2,4,6-trihydroxyacetophenone (4), 1,3-dipropanone (5), 1,3-dibutanone (6), 1-propanone (7), 1- butanone (8), 1- pentanone (9), tripropionylphloroglucinol (10), tributyrylphloroglucinol (11), bis-phlorobutyrophenone (12), dryopidin PB (13), methylene-bis-phlorobutyrophenone (14), araspidin BB (15), norflavaspidic acid AP (16), norflavaspidic acid AB (17), norflavaspidic acid PB (18), norflavaspidic acid BB (19), flavaspidic acid AA (20), flavaspidic acid AP (21), flavaspidic acid AB (22), flavaspidic acid PB (23), albaspidin AA (24), dryopcrassirine AB (25), nortrisflavaspidic acid ABB (26), trisflavaspidic acid ABB (27), trisflavaspidic acid BBB (28), filixic acid ABA (29), and dryocrassin ABBA (30)  . All iso acid PB , albaspiD. crassirhizoma, in this study, all the phloroglucinols isolated (1–30) were evaluated for their in vitro β-glucuronidase inhibitory effect. Kinetic analysis and molecular docking studies were also performed to understand the inhibition type, binding efficiency, and binding interaction of the active compounds with β-glucuronidase.As part of our ongoing study to investigate the biological activity of secondary metabolites from 1–30) against β-glucuronidase was evaluated using 4-nitrophenyl β-d-glucuronide (PNPG) as a substrate. The results are expressed as IC50 values, depicted in β-glucuronidase (IC50 >100 µM) for monomeric phloroglucinols (1–10), except for tributyrylphloroglucinol (11) with three butyryl groups in the side chain, which showed moderate β-glucuronidase inhibitory effect with an IC50 value of 43.0 ± 2.9 µM. These results suggest that the number of butyryl groups in the chemical structures of phloroglucinols may significantly influence the inhibitory effect of β-glucuronidase.The inhibitory effect of all isolated phloroglucinols (13–23) exhibited more potent β-glucuronidase inhibition than monomeric phloroglucinols. Among them, compounds 13, 14, and 15 with at least one butyryl group linked to phenol rings exhibited a strong inhibitory effect on β-glucuronidase with IC50 values of 18.1 ± 1.5, 14.4 ± 0.6, and 17.0 ± 2.5 µM, respectively, which were higher than those of the positive control d-saccharic acid 1,4-lactone . The other dimeric compounds showed weak or no β-glucuronidase inhibitory activity .Dimeric phloroglucinols (26–28) displayed the strongest inhibitory effect against β-glucuronidase. In vitro assessments revealed that nortrisflavaspidic acid ABB (26), trisflavaspidic acid ABB (27), and trisflavaspidic acid BBB (28), with two butyryl side chains linked to phenol rings in phloroglucinol trimeric structures, significantly exhibited PTP1B [β-glucuronidase inhibitory activity with IC50 values of 8.0 ± 1.8, 7.1 ± 2.6, and 5.6 ± 1.1 µM, respectively, which was more potent than that of the positive control DSA. Compounds 26–28 with two phloroglucinol units linked with a 2,5-cyclohexadienone ring exhibited a much stronger β-glucuronidase inhibitory effect than trimeric compound 29 with only one phloroglucinol unit linked with two 2,5-cyclohexadienone rings (IC50 = 90.5 ± 2.7 µM), suggesting that the number of phloroglucinol units in the chemical structure of phloroglucinols may positively affect its β-glucuronidase inhibition. Interestingly, this suggestion is fully consistent with what was observed in dimeric group. The β-glucuronidase inhibitory effect of tetrameric phloroglucinol dryocrassin ABBA (30) (IC50 = 94.9 ± 2.5 µM) was decreased compared to that of the trimeric group. Structure–activity relationship analysis suggested that the trimeric skeleton of phloroglucinol plays a vital role in its β-glucuronidase inhibitory effect.Among all the isolated compounds, trimeric phloroglucinols (ed PTP1B . In thisiK), kinetic analyses were performed at various concentrations of the PNPG substrate and inhibitors (13–15 and 26–28) using the Lineweaver–Burk and Dixon analysis methods [x- or y-axes, respectively. As shown in y-axis or 1/V axis. These results indicate that the inhibitory behavior of active compounds 13–15 and 26–28 toward β-glucuronidase was a competitive type of inhibition. According to the Dixon plots, the iK values of compounds 13–15 and 26–28 were 6.3, 4.3, 25.8, 7.5, 0.5, and 2.8 μM, respectively.To investigate the types of enzyme inhibition and inhibition constants . The results were visualized using BIOVIA Discovery Studio 21.1  and PyMOL 2.5 . The kinetic results suggest that all test compounds displayed a competitive type of inhibition. Therefore, compounds 13–15 and 26–28 could bind to the active site of β-glucuronidase, where the substrate molecule binds and carries a chemical reaction. Thus, PNPG was docked as a native ligand into the β-glucuronidase enzyme (PDB ID: 6LEL) to validate and optimize the docking operation. The docking results of the substrate and active compounds, as well as the active site of the β-glucuronidase enzyme, are presented in To understand the binding efficiency and interaction of active compounds 13–15 could bind to the active site of β-glucuronidase, with binding energy values of −8.22, −8.49, and −8.02 kcal/mol, respectively  consists of two monomers with 597 amino acid residues and is divided into three main regions: the N-terminal (180 residues) resembles the sugar-binding domain of the GH2 family, the C-terminal domain (residues 274 to 603) forms an αβ barrel and has two catalytic residues in the active site—Glu413  and Glu504 —and the region between the N- and C-terminal domains has an immunoglobulin-like β-sandwich domain similar to other members of the GH2 family [E. coli β-glucuronidase, which is missing from the human orthologue, was reported to be a key factor for the complete activity of β-glucuronidase and its selective inhibition [uronides . The asyresidues 4 to 603 D. crassirhizoma that are potential natural therapeutic agents for inhibiting β-glucuronidase, determined via a combination of in vitro assays and virtual screening techniques.Virtual screening based on molecular docking has been the most popular method for structure-based drug discovery since 1982 . Researc1–30) were evaluated for β-glucuronidase inhibitory activity. Of these, dimeric compounds 13–15 showed moderate effects on β-glucuronidase, with IC50 values ranging from 14.4 to 18.1 μM. Trimeric compounds 26–28 exhibited strong inhibitory effects on β-glucuronidase, with IC50 values ranging from 5.6 to 8.0 μM. In comparison, the monomeric group showed weak or no β-glucuronidase inhibition activity . Examination of structure–activity relationships among the active compounds suggested that the presence of three aromatic rings in the chemical structure of phloroglucinols is important for potent β-glucuronidase inhibitory activity. Our previous report emphasized the importance of the trimeric chemical structure of phloroglucinols in positively increasing the metabolites efficiency. Kinetic analysis and molecular docking simulations of the active compounds were performed to investigate the binding position and cause of the activity difference. A comparison of the docking results revealed that trimeric phloroglucinols 26–28 exhibited lower binding energies than the dimeric phloroglucinols, as evidenced by the stronger β-glucuronidase inhibitory activity of the trimeric group in comparison to the dimeric group. Dimeric compounds 13–15 displayed linkages with two important catalytic residues, Glu413 and Glu504, in the active site through hydrogen bonding and π-anion interactions, respectively. Trimeric compounds 26–28 interacted with Glu413 and Glu504 and tightly interacted with Leu361, Gly362, Ile363, and Lys370 in the bacterial loop of E. coli β-glucuronidase via hydrogen bonds, van der Waals forces, and hydrophobic interactions, which might explain their significant activity against β-glucuronidase.All the phloroglucinols isolated  and d-saccharic acid 1,4-lactone  were purchased from Sigma-Aldrich Co. . 4-Nitrophenyl β-d-glucuronide  was purchased from Tokyo Chemical Industry Co., Ltd. .β-Glucuronidase from β-glucuronidase (50 U/mL) to 100 mM phosphate buffer adjusted to pH 6.8. The solution was mixed in 96-well plates containing 20 μL of DMSO or the compound was dissolved in DMSO, followed by incubation at 37 °C for 20 min in 50 μL PNPG (1 mM for the inhibition assay). β-Glucuronidase inhibitory activity was determined by monitoring the amount of 4-nitrophenol released from the substrate PNGP at 405 nm using a UV-vis spectrophotometer . DSA, an inhibitor of β-glucuronidase, was used as the positive control. Percentage inhibition (%) was calculated using the following equation:β-Glucuronidase inhibitory activity was assayed in accordance with a previously described protocol . Briefly50 values were calculated using SigmaPlot 10.0 .ICβ-glucuronidase, two enzyme kinetic methodologies, the Lineweaver-Burk and Dixon methods, were used [β-glucuronidase inhibition was determined using a Lineweaver–Burk plot  at various concentrations of the substrate in the absence or presence of the test compound . The enzymatic reactions at various concentrations of the test compounds were evaluated by monitoring the effects of different substrate concentrations using Dixon plots  . Ki values were determined by analyzing the Dixon plot, where the x-axis value was taken as Ki. The analysis and visualization of the experimental data were performed using SigmaPlot 10.0 .To identify the inhibition