Log In Sign up
The largest database of trusted experimental protocols
> Chemicals & Drugs > Chemical Viewed Functionally > Inhibitors

Inhibitors

Inhibitiors are substances that interfere with or slow down a specific biological or chemical process.
They can be used to study the mechanisms of various systems and to develop new therapies.
Inhibitors may target enzymes, receptors, signaling pathways, and other cellular components to modulate their activity.
Understanding the effects of inhibitiors is crucial for advancing scientific research and drug discovery across diverse fields such as biochemistry, pharmacology, and medicine.

Most cited protocols related to «Inhibitors»

1
Predictive Models for ADME Properties
The Bioavailability Radar in the first section of the One-panel-per-molecule output complements the two-dimensional image from the JChem webserver and the canonical SMILES calculated by OpenBabel. We use the JpGraph PHP library (version 3.5.0b1, 2016, http://jpgraph.net) to produce the radar plot, which bears six axes for six important properties for oral bioavailability. Each property is defined by a descriptor of SwissADME and a range of optimal values is depicted as a pink area. The latter is inspired from commonly accepted bioavailability and drug-likeness guidelines23 (link)24 (link). For saturation, the ratio of sp3 (link) hybridized carbons over the total carbon count of the molecule (Fraction Csp3) should be at least 0.25. For size, the molecular weight (MW calculated by OpenBabel) should be between 150 and 500 g/mol. For polarity, the TPSA25 (link) should be between 20 and 130Å2 (link). For solubility, log S (calculated with the ESOL model36 ) should not exceed 6. For lipophilicity, XLOGP329 (link) should be in the range from −0.7 to +6.0. For flexibility, the molecule should not have more than 9 rotatable bonds. To be estimated as drug-like, the red line of the compound under study must be fully included in the pink area. Any deviation represents a suboptimal physicochemical property for oral bioavailability.
All descriptors and molecular parameters of the Physicochemical Properties section are computed through the OpenBabel API (version 2.3.0, 2012, http://openbabel.org)9 (link). Noteworthy, the topological polar surface area (TPSA) is strictly based on the fragmental system provided by Ertl et al.25 (link) including polar sulfur and phosphorus atoms.
Multiple freely available computational methods to predict n-octanol/water partition coefficient (log Po/w) values are made available in the Lipophilicity section. iLOGP (for implicit log P) is an in-house physics-based methods relying on Gibbs free energy of solvation calculated by GB/SA in water and n-octanol. Generalized-born (GB) parameters are computed through the GBMV2 method68 (link) and solvent-accessible surface area (SA) is the analytical approximation generated by CHARMM (version c36b1, 2011, https://www.charmm.org)69 (link). The iLOGP implemented in SwissADME corresponds to Model9 of the seminal publication16 (link), which was trained on 11,993 molecules (r = 0.72, MAE = 0.89, and RMSE = 1.14 against experimental log P). 5-fold crossvalidation ensured robustness (q2CV = 0.52, MAECV = 0.89, and RMSECV = 1.14) and external test benchmarks showed the excellent predictive power and extended applicability domain compared to well-established methods. XLOGP3 values are obtained through the command-line Linux program (version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry) including the knowledge-based corrections29 (link). WLOGP is our own implementation of the atomistic method developed by Wildman and Crippen30 . MLOGP values are computed through an in-house implementation of Moriguchi’s topological method31 32 . SILICOS-IT is the log Po/w estimation returned by executing the FILTER-IT program (version 1.0.2, 2013, http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/filter-it/1.0.2/filter-it.html). Finally, SwissADME gives a consensus log Po/w value, which is the arithmetic mean of the five predictive values mentioned above.
