A theoretical model of restriction endonuclease NlaIV in complex with DNA, predicted by fold recognition and validated by site-directed mutagenesis and circular dichroism spectroscopy.

Restriction enzymes (REases) are commercial reagents commonly used in DNA manipulations and mapping. They are regarded as very attractive models for studying protein-DNA interactions and valuable targets for protein engineering. Their amino acid sequences usually show no similarities to other proteins, with rare exceptions of other REases that recognize identical or very similar sequences. Hence, they are extremely hard targets for structure prediction and modeling. NIaIV is a Type II REase, which recognizes the interrupted palindromic sequence GGNNCC (where N indicates any base) and cleaves it in the middle, leaving blunt ends. NIaIV shows no sequence similarity to other proteins and virtually nothing is known about its sequence-structure-function relationships. Using protein fold recognition, we identified a remote relationship between NIaIV and EcoRV, an extensively studied REase, which recognizes the GATATC sequence and whose crystal structure has been determined. Using the 'FRankenstein's monster' approach we constructed a comparative model of NIaIV based on the EcoRV template and used it to predict the catalytic and DNA-binding residues. The model was validated by site-directed mutagenesis and analysis of the activity of the mutants in vivo and in vitro as well as structural characterization of the wild-type enzyme and two mutants by circular dichroism spectroscopy. The structural model of the NIaIV-DNA complex suggests regions of the protein sequence that may interact with the 'non-specific' bases of the target and thus it provides insight into the evolution of sequence specificity in restriction enzymes and may help engineer REases with novel specificities. Before this analysis was carried out, neither the three-dimensional fold of NIaIV, its evolutionary relationships or its catalytic or DNA-binding residues were known. Hence our analysis may be regarded as a paradigm for studies aiming at reducing 'white spaces' on the evolutionary landscape of sequence-function relationships by combining bioinformatics with simple experimental assays.