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Protein:DNA interactions are essential to a range of processes that maintain and express the information encoded in the genome. Structural modeling is an approach that aims to understand these interactions at the physicochemical level. It has been proposed that structural modeling can lead to deeper understanding of the mechanisms of protein:DNA interactions, and that progress in this field can not only help to rationalize the observed specificities of DNA-binding proteins but also to allow researchers to engineer novel DNA site specificities. In this review we discuss recent developments in the structural description of protein:DNA interactions and specificity, as well as the challenges facing the field in the future., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Computational protein design relies on a number of approximations to efficiently search the huge sequence space available to proteins. The fixed backbone and rotamer approximations in particular are important for formulating protein design as a discrete combinatorial optimization problem. However, the resulting coarse-grained sampling of possible side-chain terminal positions is problematic for the design of protein function, which depends on precise positioning of side-chain atoms. Although backbone flexibility can greatly increase the conformation freedom of side-chain functional groups, it is not obvious which backbone movements will generate the critical constellation of atoms responsible for protein function. Here, we report an automated method for identifying protein backbone movements that can give rise to any specified set of desired side-chain atomic placements and interactions, using protein-DNA interfaces as a model system. We use a library of previously observed protein-DNA interactions (motifs) and a rotamer-based description of side-chain conformation freedom to identify placements for the protein backbone that can give rise to a favorable side-chain interaction with DNA. We describe a tree-search algorithm for identifying those combinations of interactions from the library that can be realized with minimal perturbation of the protein backbone. We compare the efficiency of this method with the alternative approach of building and screening alternate backbone conformations.

The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein-DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI using a physically realistic atomic-level forcefield. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site approximately 10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease-recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.

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.

Protein-DNA interactions are crucial for many biological processes. Attempts to model these interactions have generally taken the form of amino acid-base recognition codes or purely sequence-based profile methods, which depend on the availability of extensive sequence and structural information for specific structural families, neglect side-chain conformational variability, and lack generality beyond the structural family used to train the model. Here, we take advantage of recent advances in rotamer-based protein design and the large number of structurally characterized protein-DNA complexes to develop and parameterize a simple physical model for protein-DNA interactions. The model shows considerable promise for redesigning amino acids at protein-DNA interfaces, as design calculations recover the amino acid residue identities and conformations at these interfaces with accuracies comparable to sequence recovery in globular proteins. The model shows promise also for predicting DNA-binding specificity for fixed protein sequences: native DNA sequences are selected correctly from pools of competing DNA substrates; however, incorporation of backbone movement will likely be required to improve performance in homology modeling applications. Interestingly, optimization of zinc finger protein amino acid sequences for high-affinity binding to specific DNA sequences results in proteins with little or no predicted specificity, suggesting that naturally occurring DNA-binding proteins are optimized for specificity rather than affinity. When combined with algorithms that optimize specificity directly, the simple computational model developed here should be useful for the engineering of proteins with novel DNA-binding specificities.