Similarly to lipophilicity, the Water Solubility section includes multiple predictive methods for the user to choose between the most accurate model for a given chemical series and an averaged consensus value. The ESOL model36 is a QSPR model establishing the linear relationship between log S and five molecular parameters, i.e. MW, the number of rotatable bonds, the fraction of aromatic heavy atoms and Daylight’s CLOGP. Because the lipophilicity descriptor is not freely available, the implementation of ESOL in SwissADME replaces CLOGP by XLOGP3 as parameter in the linear equation to predict log S. XLOGP3 is known to perform well on external datasets and to return similar predictions as CLOGP28 (link). The other three parameters were computed with OpenBabel. Likewise, Ali et al.37 (link) linked log S with log Po/w and TPSA. The model implemented in SwissADME corresponds to the model 3 of the original publication, with XLOGP3 as lipophilicity descriptor. The third solubility method available in SwissADME is the log S estimated by the FILTER-IT program (version 1.0.2, 2013, http://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/filter-it/1.0.2/filter-it.html). This prediction is based on a system of 16 fragmental contributions modulated by the squared root of MW. All three models are predicting log S values, which are also translated within SwissADME into solubility in mol/l and mg/ml. Finally a qualitative estimation of the solubility class is given according to the following log S scale: insoluble <−10 The Pharmacokinetics section proposes one linear method for skin permeation, which relies on the simple QSPR model by Potts and Guy39 linking the decimal logarithm of the skin permeability coefficient (log Kp in cm/s) with MW and log Po/w. The model implemented in SwissADME uses XLOGP3 as lipophilicity descriptor. Besides, most of the models in this section are machine-learning binary classifiers for important ADME behaviours. Passive gastro-intestinal (HIA) absorption and blood-brain barrier (BBB) permeation are predicted with the BOILED-Egg model, which defines favourable and unfavourable zones in the log Po/wversus PSA physicochemical space for passive diffusion through both physiological barriers17 (link). The classification showed 10-fold cross-validation accuracy of 92% and 88% for BBB and HIA, respectively (refer to Graphical Output).
Six other classification models are part of the Pharmacokinetics section to predict the propensity of the molecule under investigation to be substrate or inhibitor of important pharmacokinetics-related proteins, for which large diverse and balanced datasets were retrieved and meticulously cleansed. For P-glycoprotein1 (P-gp), the training set consists of 521 substrates and 512 non-substrates extracted from the Metrabase database70 (link) (http://www-metrabase.ch.cam.ac.uk, accessed January 2016), whereas the test set was obtained from ref. 71 (link). To ensure truly external validation, molecules overlapping with the training set were removed from the test set, which finally includes 215 substrates and 200 non-substrates. For CYP major isoforms, all datasets were those of Veith et al.50 (link) and downloaded from the PubChem database72 (link) (http://pubchem.ncbi.nlm.nih.gov, accessed February 2016). In case of unbalanced dataset (all except CYP1A2 and CYP2C19), sufficient chemical diversity was guaranteed by clustering with the Ward method and a reciprocal nearest neighbour (RNN) algorithm73 , the more populated class to lessen. The number of molecules (described by circular fingerprints) of the large class is reduced by defining clusters with the JKlustor program (version 14.9.29, 2014, http://www.chemaxon.com). Only the centre of each cluster (i.e. the molecule that has the smallest sum of dissimilarities to the other molecules in the cluster) is included in the training or test set to balance. As a result, the training sets involved respectively 4301 CYP1A2 inhibitors and 4844 CYP1A2 non-inhibitors; 4284 CYP2C19 inhibitors and 4988 CYP2C19 non-inhibitors; 2940 CYP2C9 inhibitors and 3000 CYP2C9 non-inhibitors; 1814 CYP2D6 inhibitors and 1850 CYP2D6 non-inhibitors; and 3758 CYP3A4 inhibitors and 3760 CYP3A4 non-inhibitors. The test sets involved respectively 1412 CYP1A2 inhibitors and 1588 CYP1A2 non-inhibitors; 1386 CYP2C19 inhibitors and 1614 CYP2C19 non-inhibitors; 1020 CYP2C9 inhibitors and 1055 CYP2C9 non-inhibitors; 528 CYP2D6 inhibitors and 540 CYP2D6 non-inhibitors; and 1289 CYP3A4 inhibitors and 1290 CYP3A4 non-inhibitors.
