De novo identification of essential protein domains from CRISPR-Cas9 tiling-sgRNA knockout screens

Abstract

High-throughput CRISPR-Cas9 knockout screens using a tiling-sgRNA design permit in situ evaluation of protein domain function. Here, to facilitate de novo identification of essential protein domains from such screens, we propose ProTiler, a computational method for the robust mapping of CRISPR knockout hyper-sensitive (CKHS) regions, which refer to the protein regions associated with a strong sgRNA dropout effect in the screens. Applied to a published CRISPR tiling screen dataset, ProTiler identifies 175 CKHS regions in 83 proteins. Of these CKHS regions, more than 80% overlap with annotated Pfam domains, including all of the 15 known drug targets in the dataset. ProTiler also reveals unannotated essential domains, including the N-terminus of the SWI/SNF subunit SMARCB1, which is validated experimentally. Surprisingly, the CKHS regions are negatively correlated with phosphorylation and acetylation sites, suggesting that protein domains and post-translational modification sites have distinct sensitivities to CRISPR-Cas9 mediated amino acids loss.

Maximizing CRISPR/Cas9 phenotype penetrance applying predictive modeling of editing outcomes in Xenopus and zebrafish embryos

“…Evidently, predictions of gene editing outcomes can also be harnessed to specifically design gRNAs with known dominant or loss-of-function in-frame mutations. This can be exploited for structure-function studies of protein variants in a cell or development biology context 19 . Obviously, the analysis of such experiments would be challenging in the F 0 generation but achieving dominant editing of specific in-frame variants heavily increases the chance of passing the exact genotype of interest to the F 1 generation.…”

“…Depending on their position in the primary mRNA, PTCs can subject the resulting transcript to nonsense-mediated mRNA decay (NMD) and effective cellular knockout (KO) of the CRISPR/Cas9-targeted gene can thus be obtained. In contrast, in-frame INDELs in coding exons will lead to the loss or gain of amino acids, which can still result in retention of potentially functional protein variants 19 . We believe that in many cases the inability to retrieve phenotypes in Xenopus and zebrafish F 0 CRISPR/Cas9 edited animals is the consequence of in-frame mutations in a substantial number of cells in the mosaic mutant animal.…”

PINCER: improved CRISPR/Cas9 screening by efficient cleavage at conserved residues

“…However, domain annotations are often sparse for poorly characterized gene families and short structural proteins can be entirely annotated as single domains. To solve the sparsity of domain annotations and increase resolution, other groups have described targeting highly conserved nucleotides (CRISPR-DO, CRISPR-FOCUS), amino acids by substitutions (CROATAN, Protiler), or amino acids by deletions (CRISPRO) to improve sgRNA activity ( 36 , 41 , 45 , 49–50 ). The rationale that gene regions unaltered through evolutionary history are under purifying selection and are therefore likely to be essential for gene function is a classic and effective framework in comparative genomics ( 51 ).…”

“…enzymatic activity) and guide targets (i.e. conservation) to maximize phenotypic knockout of target genes ( 36 , 41–42 , 45 , 48–50 ). Only four guide design tools provide both enzymatic efficiency predictions and either nucleotide conservation scores (CRISPR-DO, CRISPR-FOCUS) or domain annotations (GUIDES, PAVOOC) ( 42 , 45 , 48–49 ).…”

“…However, in all four the weights of these features in guide selection are neither justified by training data nor suggested, and users must weigh features for themselves. The three tools that have come closest to integrating guide and target features are ProTiler, CRISPRO and CROATAN ( 36 , 41 , 50 ). ProTiler presents a sophisticated method to predict the most CRISPR-sensitive regions of genes (including novel domains), by incorporating substitution-based protein conservation (SIFT), percentage of transcripts covered, amino acid identity and annotations of domains (Pfam), protein secondary structure and post-translational modifications into a gene targeting score, but critically omits enzymatic efficiency prediction and does not present a guide library ( 36 ).…”