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

Journal article
Authors Maria-Jose Garcia-Bonete
Maja Jensen
Christian V Recktenwald
Sandra Rocha
Volker Stadler
Maria Bokarewa
Gergely Katona
Published in Scientific reports
Volume 7
Issue 1
Pages 16816
ISSN 2045-2322
Publication year 2017
Published at Institute of Medicine, Department of Rheumatology and Inflammation Research
Department of Chemistry and Molecular Biology
Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology
Pages 16816
Language en
Links dx.doi.org/10.1038/s41598-017-17071...
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Biochemistry, Biophysics, Statistics, computer and systems science

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.

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