SwissADME’s backend calculations were ran to generate 50 molecular and physicochemical descriptors per molecule (described in the Supplementary Table S1). For a given model, a descriptor was rejected if non-zero values for all molecules in the training set are less than 20% or if the coefficient of variation is less than 3%. In case of correlation higher than 0.9 between remaining descriptors, a selection is made based on F-score. The selected descriptors for each model are shown in Supplementary Tables S2–S7. These tables also include the minimum and maximum values for each descriptor among all molecules used in the training. This enables beholding the broadness of physicochemical space involved and the applicability domain of the SVM models. The predictive capability of each model can be further appraised on Supplementary Table S8, where external accuracy was split in sensitivity and specificity to ensure that positive and negative molecules are predicted with the same level of robustness. The final training and test sets with selected descriptors were normalized and the respective model ready to be built. First, the libSVM support vector machine python library (version 3.20, 2015, https://www.csie.ntu.edu.tw/~cjlin/libsvm/)74 was used for multi-step grid-based optimization of the best coefficients for the above-selected descriptors as well as for the soft-margin permissivity (C) and the hyper-parameter (ϒ) of the RBF Gaussian kernel function. The 10-fold crossvalidated accuracy (ACCCV) for each model was so maximized and AUCCV was calculated. In a second step, the so-built models were used on the external test sets (normalized according to the training set) in order to evaluate predictive power in terms of external accuracy (ACCext) and AUCext. All final SVM models were stored in separate files, which are read through the libSVM API upon SwissADME job submission.
Daina A., Michielin O, & Zoete V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717.
Full text: Click here
Publication 2017
2
Cell Culture Optimization Protocol
Unless specified, all data were obtained from at least triplicate samples and represent at least three independent experiments and presented as mean ± s.e.m. Graphs were constructed using GraphPad Prism 4.0 (GraphPad Software). Where applicable, statistical differences were tested by unpaired Student’s t-test and were considered significant for P < 0.05.
Bhardwaj A., Kaur J., Wuest M, & Wuest F. (2017). In situ click chemistry generation of cyclooxygenase-2 inhibitors. Nature Communications, 8, 1.
Full text: Click here
Publication 2017
prisma Student
3
Nuclei Isolation for ATAC-Seq Analysis
See Supplementary
Protocol 2 for a detailed protocol. This protocol is highly similar
to the INTACT method19 (link) and
either protocol can be used for the isolation of nuclei with equivalent results.
All of the steps were carried out at 4 °C. A frozen tissue fragment ~20
mg was placed into a pre-chilled 2-ml Dounce homogenizer containing 2 ml of cold
1× homogenization buffer (320 mM sucrose, 0.1 mM EDTA, 0.1%
NP40, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris pH 7.8,
1× protease inhibitors (Roche, cOmplete), and 167 μM
β-mercaptoethanol, in water). Tissue was homogenized with approximately
ten strokes with the loose ‘A’ pestle, followed by 20 strokes
with the tight ‘B’ pestle. Connective tissue and residual debris
were precleared by filtration through an 80-μm nylon mesh filter
followed by centrifugation for 1 min at 100 r.c.f. While avoiding the pelleted
debris, 400 μl was transferred to a pre-chilled 2-ml round bottom
Lo-Bind Eppendorf tube. An equal volume (400 μl) of a 50%
iodixanol solution (50% iodixanol in 1× homogenization buffer)
was added and mixed by pipetting to make a final concentration of 25%
iodixanol. 600 μl of a 29% iodixanol solution (29%
iodixanol in 1× homogenization buffer containing 480 mM sucrose) was
layered underneath the 25% iodixanol mixture. A clearly defined
interface should be visible. In a similar fashion, 600 μl of a
35% iodixanol solution (35% iodixanol in 1×
homogenization containing 480 mM sucrose) was layered underneath the 29%
iodixanol solution. Again, a clearly defined interface should be visible between
all three layers. In a swinging-bucket centrifuge, nuclei were centrifuged for
20 min at 3,000 r.c.f. After centrifugation, the nuclei were present at the
interface of the 29% and 35% iodixanol solutions. This band with
the nuclei was collected in a 300 μl volume and transferred to a
pre-chilled tube. Nuclei were counted after addition of trypan blue, which
stains all nuclei due to membrane permeabilization from freezing. 50,000 counted
nuclei were then transferred to a tube containing 1 ml of ATAC-seq RSB with
0.1% Tween-20. Nuclei were pelleted by centrifugation at 500 r.c.f. for
10 min in a pre-chilled (4 °C) fixed-angle centrifuge. Supernatant was
removed using the two pipetting steps described above. Because the nuclei were
already permeabilized, no lysis step was performed, and the transposition mix
(25 μl 2× TD buffer, 2.5 μl transposase (100 nM final),
16.5 μl PBS, 0.5 μl 1% digitonin, 0.5 μl
10% Tween-20, 5 μl water) was added directly to the nuclear
pellet and mixed by pipetting up and down six times. Transposition reactions
were incubated at 37 °C for 30 min in a thermomixer with shaking at
1,000 r.p.m. Reactions were cleaned up with Zymo DNA Clean and Concentrator 5
columns. The remainder of the ATAC-seq library preparation was performed as
described previously18 .
