Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions

Abstract

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Pooled PPIseq: screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform

“…We then combined the pools of diploid PPIseq assay strains for quantification of the selection. A challenge for barcode-sequencing screens is in balancing the library such that sufficient read depth is available to adequately quantify individual barcode abundance in highly complex libraries, even as those barcoded lineages change in abundance in response to selection-based assays [76]. Thus, we first cultured the negative control, positive control, and test strain pools separately for one cycle of MTX selection before combining the pools with a relatively small abundance of positive controls.…”