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Bayesian progress c...
Bayesian progress curve analysis of MicroScale thermophoresis data
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- Larasati Anindya, Atsarina (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,Department of Chemistry and Molecular Biology
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- Garcia-Bonete, Maria-Jose, 1989 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för medicinsk kemi och cellbiologi,Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology
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- Jensen, Maja, 1978 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,Department of Chemistry and Molecular Biology
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- Recktenwald, Christian V (author)
- Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för medicinsk kemi och cellbiologi,Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology
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- Bokarewa, Maria, 1963 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för reumatologi och inflammationsforskning,Institute of Medicine, Department of Rheumatology and Inflammation Research
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- Katona, Gergely, 1975 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,Department of Chemistry and Molecular Biology
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(creator_code:org_t)
- 2022
- 2022
- English.
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In: Digital Discovery. - : Royal Society of Chemistry (RSC). - 2635-098X. ; 1:3, s. 325-332
- Related links:
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https://doi.org/10.1...
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Abstract
Subject headings
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- MicroScale Thermophoresis (MST) follows the movement of fluorescent-labelled biomolecules with different sizes along a temperature gradient. The presence of a “contrary trend” pattern, that is, the trend of fluorescence change reversing at higher titrant concentrations, is a well-known problem with uncertain cause. Conventionally, binding curves and kinetic parameters are derived from MST datasets using regression analysis on isolated time windows, while the rest of the data are ignored, and the “contrary trend” fluorescent levels are also usually removed as outliers. This biased approach can be avoided with a more continuous analysis of the entire kinetic process. The Bayesian model of MST progress curves allows the inference of parameters and modelling of the whole experiment. The removal of unusual data points is unnecessary once the anomalous kinetic process is identified. This alternative data analysis approach was applied to our MST datasets from survivin–hSgol2 interactions, and the results show that the binding curves remained sigmoid when all data were included. We were also able to infer the value and uncertainty of the dissociation constant (KD) by ascribing the anomalous data points to a new, linear kinetic component. This approach demonstrates good posterior predictions from the MST process in both short and longer experiments as well as the feasibility of KD inference from short experiments.
Subject headings
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
- NATURVETENSKAP -- Kemi -- Fysikalisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Physical Chemistry (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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