ArticleScientific reports2017
Bayesian Analysis of MicroScale Thermophoresis Data to Quantify Affinity of Protein:Protein Interactions with Human Survivin.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Survivin prevents the polycomb repressor complex 2 from methylating histone 3 lysine 27.iScience · 2023Article
- Production of high-complexity frameshift neoantigen peptide microarrays.RSC advances · 2020Article
- Selective imaging of solid tumours via the calcium-dependent high-affinity binding of a cyclic octapeptide to phosphorylated Annexin A2.Nature biomedical engineering · 2020Article
- Bayesian machine learning improves single-wavelength anomalous diffraction phasing.Acta crystallographica. Section A, Foundations and advances · 2019Article
- Electrospray Functionalization of Titanium Dioxide Nanoparticles with Transferrin for Cerenkov Radiation Induced Cancer Therapy.ACS applied bio materials · 2019Article
- Looking for Novel Capsid Protein Multimerization Inhibitors of Feline Immunodeficiency Virus.Pharmaceuticals (Basel, Switzerland) · 2018Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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