mode of the active compounds on ere used ,25. The β-glucuronidase using AutoDock 4.2  [E. coli β-glucuronidase (PDB ID: 6LEL) was obtained from the RCSB Protein Data Bank database at a resolution of 2.5 Å [Molecular docking simulations were performed to understand the binding efficiency and interaction of active compounds with CA, USA) , as per CA, USA) ,31. The of 2.5 Å . The 3D p < 0.05.All results are presented as means ± standard error of the mean (SEM) of at least three independent experiments. Significance was analyzed using one-way ANOVA and Duncan’s test  and was noted at β-glucuronidase of phloroglucinols isolated from the rhizomes of D. crassirhizoma. Our results showed that nortrisflavaspidic acid ABB (26), trisflavaspidic acid ABB (27), and trisflavaspidic acid BBB (28) exhibited strong inhibitory activity on β-glucuronidase with IC50 values of 8.0, 7.1, and 5.6 μM, respectively, whereas the dimeric compounds 13–15 showed a moderate β-glucuronidase inhibitory effect. Structure–activity relationship analysis confirmed the importance of the trimeric skeleton in the chemical structure of phloroglucinols for their biological activity. Kinetic studies indicated that all the active compounds displayed competitive inhibition of β-glucuronidase. Moreover, molecular docking simulations demonstrated that these active compounds could tightly bind to the active sites of β-glucuronidase with negative binding energies, in agreement with the results of kinetic analysis. Our in vitro and in silico results suggest that D. crassirhizoma rhizomes and their phloroglucinols have great potential as natural herbal β-glucuronidase inhibitors.In conclusion, this is the first report of the inhibitory effect against


Flos sophorae immaturus (FSI) is considered to be a natural hypoglycemic product with the potential for a-glucosidase inhibitory activity. In this work, the polyphenols with α-glucosidase inhibition in FSI were identified, and then their potential mechanisms were investigated by omission assay, interaction, type of inhibition, fluorescence spectroscopy, circular dichroism, isothermal titration calorimetry and molecular docking analysis. The results showed that five polyphenols, namely rutin, quercetin, hyperoside, quercitrin and kaempferol, were identified as a-glucosidase inhibitors with IC50 values of 57, 0.21, 12.77, 25.37 and 0.55 mg/mL, respectively. Quercetin plays a considerable a-glucosidase inhibition role in FSI. Furthermore, the combination of quercetin with kaempferol generated a subadditive effect, and the combination of quercetin with rutin, hyperoside and quercitrin exhibited an interference effect. The results of inhibition kinetics, fluorescence spectroscopy, isothermal titration calorimetry and molecular docking analysis showed that the five polyphenols were mixed inhibitors and significantly burst the fluorescence intensity of α-glucosidase. Moreover, the isothermal titration calorimetry and molecular docking analysis showed that the binding to α-glucosidase was a spontaneous heat-trapping process, with hydrophobic interactions and hydrogen bonding being the key drivers. In general, rutin, quercetin, hyperoside, quercitrin and kaempferol in FSI are potential α-glucosidase inhibitors. Flos sophorae immaturus (FSI) originated in Asia and is widely cultivated in Asia, including China, South Korea, Japan, etc. FSI exhibited hypoglycemia, and antibacterial and antioxidant properties [operties . These foperties . It is poperties . In addioperties . HoweverMoringa oleifera Lam. might be the main bioactive components for its a-glucosidase inhibit activity. Bao et al. [Castanea sativa wood) has higher a-glucosidase inhibition ability. Fang et al. [Lithocarpus polystachyus Rehd) have the highest a-glucosidase inhibition abilities, and a higher value of natural α-glucosidase inhibitors compared to established drugs [α-glucosidase is a key enzyme in the hydrolysis of carbohydrates to glucose . Therefoo et al.  found tho et al.  found thg et al.  found thed drugs .Previous studies have shown that FSI contains a variety of polyphenolic substances, including rutin, chrysin, kaempferol, quercetin, isorhamnetin, hyperoside and quercitrin . However50 assay, the omission method, interaction, type of inhibition, fluorescence spectroscopy, circular dichroism (CD), isothermal titration calorimetry (ITC) and molecular docking.Therefore, this study was conducted to identify the major polyphenols in FSI, then compare the α-glucosidase inhibitory effects of these polyphenols. Furthermore, the inhibitory mechanism of FSI polyphenols on α-glucosidase was preliminarily elucidated by ICDried FSI (sun dried with the water