Role of inter and intramolecular interactions in protein-DNA recognition

PDBparam: Online Resource for Computing Structural Parameters of Proteins

Understanding the structure-function relationship in proteins is a longstanding goal in molecular and computational biology. The development of structure-based parameters has helped to relate the structure with the function of a protein. Although several structural features have been reported in the literature, no single server can calculate a wide-ranging set of structure-based features from protein three-dimensional structures. In this work, we have developed a web-based tool, PDBparam, for computing more than 50 structure-based features for any given protein structure. These features are classified into four major categories: (i) interresidue interactions, which include short-, medium-, and long-range interactions, contact order, long-range order, total contact distance, contact number, and multiple contact index, (ii) secondary structure propensities such as α-helical propensity, β-sheet propensity, and propensity of amino acids to exist at various positions of α-helix and amino acid compositions in high B-value regions, (iii) physicochemical properties containing ionic interactions, hydrogen bond interactions, hydrophobic interactions, disulfide interactions, aromatic interactions, surrounding hydrophobicity, and buriedness, and (iv) identification of binding site residues in protein-protein, protein-nucleic acid, and protein-ligand complexes. The server can be freely accessed at http://www.iitm.ac.in/bioinfo/pdbparam/. We suggest the use of PDBparam as an effective tool for analyzing protein structures.

Topology of RNA-protein nucleobase-amino acid π-π interactions and comparison to analogous DNA-protein π-π contacts

The present work analyzed 120 high-resolution X-ray crystal structures and identified 335 RNA-protein π-interactions (154 nonredundant) between a nucleobase and aromatic (W, H, F, or Y) or acyclic (R, E, or D) π-containing amino acid. Each contact was critically analyzed (including using a visual inspection protocol) to determine the most prevalent composition, structure, and strength of π-interactions at RNA-protein interfaces. These contacts most commonly involve F and U, with U:F interactions comprising one-fifth of the total number of contacts found. Furthermore, the RNA and protein π-systems adopt many different relative orientations, although there is a preference for more parallel (stacked) arrangements. Due to the variation in structure, the strength of the intermolecular forces between the RNA and protein components (as determined from accurate quantum chemical calculations) exhibits a significant range, with most of the contacts providing significant stability to the associated RNA-protein complex (up to -65 kJ mol(-1)). Comparison to the analogous DNA-protein π-interactions emphasizes differences in RNA- and DNA-protein π-interactions at the molecular level, including the greater abundance of RNA contacts and the involvement of different nucleobase/amino acid residues. Overall, our results provide a clearer picture of the molecular basis of nucleic acid-protein binding and underscore the important role of these contacts in biology, including the significant contribution of π-π interactions to the stability of nucleic acid-protein complexes. Nevertheless, more work is still needed in this area in order to further appreciate the properties and roles of RNA nucleobase-amino acid π-interactions in nature.

Zinc finger protein binding to DNA: an energy perspective using molecular dynamics simulation and free energy calculations on mutants of both zinc finger domains and their specific DNA bases

Energy calculations based on MM-GBSA were employed to study various zinc finger protein (ZF) motifs binding to DNA. Mutants of both the DNA bound to their specific amino acids were studied. Calculated energies gave evidence for a relationship between binding energy and affinity of ZF motifs to their sites on DNA. ΔG values were -15.82(12), -3.66(12), and -12.14(11.6) kcal/mol for finger one, finger two, and finger three, respectively. The mutations in the DNA bases reduced the value of the negative energies of binding (maximum value for ΔΔG = 42Kcal/mol for F1 when GCG mutated to GGG, and ΔΔG = 22 kcal/mol for F2, the loss in total energy of binding originated in the loss in electrostatic energies upon mutation (r = .98). The mutations in key amino acids in the ZF motif in positions-1, 2, 3, and 6 showed reduced binding energies to DNA with correlation coefficients between total free energy and electrostatic was .99 and with Van der Waal was .93. Results agree with experimentally found selectivity which showed that Arginine in position-1 is specific to G, while Aspartic acid (D) in position 2 plays a complicated role in binding. There is a correlation between the MD calculated free energies of binding and those obtained experimentally for prepared ZF motifs bound to triplet bases in other reports (), our results may help in the design of ZF motifs based on the established recognition codes based on energies and contributing energies to the total energy.