Corces M.R., Trevino A.E., Hamilton E.G., Greenside P.G., Sinnott-Armstrong N.A., Vesuna S., Satpathy A.T., Rubin A.J., Montine K.S., Wu B., Kathiria A., Cho S.W., Mumbach M.R., Carter A.C., Kasowski M., Orloff L.A., Risca V.I., Kundaje A., Khavari P.A., Montine T.J., Greenleaf W.J, & Chang H.Y. (2017). An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nature methods, 14(10), 959-962.
Publication 2017
2-Mercaptoethanol ATAC-Seq Buffers Cell Nucleus Centrifugation Cerebrovascular Accident Connective Tissue Digitonin DNA Library Edetic Acid Filtration iodixanol isolation Nylons Protease Inhibitors Sucrose Tissue, Membrane Tissues Transposase Tromethamine Trypan Blue Tween 20
4
Analyzing Interaction Effects on Diabetes
First, we calculated the odds ratio of the use of ACE inhibitors on the risk of diabetes, and the odds ratio of the DD genotype of the ACE gene on the risk of diabetes. These odds ratios represent the effect of one of the exposures analyzed without conditioning on the other exposure. We refer to these effects as ‘single effects’. Subsequently, we calculated joint effects of the use of ACE inhibitors and the DD genotype of the ACE gene using one reference category.
Second, we calculated the three measures of interaction on an additive scale (RERI, AP, and S) and their 95% confidence intervals using the delta method [9 (link)], assuming that the odds ratios calculated in the example dataset approximated relative risks. We also calculated 95% confidence intervals using the method described by Zou [18 (link)], which resulted in similar confidence intervals.
Third, we recoded the variables in such a way that the stratum with the lowest risk, when both factors are considered jointly, became the reference category. We calculated the measures of additive interaction again and compared the results with the original results.
Because we used the data for illustration purposes only, we did not take into account the matching of cases and controls, and we did not adjust for potential confounders.
Knol M.J., VanderWeele T.J., Groenwold R.H., Klungel O.H., Rovers M.M, & Grobbee D.E. (2011). Estimating measures of interaction on an additive scale for preventive exposures. European Journal of Epidemiology, 26(6), 433-438.
