Abstract
A biomolecular ensemble exhibits different responses to a temperature gradient depending on its diffusion properties. MicroScale Thermophoresis technique exploits this effect and is becoming a popular technique for analyzing interactions of biomolecules in solution. When comparing affinities of related compounds, the reliability of the determined thermodynamic parameters often comes into question. The thermophoresis binding curves can be assessed by Bayesian inference, which provides a probability distribution for the dissociation constant of the interacting partners. By applying Bayesian machine learning principles, binding curves can be autonomously analyzed without manual intervention and without introducing subjective bias by outlier rejection. We demonstrate the Bayesian inference protocol on the known survivin:borealin interaction and on the putative protein-protein interactions between human survivin and two members of the human Shugoshin-like family (hSgol1 and hSgol2). These interactions were identified in a protein microarray binding assay against survivin and confirmed by MicroScale Thermophoresis.
Highlights
During a standard MST experiment the solutions of two molecules are mixed: one molecule has a fluorescence label while the other is unlabeled
Biochemical process and sample handing error can result in an anomalous observations
To fully realize the power of automation, the anomalous observations need to be treated without human influence
Summary
During a standard MST experiment the solutions of two molecules are mixed: one molecule has a fluorescence label while the other is unlabeled. After an appropriate incubation time the observed fluorophore tagged biomolecules distribute themselves into complexes and free components depending on their concentrations and the dissociation constant of the interaction. The interaction of a fluorescent biomolecule with other molecules may provoke changes in the molecular properties (size, hydration and charge) and affects its thermophoretic movement[1]. These changes are used to measure affinities and to calculate the dissociation constant, KD, down to the range of a few hundred picomolar (pM)[2]. The increased data acquisition rate has to be complemented with robust, automated procedures to analyze the binding curves and to quantify the uncertainty of determined dissociation constants[5]. Clinical relevance of Sgos is less explored and could be indirectly connected to the novel therapeutic interest in protein phosphatase 2 A (PP2A) agonists[17]
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