Landscape of π-π and sugar-π contacts in DNA-protein interactions

There were 1765 contacts identified between DNA nucleobases or deoxyribose and cyclic (W, H, F, Y) or acyclic (R, E, D) amino acids in 672 X-ray structures of DNA-protein complexes. In this first study to compare π-interactions between the cyclic and acyclic amino acids, visual inspection was used to categorize amino acid interactions as nucleobase π-π (according to biological edge) or deoxyribose sugar-π (according to sugar edge). Overall, 54% of contacts are nucleobase π-π interactions, which involve all amino acids, but are more common for Y, F, and R, and involve all DNA nucleobases with similar frequencies. Among binding arrangements, cyclic amino acids prefer more planar (stacked) π-systems than the acyclic counterparts. Although sugar-π interactions were only previously identified with the cyclic amino acids and were found to be less common (38%) than nucleobase-cyclic amino acid contacts, sugar-π interactions are more common than nucleobase π-π contacts for the acyclic series (61% of contacts). Similar to DNA-protein π-π interactions, sugar-π contacts most frequently involve Y and R, although all amino acids adopt many binding orientations relative to deoxyribose. These DNA-protein π-interactions stabilize biological systems, by up to approximately -40 kJ mol(-1) for neutral nucleobase or sugar-amino acid interactions, but up to approximately -95 kJ mol(-1) for positively or negatively charged contacts. The high frequency and strength, despite variation in structure and composition, of these π-interactions point to an important function in biological systems.

Role of indirect readout mechanism in TATA box binding protein-DNA interaction

Gene expression generally initiates from recognition of TATA-box binding protein (TBP) to the minor groove of DNA of TATA box sequence where the DNA structure is significantly different from B-DNA. We have carried out molecular dynamics simulation studies of TBP-DNA system to understand how the DNA structure alters for efficient binding. We observed rigid nature of the protein while the DNA of TATA box sequence has an inherent flexibility in terms of bending and minor groove widening. The bending analysis of the free DNA and the TBP bound DNA systems indicate presence of some similar structures. Principal coordinate ordination analysis also indicates some structural features of the protein bound and free DNA are similar. Thus we suggest that the DNA of TATA box sequence regularly oscillates between several alternate structures and the one suitable for TBP binding is induced further by the protein for proper complex formation.

Computational approaches for predicting the binding sites and understanding the recognition mechanism of protein-DNA complexes

Protein-DNA recognition plays an important role in the regulation of gene expression. Understanding the influence of specific residues for protein-DNA interactions and the recognition mechanism of protein-DNA complexes is a challenging task in molecular and computational biology. Several computational approaches have been put forward to tackle these problems from different perspectives: (i) development of databases for the interactions between protein and DNA and binding specificity of protein-DNA complexes, (ii) structural analysis of protein-DNA complexes, (iii) discriminating DNA-binding proteins from amino acid sequence, (iv) prediction of DNA-binding sites and protein-DNA binding specificity using sequence and/or structural information, and (v) understanding the recognition mechanism of protein-DNA complexes. In this review, we focus on all these issues and extensively discuss the advancements on the development of comprehensive bioinformatics databases for protein-DNA interactions, efficient tools for identifying the binding sites, and plausible mechanisms for understanding the recognition of protein-DNA complexes. Further, the available online resources for understanding protein-DNA interactions are collectively listed, which will serve as ready-to-use information for the research community.

