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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.

Bayesian Analysis of MicroScale Thermophoresis Data to Quantify Affinity of Protein:Protein Interactions with Human Survivin