content of 8.0 ± 0.5%) was purchased from Hebei Anguo Yao Yuan Trading Co., Ltd. . Rutin (RU), quercetin (QU), hyperoside (HY), quercitrin (QI), kaempferol (KA), gentianoside, protocatechuic acid, isorhamnetin, kaempferol-3-O-rutinoside, narcissoside, chlorogenic acid, 4-nitrophenyl β-D-glucopyranoside (PNPG), acarbose, α-glucosidase (Yeast) (100 U/mg) and all chemicals were obtained from Shanghai Yuan Ye Biological Technology Co., Ltd. .v/v) ethanol on a magnetic stirrer  for 40 min. The speed and temperature were set as 300 rpm/min and 26 ± 2 °C, respectively. Then the extract was centrifuged at 10,000× g for 10 min. The residue was extracted three times in a row. The supernatants were combined and evaporated to near dryness with a rotary evaporator  under vacuum at 60 °C, then redissolved with methanol to make the volume 50 mL.Dried FSI (1.25 g) was extracted with 40 mL 70% :The α-glucosidase inhibitory activity of the FSI samples was measured using the method described by Xiao et al. , with miThree different solutions were prepared based on different polyphenol fractions (RU (3.40 mg/mL), QU (0.38 mg/mL), HY (0.34 mg/mL), QI (0.23 mg/mL) and KA (0.23 mg/mL): (1) Model solution: a model solution based on the composition of FSI extract. (2) Single polyphenol solution: a component solution containing only a single polyphenol, prepared based on the polyphenol content in FSI extract. (3) Recombinant solution: a reconstituted solution consisting of the missing individual polyphenol. The α-glucosidase inhibitory activities of the model, single polyphenol and recombinant solutions were determined and the contribution of the missing polyphenols to α-glucosidase activity inhibition was calculated .2 was defined as the sum of the inhibition rates of two polyphenols, and the α-glucosidase inhibition rates of three (V*3), and four (V*4) were named in the same way as V*2. Vab is defined as the rate of α-glucosidase inhibition with the addition of polyphenols a and b, and the α-glucosidase inhibition rates of Vabc, and Vabcd were named in the same way as Vab.. Vmin represents the minimum value of α-glucosidase inhibition by a single polyphenol, while Vmax represents the maximum value. V*-Vab < −0.10 represents synergistic (SY). −0.1 < V*-Vab < 0.1 represents additive (AD). Sub-additive is represented by 0.1 < V*-Vab < Vmin (SU). Vmin < V*-Vab < Vmax for interference (IN). Vmax < V*-Vab for antagonism (AN).Different combinations of polyphenols (0.02 mg/mL) were investigated according to the method of Li et al. . Va denoIn 5 mL of the reaction system, different concentrations of PNPG (0.5 mL) were mixed with different concentrations of polyphenols (0.1 mL) in a warm bath at 37 °C for 5 min, α-glucosidase solution (1 mL) was added and the absorbance was recorded at 405 nm in real time. Lineweaver–Burk curve method using the inverse of the PNPG concentration 1/(s) as the horizontal coordinate and the inverse of the enzymatic reaction rate 1/v as the vertical coordinate, and the type of inhibition was determined from the characteristics of the graph . The inhAn equal volume of polyphenol solution (0.02 mg/mL) was added to 3 mL of α-glucosidase solution (0.05 mg/mL) in 9 separate additions of 10 μL each. The mixture was shaken and then placed in a warm bath at 37 °C for 15 min. The system was scanned for fluorescence at three temperatures  using an F-7000 fluorescence spectrometer  at an excitation wavelength of 280 nm and excitation and emission slits of 5 nm. The fluorescence was scanned in the range of 310–500 nm and the corresponding blank control fluorescence spectra were subtracted to calculate the fluorescence. The Stern–Volmer equation was used to calculate the fluorescence burst constants and determine the type of burst .G), enthalpy change (ΔH) and entropy change (ΔS) can be calculated by Equations (5) and (6).R = 8.314 J/(mol·K).The binding reaction of the inhibitor and α-glucosidase is temperature dependent. Therefore, the spontaneity and binding force of their interactions can be studied by calculating thermodynamic parameters . The valα-glucosidase (0.3 mg/mL) with different polyphenols (1 mg/mL) was placed in a 400 μL cuvette and CD spectra (wavelength 190–260 nm) were collected using a Chirascan V100 spectrometer  with a control of 20 mM phosphate buffer (pH 6.8). The CD spectra were analysed using CDNN 2.1 software to obtain α-helix, β-sheet, β-turn and random coil .The inhibitors RU, QU, HY, QI and KA were dissolved in 5% ethanol solution and loaded into VP-ITC  syringes. A solution of α-glucosidase (20 μM) dissolved in 5% ethanol solution was added to the ITC cell. The titration reaction temperature was 25 °C, the number