micro spotting solution cat id: mss Search Results


micro spot plate 3 well  (Vector Laboratories)
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Vector Laboratories micro spot plate 3 well
Micro Spot Plate 3 Well, supplied by Vector Laboratories, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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macrospin columns  (Chem Impex International)
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Chem Impex International macrospin columns
Macrospin Columns, supplied by Chem Impex International, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ultragaps2 microarray slides  (Corning Life Sciences)
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Corning Life Sciences ultragaps2 microarray slides
Ultragaps2 Microarray Slides, supplied by Corning Life Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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silicon microarray spotting pins  (Parallel Synthesis Technologies)
90
Parallel Synthesis Technologies silicon microarray spotting pins
Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the <t>microarray</t> data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).
Silicon Microarray Spotting Pins, supplied by Parallel Synthesis Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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micro spotting solution cat id: mss  (Arrayit Corporation)
90
Arrayit Corporation micro spotting solution cat id: mss
Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the <t>microarray</t> data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).
Micro Spotting Solution Cat Id: Mss, supplied by Arrayit Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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thin co2 sensor (5 in diameter and 200 microns thick)  (PreSens Inc)
90
PreSens Inc thin co2 sensor (5 in diameter and 200 microns thick)
Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the <t>microarray</t> data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).
Thin Co2 Sensor (5 In Diameter And 200 Microns Thick), supplied by PreSens Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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transcription pathway microarray groupings  (TaKaRa)
86
TaKaRa transcription pathway microarray groupings
Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the <t>microarray</t> data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).
Transcription Pathway Microarray Groupings, supplied by TaKaRa, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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micro spotting pins  (TeleChem International)
90
TeleChem International micro spotting pins
Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the <t>microarray</t> data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).
Micro Spotting Pins, supplied by TeleChem International, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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chlorhexidine digluconate solution 20% in h2o  (Chem Impex International)
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Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the microarray data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).

Journal: Nature protocols

Article Title: Quantifying protein-protein interactions in high throughput using protein domain microarrays

doi: 10.1038/nprot.2010.36

Figure Lengend Snippet: Quantifying domain–peptide interactions in high throughput using protein domain microarrays. (a) A set of n protein interaction domains are cloned, expressed, purified and arrayed. The microarrays of protein domains are then probed with m fluorescently labeled peptides to reveal the full n × m matrix of domain–peptide interactions. (b) For high-affinity interactions (KD < 2 μM), dissociation constants can be determined directly using protein microarrays. Microarrays of protein domains are probed with eight concentrations of each peptide and the resulting saturation binding curves are used to determine the binding affinity of each domain–peptide interaction. (c) For low-affinity interactions (KD < 50 μM), microarrays of protein domains are probed with fluorescently labeled peptides and a fluorescence threshold is used to divide domain–peptide pairs into putative interactions (array positives) and putative noninteractions (array negatives). A secondary assay (FP) is then used to retest and quantify all array positives. The result is a quantitative interaction data set (data set 1) in which all the false positives in the microarray data set have been eliminated. To remove false negatives, it is necessary to build a model that can predict domain–peptide interactions. The model is then used to highlight suspected false negatives in the microarray data set, which are retested by FP. By performing multiple cycles of prediction, retesting and retraining of the model, many of the microarray false negatives can be corrected. This results in a substantially refined data set (data set 2).

Article Snippet: Custom-made aldehyde-coated 74.5 mm × 112.5 mm × 1 mm glass substrates (Thermo Fisher Scientific Inc., cat. no. HAR-1101-C60) ProPlate Gaskets (Grace Bio-labs, cat. no. 204971) 96-Well No-Bottom microtiter plates (Greiner Bio-One, cat. no. 655000) 96-well microtiter plates (Greiner Bio-One, cat. no. 650201) 384-well assay plates, black nonbinding, for FP assays (Corning, cat. no. 3575) 384-well microarray plates, for printing protein microarrays (Genetix, cat. no. X7022) Storage Mat III (Costar, cat. no. 3080) Disposable reagent reservoirs, sterile (VWR, cat. no. 82026-350) Deep-well 96-well microtiter plate (Costar, cat. no. 3960) 14 ml Polypropylene Round-Bottom Tube (Becton Dickinson, cat. no. 352059) NanoPrint LM60 Microarrayer (Arrayit Corporation) including cooling block for source plate and destination block designed for 16 microtiter-sized glass plates Silicon Microarray spotting pins (Parallel Synthesis Technologies Inc., cat. no. SMT-S75) 48-pin Silicon printhead assembly (Parallel Synthesis Technologies Inc., cat. no. SMT-H192) Funnel for silicon 48-pin printhead (Parallel Synthesis Technologies Inc., cat. no. SMT-F48) Fluorescence microarray scanner—Tecan LS400 Laser Scanner or similar (Tecan, cat. no. LS400) ArrayPro software or equivalent (Tecan) Matlab software or equivalent (The MathWorks) Peptide Synthesizer, Apex 396 or similar (Aapptec, cat. no. Apex 396-DC-FW-M) Kromasil 100 (5 μm) C18 semi-prep column (Peeke Scientific, cat. no. 100-5-C18 20 × 250) Kromasil 100 (5 μm) C18 analytical column (Peeke Scientific, cat. no. 100-5-C18 2.1 × 150) Superdex 200 10/300 GL column (Amersham Biosciences, cat. no 17-5175-01) Fluorescence polarization plate reader, Analyst AD 96:384 or similar (LJL Biosystems) Storage Mat Applicator (Corning, cat. no. 3081) Microtiter plate shaker (Lab Line, cat. no. 4625) Floor centrifuge, capable of holding both 500 and 30 ml tubes 2-L Baffled flasks (VWR , cat. no. 89083-696) 500-ml Centrifuge tubes (Sorvall, cat. no. 7-9957) 30-ml Oakridge centrifuge tubes (Thermo Fisher Scientific Inc., cat. no. 3119-0010) P1000 multichannel pipette (Rainin, cat. no. L1000) P200 multichannel pipette (Rainin, cat. no. L200) P10 multichannel pipette (Rainin, cat. no. L10) Mini Bunsen burner Agilent 1200 series HPLC with fraction collector

Techniques: High Throughput Screening Assay, Clone Assay, Purification, Labeling, Binding Assay, Fluorescence, Microarray