Evaluating how discrete water molecules affect protein-DNA π-π and π(+)-π stacking and T-shaped interactions: the case of histidine-adenine dimers

Changes in the magnitude of (M06-2X/6-31+G(d,p)) π-π stacking and T-shaped (nucleobase-edge and amino acid-edge) interactions between (neutral or protonated) histidine (His) and adenine (A) dimers upon microsolvation with up to four discrete water molecules were determined. A variety of histidine-water interactions were considered including conventional (N-H···O, N···H-O, C-H···O) hydrogen bonding and nonconventional (X-H···π (neutral His) or lone-pair···π (protonated His)) contacts. Overall, the effects of discrete His-H(2)O interactions on the neutral histidine-adenine π-π contacts are negligible (<3 kJ mol(-1) or 15%) regardless of the type of water binding, the number of water molecules bound, or the His-A dimer (stacked or (amino acid- or nucleobase-edge) T-shaped) configuration. This suggests that previously reported gas-phase binding strengths for a variety of neutral amino acid-nucleobase dimers are likely relevant for a wide variety of (microsolvated) environments. In contrast, the presence of water decreases the histidine-adenine π(+)-π interaction by up to 15 kJ mol(-1) (or 30%) for all water binding modes and orientations, as well as different stacked and T-shaped His(+)-A dimers. Regardless of the larger effect of discrete histidine-water interactions on the magnitude of the π(+)-π compared with π-π interactions, the π(+)-π binding strengths remain substantially larger than the corresponding π-π contacts. These findings emphasize the distinct nature of π(+)-π and π-π interactions and suggest that π(+)-π contacts can provide significant stabilization in biological systems relative to π-π contacts under many different environmental conditions.

Scoring function based approach for locating binding sites and understanding recognition mechanism of protein-DNA complexes

Protein-DNA recognition plays an essential role in the regulation of gene expression. Understanding the recognition mechanism of protein-DNA complexes is a challenging task in molecular and computational biology. In this work, a scoring function based approach has been developed for identifying the binding sites and delineating the important residues for binding in protein-DNA complexes. This approach considers both the repulsive interactions and the effect of distance between atoms in protein and DNA. The results showed that positively charged, polar, and aromatic residues are important for binding. These residues influence the formation of electrostatic, hydrogen bonding, and stacking interactions. Our observation has been verified with experimental binding specificity of protein-DNA complexes and found to be in good agreement with experiments. The comparison of protein-RNA and protein-DNA complexes reveals that the contribution of phosphate atoms in DNA is twice as large as in protein-RNA complexes. Furthermore, we observed that the positively charged, polar, and aromatic residues serve as hotspot residues in protein-RNA complexes, whereas other residues also altered the binding specificity in protein-DNA complexes. Based on the results obtained in the present study and related reports, a plausible mechanism has been proposed for the recognition of protein-DNA complexes.

Use of structural DNA properties for the prediction of transcription-factor binding sites in Escherichia coli

Recognition of genomic binding sites by transcription factors can occur through base-specific recognition, or by recognition of variations within the structure of the DNA macromolecule. In this article, we investigate what information can be retrieved from local DNA structural properties that is relevant to transcription factor binding and that cannot be captured by the nucleotide sequence alone. More specifically, we explore the benefit of employing the structural characteristics of DNA to create binding-site models that encompass indirect recognition for the Escherichia coli model organism. We developed a novel methodology [Conditional Random fields of Smoothed Structural Data (CRoSSeD)], based on structural scales and conditional random fields to model and predict regulator binding sites. The value of relying on local structural-DNA properties is demonstrated by improved classifier performance on a large number of biological datasets, and by the detection of novel binding sites which could be validated by independent data sources, and which could not be identified using sequence data alone. We further show that the CRoSSeD-binding-site models can be related to the actual molecular mechanisms of the transcription factor DNA binding, and thus cannot only be used for prediction of novel sites, but might also give valuable insights into unknown binding mechanisms of transcription factors.

