Back

Mechanistic Insights into Proteomic Mutation-Phenotype Linkages from Tiling Mutagenesis Screens

He, W.; Huang, J.-W.; Wang, Y.; Hayward, S. B.; Leuzzi, G.; Fu, R.; Wang, S.; Vaitsiankova, A.; Bedford, M. T.; Guerois, R.; Ciccia, A.; Xu, H.

2025-04-23 bioinformatics
10.1101/2025.04.17.649336 bioRxiv
Show abstract

High-throughput mutagenesis screens are powerful tools for mapping mutations to phenotypes. However, deciphering the molecular mechanisms that link mutations to phenotypic outcomes remains a significant challenge. Here, we present ProTiler-Mut, a versatile computational framework that harnesses tiling mutagenesis screens, which introduce variants across entire protein sequences, to facilitate investigation of mutation-to-phenotype associations at multiple levels, including individual residues, protein substructures, and protein-protein interactions (PPIs). As demonstrated through our analyses of base editing (BE) screens targeting DNA Damage Response (DDR) proteins and T cell regulators, ProTiler-Mut provides novel insights into the mutation-phenotype linkages, including: i) refined classification of mutation that reveals separation-of-function (SOF) category beyond the conventional binary classification of loss-of-function (LOF) and gain-of-function (GOF); ii) definition of phenotype-associated hotspot substructures that enable the inference of the function of unscreened pathogenic mutations; and iii) identification of phenotype-associated PPIs disrupted by functional mutations. Through ProTiler-Mut analyses, we identified a substructure harboring pathogenic GOF mutations that disrupt interactions between the kinases MAPK1 and RSK1, leading to MAPK1 activation and elevated expression of the immune checkpoint receptor PD-1. Furthermore, we demonstrate the applicability of ProTiler-Mut to various mutagenesis screening platforms, highlighting its broad utility and generalizability.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.