Publication 2011
Angiotensin-Converting Enzyme Inhibitors Diabetes Mellitus Genes Genotype Joints
5
In Vitro Synthesis of COX-2 Inhibitors
To investigate the COX-2 isozyme templated synthesis, each 5-azido-pyraozle (5, 14, 27, and 31, 1 µl of 3 mM DMSO solution) and alkyne (6a–6f, 15a–15e, 1 µl of 20 mM DMSO solution) were pairwise mixed with human recombinant COX-2 isozyme (95 µl COX-2) in 1 µl of 1 M Tris-HCl, pH 8.0. The each reaction mixture was vortexed for 1 min, and then incubated at room temperature (For temperature dependency of COX-2 enzyme activity, see Supplementary Fig. 16). Final reagent concentrations were as follows: COX-2 (7 µM), azide (30 µM) alkyne (200 µM). After 3, 6, 9, 12, 15, 18, 21, and 24 h each sample was analyzed in triplicate by injecting (10 µl) into the LC/MS instrument with SIM mode (Water’s Micromass ZQTM 4000 LC−MS instrument, operating in the ESI-positive mode, equipped with a Water’s 2795 separation module). Calibration curve for hit compounds 18 and 21 is given in Supplementary Fig. 17. Summaries of all LC/MS data are presented in Supplementary Tables 3–7. Separations were performed in triplicate using a Kromasil 100-5-C18 (100 μm pore size, 5 μm particle size) reverse phase column (2.1 mm diameter × 50 mm length), preceded by a Kromasil 100-5-C18 2.1 × guard column. Separations were effected using a gradient MeCN/H2O (0.05% trifluoroacetic acid (TFA))/MeOH in 40/30/30, v/v/v over 15 min at flow rate 0.25 ml min−1. Operating parameters were as follows: capillary voltage = 3.5 kV; cone voltage = 20 V; source temperature = 140 °C; sesolvation temperature = 250 °C; cone nitrogen gas flow = 100 l h−1; desolvation nitrogen gas flow = 550 l h−1. The identities of triazole products (retention time of 6.73 min for 18), (retention time of 4.56 min for 21), and the internal standard (retention time of 10.89 min) were confirmed by molecular weight and comparison of the retention times of the authentic products formed from copper catalyzed reactions. Control experiments in the presence of BSA (1 mg mL−1) instead of the COX-2 enzyme as well as in the absence of COX-2 enzyme and the known COX-2 selective inhibitor (1 µl of celecoxib, 100 µM final concentration) were run as described above. For multicomponent in situ click chemistry reactions, each azide (5, 14, 27, and 31, 1 µL of 3 mM DMSO solution) and eleven alkynes (6a–6f and 15a–15e, 1 µl of 20 mM DMSO solution) were thoroughly mixed together in the presence of COX-2 isozyme (95 µl COX-2) in 1 µl of 1 M Tris-HCl, pH 8.0 and incubated at room temperature. After 24 h each sample was analyzed in triplicate by injecting (10 µl) into the LC/MS instrument by following the procedure described above, except the ions are monitored for all possible masses. The cyclo addition products were identified by their molecular weights and by comparison of the retention times of authentic products prepared through Cu-catalyzed reactions. Control experiments using BSA (1 mg ml−1) in place of COX-2 isozyme and in the absence of COX-2 isozyme were run consecutively.
Bhardwaj A., Kaur J., Wuest M, & Wuest F. (2017). In situ click chemistry generation of cyclooxygenase-2 inhibitors. Nature Communications, 8, 1.
Full text: Click here
Publication 2017
Alkynes Anabolism Azides Capillaries Celecoxib compound 18 Copper Cyclooxygenase 2 Inhibitors enzyme activity Enzymes Homo sapiens Ions Isoenzymes Nitrogen PTGS2 protein, human Retention (Psychology) Retinal Cone Sulfoxide, Dimethyl Triazoles Trifluoroacetic Acid Tromethamine

Most recents protocols related to «Inhibitors»

1
Physicochemical Profiles of Transporter Inhibitors
Not available on PMC !
P-gp and BCRP inhibitors were identified in all physicochemical property categories, with the exception that no BCRP inhibitor had a LogP <1.0. P-gp inhibitors tended to be highly permeable and lipophilic, with a higher proportion of compounds with high lipophilicity (LogP >4.0, LogD >2.5) being inhibitors. For BCRP, relatively large compounds with MW ≥500, medium to high permeability and with intermediate-high lipophilicity were more likely to be inhibitors. Compounds in all ionisation states except zwitterions were BCRP inhibitors, but no association of ionisation state with P-gp inhibition could be identified in this small dataset except for zwitterions being P-gp inhibitors.
