We describe the identification and characterization of novel homing endonucleases using genome database mining to identify putative target sites, followed by high throughput activity screening in a bacterial selection system. We characterized the substrate specificity and kinetics of these endonucleases by monitoring DNA cleavage events with deep sequencing. The endonuclease specificities revealed by these experiments can be partially recapitulated using 3D structure-based computational models. Analysis of these models together with genome sequence data provide insights into how alternative endonuclease specificities were generated during natural evolution.

Homing endonucleases (HEs) can be used to induce targeted genome modification to reduce the fitness of pathogen vectors such as the malaria-transmitting Anopheles gambiae and to correct deleterious mutations in genetic diseases. We describe the creation of an extensive set of HE variants with novel DNA cleavage specificities using an integrated experimental and computational approach. Using computational modeling and an improved selection strategy, which optimizes specificity in addition to activity, we engineered an endonuclease to cleave in a gene associated with Anopheles sterility and another to cleave near a mutation that causes pyruvate kinase deficiency. In the course of this work we observed unanticipated context-dependence between bases which will need to be mechanistically understood for reprogramming of specificity to succeed more generally.

The accuracy of a homology model based on the structure of a distant relative or other topologically equivalent protein is primarily limited by the quality of the alignment. Here we describe a systematic approach for sequence-to-structure alignment, called 'K*Sync', in which alignments are generated by dynamic programming using a scoring function that combines information on many protein features, including a novel measure of how obligate a sequence region is to the protein fold. By systematically varying the weights on the different features that contribute to the alignment score, we generate very large ensembles of diverse alignments, each optimal under a particular constellation of weights. We investigate a variety of approaches to select the best models from the ensemble, including consensus of the alignments, a hydrophobic burial measure, low- and high-resolution energy functions, and combinations of these evaluation methods. The effect on model quality and selection resulting from loop modeling and backbone optimization is also studied. The performance of the method on a benchmark set is reported and shows the approach to be effective at both generating and selecting accurate alignments. The method serves as the foundation of the homology modeling module in the Robetta server.

Rare-cleaving endonucleases have emerged as important tools for making targeted genome modifications. While multiple platforms are now available to generate reagents for research applications, each existing platform has significant limitations in one or more of three key properties necessary for therapeutic application: efficiency of cleavage at the desired target site, specificity of cleavage (i.e. rate of cleavage at 'off-target' sites), and efficient/facile means for delivery to desired target cells. Here, we describe the development of a single-chain rare-cleaving nuclease architecture, which we designate 'megaTAL', in which the DNA binding region of a transcription activator-like (TAL) effector is used to 'address' a site-specific meganuclease adjacent to a single desired genomic target site. This architecture allows the generation of extremely active and hyper-specific compact nucleases that are compatible with all current viral and nonviral cell delivery methods.

Site-specific homing endonucleases are capable of inducing gene conversion via homologous recombination. Reprogramming their cleavage specificities allows the targeting of specific biological sites for gene correction or conversion. We used computational protein design to alter the cleavage specificity of I-MsoI for three contiguous base pair substitutions, resulting in an endonuclease whose activity and specificity for its new site rival that of wild-type I-MsoI for the original site. Concerted design for all simultaneous substitutions was more successful than a modular approach against individual substitutions, highlighting the importance of context-dependent redesign and optimization of protein-DNA interactions. We then used computational design based on the crystal structure of the designed complex, which revealed significant unanticipated shifts in DNA conformation, to create an endonuclease that specifically cleaves a site with four contiguous base pair substitutions. Our results demonstrate that specificity switches for multiple concerted base pair substitutions can be computationally designed, and that iteration between design and structure determination provides a route to large scale reprogramming of specificity.

Although engineered LAGLIDADG homing endonucleases (LHEs) are finding increasing applications in biotechnology, their generation remains a challenging, industrial-scale process. As new single-chain LAGLIDADG nuclease scaffolds are identified, however, an alternative paradigm is emerging: identification of an LHE scaffold whose native cleavage site is a close match to a desired target sequence, followed by small-scale engineering to modestly refine recognition specificity. The application of this paradigm could be accelerated if methods were available for fusing N- and C-terminal domains from newly identified LHEs into chimeric enzymes with hybrid cleavage sites. Here we have analyzed the structural requirements for fusion of domains extracted from six single-chain I-OnuI family LHEs, spanning 40-70% amino acid identity. Our analyses demonstrate that both the LAGLIDADG helical interface residues and the linker peptide composition have important effects on the stability and activity of chimeric enzymes. Using a simple domain fusion method in which linker peptide residues predicted to contact their respective domains are retained, and in which limited variation is introduced into the LAGLIDADG helix and nearby interface residues, catalytically active enzymes were recoverable for ≈ 70% of domain chimeras. This method will be useful for creating large numbers of chimeric LHEs for genome engineering applications.

LAGLIDADG homing endonucleases (LHEs) are compact endonucleases with 20-22 bp recognition sites, and thus are ideal scaffolds for engineering site-specific DNA cleavage enzymes for genome editing applications. Here, we describe a general approach to LHE engineering that combines rational design with directed evolution, using a yeast surface display high-throughput cleavage selection. This approach was employed to alter the binding and cleavage specificity of the I-Anil LHE to recognize a mutation in the mouse Bruton tyrosine kinase (Btk) gene causative for mouse X-linked immunodeficiency (XID)-a model of human X-linked agammaglobulinemia (XLA). The required re-targeting of I-AniI involved progressive resculpting of the DNA contact interface to accommodate nine base differences from the native cleavage sequence. The enzyme emerging from the progressive engineering process was specific for the XID mutant allele versus the wild-type (WT) allele, and exhibited activity equivalent to WT I-AniI in vitro and in cellulo reporter assays. Fusion of the enzyme to a site-specific DNA binding domain of transcription activator-like effector (TALE) resulted in a further enhancement of gene editing efficiency. These results illustrate the potential of LHE enzymes as specific and efficient tools for therapeutic genome engineering.

Protein-DNA interactions play a central role in transcriptional regulation and other biological processes. Investigating the mechanism of binding affinity and specificity in protein-DNA complexes is thus an important goal. Here we develop a simple physical energy function, which uses electrostatics, solvation, hydrogen bonds and atom-packing terms to model direct readout and sequence-specific DNA conformational energy to model indirect readout of DNA sequence by the bound protein. The predictive capability of the model is tested against another model based only on the knowledge of the consensus sequence and the number of contacts between amino acids and DNA bases. Both models are used to carry out predictions of protein-DNA binding affinities which are then compared with experimental measurements. The nearly additive nature of protein-DNA interaction energies in our model allows us to construct position-specific weight matrices by computing base pair probabilities independently for each position in the binding site. Our approach is less data intensive than knowledge-based models of protein-DNA interactions, and is not limited to any specific family of transcription factors. However, native structures of protein-DNA complexes or their close homologs are required as input to the model. Use of homology modeling can significantly increase the extent of our approach, making it a useful tool for studying regulatory pathways in many organisms and cell types.