A bioinformatic toolbox for the analysis and optimization of a high-throughput deep mutational scanning assay: development and case-study

01 November 2020 → 31 October 2022
Research Foundation - Flanders (FWO)
Research disciplines
  • Natural sciences
    • Development of bioinformatics software, tools and databases
    • Structural bioinformatics and computational proteomics
    • Interactomics
  • Medical and health sciences
    • Cancer biology
  • Engineering and technology
    • Bio-informatics
structural biology cancer high-throughput screening bioinformatics protein-protein interactions
Project description

Deep Mutational Scanning (DMS) is a recent methodology for characterizing and functionally screening protein mutations by coupling a property of interest to a DNA-sequencing read-out. Via next-generation sequencing, its throughput is unmatched by traditional assays such as two-hybrid screening, which can only be realistically performed on a few variants at a time. To tackle the shortcomings of current DMS-implementations, we developed a new cost-efficient, full-length, in vivo human DMS, of which the first trial experiments were already successful. A first goal of this proposal is the development and optimization of a bioinformatic pipeline to analyze the novel data this assay produces, which in turn will be used for experimental optimization of the DMS-assay itself. A second goal consists of exploring the best way to process and interpret the results, such as statistically clustering relevant (i.e. impactful on the studied property) mutations on the 3D protein (surface). Lastly, to prove the assay’s added value, it will be used in a case-study exploring the impact of mutations on in vivo protein-protein interactions (PPI) of three cancer driver genes (TP53, PTEN, PIK3R1). This biomedically relevant application enables us to leverage large amounts of publicly available data (e.g. TCGA) to not only validate the assay, but also allows for studies of unprecedented scale on the role of specific PPIs in cancer-related pathways, tumor expression profiles and disease progression.