OATP1B1 and OATP1B3 inhibitors were identified in all physicochemical property categories. Intermediate-high lipophilicity (LogD >0.5 and/or LogP >1.0), acidic and relatively large size (MW ≥ 500 Da) compounds tended to be inhibitors. No OATP1B1 or OATP1B3 inhibitor had a LogP <1.0. Additionally, neutral compounds were more likely to be OATP1B1 inhibitors. The tendency to inhibit OATP1B1 also included compounds with medium to high permeability, whilst compounds with medium permeability were identified as OATP1B3 inhibitors.
OCT1, OCT2, MATE1, MATE2-K inhibitors were also identified in all physicochemical property categories. Physicochemical property analysis was not conducted for MATE1 due to the low number of non-inhibitors (n=2). Bases were associated with OCT1 and MATE2-K inhibitors. Additionally, high lipophilicity, high permeability, and neutral compounds tended to be OCT1 inhibitors and compounds with intermediate lipophilicity were likely to be MATE2-K inhibitors. In this dataset, there were no physicochemical property trends identified for OCT2 inhibition.
OAT3 and OAT1 inhibitors were also identified in most physicochemical categories. Higher proportion of acidic compounds were OAT1 and OAT3 inhibitors while the majority of the compounds in other ionisation states were non-inhibitors.
Substrates:
Compounds with intermediate-high lipophilicity were likely to be P-gp and/or BCRP substrates. Although, substrates of both transporters were identified in all ionisation states, neutral and basic compounds were predominantly substrates for both transporters.
For OAT3, smaller compounds (< 500 Da) tended to be substrates while high lipophilicity compounds were mostly non-substrates. Contrary to expectations, no acids in this dataset were OATP1B1 and/or OATP1B3 substrates. Physicochemical property analyses of substrates of the other transporters (OAT1, OCT1, OCT2, MATE1 and MATE2-K) could not be conducted due to limited dataset.
Rollison H.E., Mitra P., Chanteux H., Fang Z., Liang X., Park S.H., Costales C., Hanna I., Thakkar N., Vergis J.M., Bow D.A.J., Hillgren K.M., Brumm J., Chu X., Hop C.E.C.A., Lai Y., Li C.Y., Mahar K.M., Salphati L., Sane R., Shen H., Taskar K., Taub M., Tohyama K., Xu C, & Fenner K.S. (2024). Survey of Pharmaceutical Industry's Best Practices around In Vitro Transporter Assessment and Implications for Drug Development: Considerations from the International Consortium for Innovation and Quality for Pharmaceutical Development Transporter Working Group. Drug metabolism and disposition: the biological fate of chemicals, 52(7).
Publication 2024
2
Inhibition of CD8+ T-cell Signaling
For some experiments, prior to co-culture with BEC, CD8+ T cells were treated with molecular inhibitors for 30 min (1 μM Wortmannin [Merck, Cat No. W1628], 5 nM H-1152 [Merck, Cat No. 555550] or 5 μM cytochalasin D [Merck, Cat No. C2618]). Inhibitors were solubilised to stock concentrations such that the DMSO content of inhibitors prepared at working concentrations, would be no more than 0.1%. Concentrations of all treatments and relevant vehicle controls were maintained throughout the co-culture duration.
Davies S.P., Ronca V., Wootton G.E., Krajewska N.M., Bozward A.G., Fiancette R., Patten D.A., Yankouskaya K., Reynolds G.M., Pat S., Osei-Bordom D.C., Richardson N., Grover L.M., Weston C.J, & Oo Y.H. (2024). Expression of E-cadherin by CD8+ T cells promotes their invasion into biliary epithelial cells. Nature Communications, 15, 853.
Full text: Click here
Publication 2024
3
Sourcing and Storage of Inhibitory Compounds
Not available on PMC !

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2024
4
Epigenetic Modulator Protocols
All inhibitors were obtained from Sigma. Apicidin and valproic acid were dissolved in 100% ethanol at 10 mm and 100 mm respectively. Ellagic acid and MS-275 were dissolved in DMSO at 10 mm and 2.5 mm, respectively. Phenylbutyric acid and nicotinamide were dissolved in water at 1M and 0.2M, respectively. Inhibitors were added to cultures to the following final concentration: apicidin, 10 µm; valproic acid, 100 µm; ellagic acid, 10 µm; MS-275, 2.5 µm; PBA, 1 mm; nicotinamide, 200 µm.