of drops was set to 28, the stirring speed was set to 394 rpm and the reference power was set to 10.00 μcal/s. The titration interval was set to 180 s. The raw data peaks obtained were transformed into a relationship between the molar ratio and the enthalpy change per mole of injected solution [Molecular docking was performed using AutoDock Vina1.1.2 . α-glucop < 0.05 was evaluated by one-way ANOVA. All determinations were repeated three times.Duncan’s multivariate range test in SPSS software  was used for statistical analysis. The statistical significance of 50 values were calculated  and ensured that the sum of their inhibition did not exceed 100% [The α-glucosidase inhibitory activities of RU, QU, HY, QI and KA were analysed . Based oeed 100% . Among teed 100% . When tweed 100%  found theed 100% . KA inhieed 100% . RU, HY eed 100% . TherefoAll polyphenols were at a concentration of 0.02 mg/mL. IN represents the interference effect, SU represents the subadditive effect and AN represents the antagonism effect; RU represents rutin, QU represents quercetin, HY represents hyperoside, QI represents quercitrin, KA represents, kaempferol.The α-glucosidase inhibition types of RU, QU, HY, QI and KA were analysed using inhibition kinetic assays and Lineweaver–Burk plots. For irreversible inhibition, the inhibitor binds to the enzyme through a covalent bond, whereas for reversible inhibition, the inhibitor binds to the enzyme through a non-covalent bond . Three tFluorescence burst experiments can be used to determine the possible burst mechanism between the enzyme and the inhibitor. Fluorescence burst mechanisms are classified as static and dynamic bursts. The type of burst can be determined from the values of Kq and Ksv with temperature . The ene12 L mol−1s−1), suggesting that the burst mechanism was a complex formation or static burst [b value indicates a stronger binding ability of the inhibitor with α-glucosidase, which means that the complex is more stable at higher temperatures [Stern–Volmer curves were prepared for α-glucosidase inhibitors at 298, 303 and 310 K to explore possible burst mechanisms B,E,H,K,Neratures . The strH and negative values of ΔG for the binding reactions of RU, QU, HY, QI and KA with α-glucosidase indicate that the binding process was spontaneous and that hydrophobic interactions were the main role in the binding process or that a conformational shift occurs [S values for QU, QI and KA may be a result of weak hydrophobic interactions with α-glucosidase during binding  [50 results. It remains to be investigated whether the inhibitor effect on the enzyme results in a conformational shift.The thermodynamic parameters of RU, QU, HY, QI and KA with α-glucosidase were calculated according to Equations (5) and (6) . Electrot occurs . In addibonding) . This isbonding) . This isCD spectroscopy is a powerful method to analyze the effect of inhibitors such as RU, QU, HY, QI and KA on the secondary structure of α-glucosidase and the conformational changes in the enzyme can be reflected in the corresponding CD spectra . Figure H, ΔS, binding stoichiometry (n) and equilibrium dissociation constant (KD)  . Figure H and ΔS , it was  process . In conten bonds . The binothermic D. When Δothermic E; ΔS wasen bonds . This reMolecular docking is an effective method to analyse the interaction of inhibitors with enzyme moieties . In this50 = 0.212 mg/mL), followed by kaempferol, hypericin, quercetin and kaempferol. Meanwhile,, the combination of QU with KA produced a SU effect and the combination with RU, HY and QI produced an IN effect. Moreover, RU, QU, HY, QI and KA had a significant fluorescence burst effect on α-glucosidase and the interaction mechanism was a static process. However, circular dichroism spectroscopy indicated that the inhibitor induced slight secondary structural changes in α-glucosidase, and had an insignificant effect on α-glucosidase activity. Furthermore, ITC analysis and molecular docking results indicated that the binding of the inhibitor to the enzyme was entirely spontaneous, with the polyphenols interacting with α-glucosidase through hydrogen bonding and hydrophobic interactions, with hydrophobic interactions being the main driving force. The results of this study provide new insights and valuable information for the study of the inhibitory mechanism of α-glucosidase by FSI polyphenols. Further in vivo experiments are needed in the future to demonstrate their hypoglycemic activity and application mechanism.In this study, RU, QU, HY, QI and KA were identified as the primary α-glucosidase inhibitors and mixed inhibitors in FSI. Quercetin has the strongest α-glucosidase inhibition ability (IC