ABSTRACT
Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.
Subject(s)
Deep Learning , Transcription Factors , Humans , Transcription Factors/genetics , Transcription Factors/metabolism , Zinc Fingers/genetics , Gene Expression Regulation , DNA/geneticsABSTRACT
The widely used Streptococcus pyogenes Cas9 (SpCas9) nuclease derives its DNA targeting specificity from protein-DNA contacts with protospacer adjacent motif (PAM) sequences, in addition to base-pairing interactions between its guide RNA and target DNA. Previous reports have established that the PAM specificity of SpCas9 can be altered via positive selection procedures for directed evolution or other protein engineering strategies. Here we exploit in vivo directed evolution systems that incorporate simultaneous positive and negative selection to evolve SpCas9 variants with commensurate or improved activity on NAG PAMs relative to wild type and reduced activity on NGG PAMs, particularly YGG PAMs. We also show that the PAM preferences of available evolutionary intermediates effectively determine whether similar counterselection PAMs elicit different selection stringencies, and demonstrate that negative selection can be specifically increased in a yeast selection system through the fusion of compensatory zinc fingers to SpCas9.
Subject(s)
CRISPR-Associated Protein 9/metabolism , CRISPR-Cas Systems , DNA/metabolism , Gene Editing/methods , RNA, Guide, Kinetoplastida/metabolism , Streptococcus pyogenes/metabolism , Amino Acid Sequence , CRISPR-Associated Protein 9/genetics , Cell Line, Tumor , DNA/chemistry , DNA/genetics , Directed Molecular Evolution/methods , Humans , Mutation , Nucleic Acid Conformation , Nucleotide Motifs/genetics , Protein Engineering/methods , RNA, Guide, Kinetoplastida/genetics , Streptococcus pyogenes/genetics , Substrate SpecificityABSTRACT
The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein-DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion.