Figgins E.L., Arora P., Gao D., Porcelli E., Ahmed R., Daep C.A., Keele G., Ryan L.K, & Diamond G. (2024). Enhancement of innate immunity in gingival epithelial cells by vitamin D and HDAC inhibitors. Frontiers in Oral Health, 5, 1378566.
Full text: Click here
Publication 2024
5
Pharmacological Inhibition of Cell Signaling
The inhibitors of p38MAPK SB203580 and BMS-582949, the inhibitor of STAT1 fludarabine, the calmodulin kinase inhibitor KN-62, the microtubule stabilizer paclitaxel, and the microtubule stability inhibitor colchicine were all purchased from Tsbiochem Biotechnology (Shanghai, China). All inhibitors were dissolved in dimethyl sulfoxide (DMSO) and diluted to the final concentration in complete medium. In our study, the final concentration of SB203580 was 0.5 µM, BMS-582949 was 10 nM, fludarabine was 50 µM, KN-62 was 1 µM, paclitaxel was 10 nM and colchicine was 3 nM.
Zeng L., Lyu X., Yuan J., Chen Y., Wen H., Zhang L., Shi J., Liu B., Li W, & Yang S. (2024). STMN1 Promotes Tumor Metastasis in Non-small Cell Lung Cancer Through Microtubule-dependent And Nonmicrotubule-dependent Pathways. International Journal of Biological Sciences, 20(4), 1509-1527.
Full text: Click here
Publication 2024

Top products related to «Inhibitors»

1
Protease inhibitor by Roche
Sourced in United States, Switzerland, Germany, China, United Kingdom, Canada, France, Japan, Italy, Australia, Spain, India, Macao, Netherlands, Denmark, Belgium, Cameroon
Protease inhibitors are a class of laboratory equipment used in the field of biochemistry and molecular biology. These inhibitors are designed to specifically target and inactivate proteases, which are enzymes that break down proteins. Protease inhibitors play a crucial role in various experimental and analytical procedures, such as protein extraction, purification, and stabilization.
2
Pvdf membrane by Merck Group
Sourced in United States, Germany, China, United Kingdom, Morocco, Ireland, France, Italy, Japan, Canada, Spain, Switzerland, New Zealand, India, Hong Kong, Sao Tome and Principe, Sweden, Netherlands, Australia, Belgium, Austria
PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.
3
Lipofectamine 2000 by Thermo Fisher Scientific
Sourced in United States, China, Germany, United Kingdom, Canada, Japan, France, Italy, Switzerland, Australia, Spain, Belgium, Denmark, Singapore, India, Netherlands, Sweden, New Zealand, Portugal, Poland, Lithuania, Hong Kong, Argentina, Ireland, Austria, Israel, Czechia, Cameroon, Taiwan, Province of China, Morocco
Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
4
Fetal bovine serum (fbs) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
5
Protease inhibitor by Merck Group
Sourced in United States, Germany, China, United Kingdom, Italy, Macao, Sao Tome and Principe, Canada, Japan, France, Switzerland, Israel
Protease inhibitors are a class of pharmaceutical compounds that work by inhibiting the activity of proteases, which are enzymes that break down proteins. They are commonly used in the treatment of various conditions, including viral infections and certain types of cancer. Protease inhibitors function by binding to and blocking the active site of proteases, preventing them from carrying out their enzymatic activities.
6
Protease and phosphatase inhibitor by Roche
Sourced in United States, Switzerland, Germany, China, United Kingdom, Canada, France, Japan, Belgium, Spain, Australia
Protease and phosphatase inhibitors are a class of lab equipment used to prevent the degradation of proteins and protein modifications during sample preparation and analysis. They work by inhibiting the activity of enzymes that can cleave or alter proteins, helping to preserve the integrity of the samples for further study.
