Perturbing proteomes at single residue resolution using base editing

Abstract

<jats:title>Abstract</jats:title><jats:p>Base editors derived from CRISPR-Cas9 systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. Here, we test the ability of an editor based on a cytidine deaminase, the Target-AID base editor, to systematically modify genes genome-wide using the set of yeast essential genes. We tested the effect of mutating around 17,000 individual sites in parallel across more than 1,500 genes in a single experiment. We identified over 1,100 sites at which mutations have a significant impact on fitness. Using previously determined and preferred Target-AID mutational outcomes, we predicted the protein variants caused by each of these gRNAs. We found that gRNAs with significant effects on fitness are enriched in variants predicted to be deleterious by independent methods based on site conservation and predicted protein destabilization. Finally, we identify key features to design effective gRNAs in the context of base editing. Our results show that base editing is a powerful tool to identify key amino acid residues at the scale of proteomes.</jats:p>

Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

“…As described previously and demonstrated above, MAVEs provide a wealth of data not only for use in medical applications ( Weile and Roth, 2018 ; Stein et al, 2019 ) but also for understanding basic properties of proteins ( Dunham and Beltrao, 2020 ). Despite recent advances in proteome-wide experiments ( Després et al, 2020 ), it is still not possible to probe all possible variants in all proteins experimentally, and thus computational methods remain an important supplement to predict and understand variant effects. Experimental data from MAVEs are thus increasingly used to benchmark prediction methods, as they provide a broad view of the effect of amino acid substitutions in proteins ( Hopf et al, 2017 ; Jepsen et al, 2020 ; Livesey and Marsh, 2020 ; Reeb et al, 2020 ).…”

“…Despite recent advances in proteome-wide experiments(Després et al, 2020), it is still not possible to probe all possible variants in all proteins experimentally, and thus computational methods remain an important supplement to predict and understand variant effects. Experimental data from MAVEs are thus increasingly used to benchmark prediction methods, as they provide a broad view of the effect of amino acid substitutions in proteins(Hopf et al, 2017; Jepsen et al, 2020;Livesey and Marsh, 2020;Reeb et al, 2020).…”

CRISPR-Based Genetic Manipulation of Candida Species: Historical Perspectives and Current Approaches

“…As described, dCas9-based CRISPR systems have been used as part of CRISPRi and CRISPRa systems in C. albicans (Román et al, 2019a;Wensing et al, 2019), and other dCas9 fusions could be similarly used for epigenetic silencing in Candida species (Gjaltema and Rots, 2020). Other emerging techniques, such as CRISPR-based RNA editing (Jing et al, 2018) and base editing (Després et al, 2020) could have exciting applications in Candida species, to enable targeted manipulation of RNA transcripts without DNA edits, and targeted DNA nucleotide substitutions in the absence of DSBs, respectively. The versatility of CRISPR platforms in Candida species allows for potential applications in multiplexed genome editing, whereby multiple genes can be targeted in a single transformation.…”

Mutants libraries reveal negative design shielding proteins from mis-assembly and re-localization in cells

“…At the protein level, alanine scans have revealed how individual residues contribute to protein function, stability, and binding affinity (2)(3)(4). More recently, systematic mappings have been widely used to connect sequence variability to changes in protein structure (5,6), stability (7)(8)(9), solubility (10), and functionality (2,(11)(12)(13)(14). Similar efforts have been made to map the impact of mutations in protein-ligand (15,16) and protein-protein interactions (17)(18)(19)(20)(21).…”