7
Bca protein assay kit by Thermo Fisher Scientific
Sourced in United States, China, Germany, United Kingdom, Japan, Belgium, France, Switzerland, Italy, Canada, Australia, Sweden, Spain, Israel, Lithuania, Netherlands, Denmark, Finland, India, Singapore
The BCA Protein Assay Kit is a colorimetric detection and quantification method for total protein concentration. It utilizes bicinchoninic acid (BCA) for the colorimetric detection and quantification of total protein. The assay is based on the reduction of Cu2+ to Cu1+ by protein in an alkaline medium, with the chelation of BCA with the Cu1+ ion resulting in a purple-colored reaction product that exhibits a strong absorbance at 562 nm, which is proportional to the amount of protein present in the sample.
8
Pierce bca protein assay kit by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, Australia, Switzerland, France, Canada, Spain, Italy, Japan, Belgium, Sweden, Lithuania, Austria, Denmark, Poland, Ireland, Portugal, Finland, Czechia, Norway, Macao, India, Singapore
The Pierce BCA Protein Assay Kit is a colorimetric-based method for the quantification of total protein in a sample. It utilizes the bicinchoninic acid (BCA) reaction, where proteins reduce Cu2+ to Cu+ in an alkaline environment, and the resulting purple-colored reaction is measured spectrophotometrically.
9
Ripa buffer by Merck Group
Sourced in United States, Germany, United Kingdom, Switzerland, Italy, Sao Tome and Principe, China, Canada, Macao, France, Japan, Spain, Ireland, Australia, Sweden, Belgium, Denmark, Morocco, Austria, Poland
RIPA buffer is a widely used lysis buffer for extracting proteins from cells and tissues. It is a detergent-based buffer that helps solubilize proteins, disrupt cell membranes, and maintain the integrity of protein structures during the extraction process. The buffer contains a combination of ionic and non-ionic detergents, as well as other components that help stabilize and preserve the extracted proteins.
10
Dimethyl sulfoxide (dmso) by Merck Group
Sourced in United States, Germany, United Kingdom, China, Italy, Sao Tome and Principe, France, Macao, India, Canada, Switzerland, Japan, Australia, Spain, Poland, Belgium, Brazil, Czechia, Portugal, Austria, Denmark, Israel, Sweden, Ireland, Hungary, Mexico, Netherlands, Singapore, Indonesia, Slovakia, Cameroon, Norway, Thailand, Chile, Finland, Malaysia, Latvia, New Zealand, Hong Kong, Pakistan, Uruguay, Bangladesh
DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.

More about "Inhibitors"

Inhibitors are powerful tools used in scientific research and drug development.
These substances can interfere with or slow down specific biological or chemical processes, allowing researchers to study the mechanisms of various systems and develop new therapies.
Inhibitors can target enzymes, receptors, signaling pathways, and other cellular components, modulating their activity and providing crucial insights.
One important subclass of inhibitors are Protease inhibitors, which block the action of proteolytic enzymes.
These are widely used in fields like biochemistry and pharmacology to understand protein function and develop therapeutic interventions.
PVDF membranes, a common laboratory tool, can be used in conjunction with protease inhibitors for protein detection and analysis.
Lipofectamine 2000, a transfection reagent, is often used in combination with inhibitors to study gene expression and signaling pathways.
FBS (Fetal Bovine Serum) and protease and phosphatase inhibitors are also commonly employed to maintain the integrity of cellular samples during experiments.
The BCA protein assay kit, including the Pierce BCA Protein Assay Kit, is a popular method for quantifying protein concentrations in the presence of inhibitors.
RIPA buffer, a lysis buffer, can be used to extract proteins while preserving the activity of cellular components like enzymes, which may be affected by inhibitors.
DMSO (Dimethyl Sulfoxide) is a versatile solvent that can be used to dissolve inhibitors, facilitating their application in various experimental settings.
Understanding the effects of inhibitors is crucial for advancing scientific research and drug discovery across diverse fields, from biochemistry and pharmacology to medicine.
PubCompare.ai is a powerful platform that can help researchers identify and compare the most effective and reproducible methods, including those involving inhibitors, to optimize their workflows and enhance the reproducibility of their findings.