Effects of the biological backbone on stacking interactions at DNA-protein interfaces: the interplay between the backbone···π and π···π components

The (gas-phase) MP2/6-31G*(0.25) π···π stacking interactions between the five natural bases and the aromatic amino acids calculated using (truncated) monomers composed of conjugated rings and/or (extended) monomers containing the biological backbone (either the protein backbone or deoxyribose sugar) were previously compared. Although preliminary energetic results indicated that the protein backbone strengthens, while the deoxyribose sugar either strengthens or weakens, the interaction calculated using truncated models, the reasons for these effects were unknown. The present work explains these observations by dissecting the interaction energy of the extended complexes into individual backbone···π and π···π components. Our calculations reveal that the total interaction energy of the extended complex can be predicted as a sum of the backbone···π and π···π components, which indicates that the biological backbone does not significantly affect the ring system through π-polarization. Instead, we find that the backbone can indirectly affect the magnitude of the π···π contribution by changing the relative ring orientations in extended dimers compared with truncated dimers. Furthermore, the strengths of the individual backbone···π contributions are determined to be significant (up to 18 kJ mol(-1)). Therefore, the origin of the energetic change upon model extension is found to result from a balance between an additional (attractive) backbone···π component and differences in the strength of the π···π interaction. In addition, to understand the effects of the biological backbone on the stacking interactions at DNA-protein interfaces in nature, we analyzed the stacking interactions found in select DNA-protein crystal structures, and verified that an additive approach can be used to examine the strength of these interactions in biological complexes. Interestingly, although the presence of attractive backbone···π contacts is qualitatively confirmed using the quantum theory of atoms in molecules (QTAIM), QTAIM electron density analysis is unable to quantitatively predict the additive relationship of these interactions. Most importantly, this work reveals that both the backbone···π and π···π components must be carefully considered to accurately determine the overall stability of DNA-protein assemblies.

A novel sequence-based method of predicting protein DNA-binding residues, using a machine learning approach

Protein-DNA interactions play an essential role in transcriptional regulation, DNA repair, and many vital biological processes. The mechanism of protein-DNA binding, however, remains unclear. For the study of many diseases, researchers must improve their understanding of the amino acid motifs that recognize DNA. Because identifying these motifs experimentally is expensive and time-consuming, it is necessary to devise an approach for computational prediction. Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues. To make these predictions, we used the properties of the micro-environment of each amino acid from the AAIndex as well as conservation scores. Testing by the cross-validation method, we obtained an overall accuracy of 94.89%. Our method shows that the amino acid micro-environment is important for DNA binding, and that it is possible to identify the protein-DNA binding sites with it.

A generalized conformational energy function of DNA derived from molecular dynamics simulations

Proteins recognize DNA sequences by two different mechanisms. The first is direct readout, in which recognition is mediated by direct interactions between the protein and the DNA bases. The second is indirect readout, which is caused by the dependence of conformation and the deformability of the DNA structure on the sequence. Various energy functions have been proposed to evaluate the contribution of indirect readout to the free-energy changes in complex formations. We developed a new generalized energy function to estimate the dependence of the deformability of DNA on the sequence. This function was derived from molecular dynamics simulations previously conducted on B-DNA dodecamers, each of which had one possible tetramer sequence embedded at its center. By taking the logarithm of the probability distribution function (PDF) for the base-step parameters of the central base-pair step of the tetramer, its ability to distinguish the native sequence from random ones was superior to that with the previous method that approximated the energy function in harmonic form. From a comparison of the energy profiles calculated with these two methods, we found that the harmonic approximation caused significant errors in the conformational energies of the tetramers that adopted multiple stable conformations.

Experimentally based contact energies decode interactions responsible for protein-DNA affinity and the role of molecular waters at the binding interface

A major obstacle towards understanding the molecular basis of transcriptional regulation is the lack of a recognition code for protein–DNA interactions. Using high-quality crystal structures and binding data on the promiscuous family of C₂H₂ zinc fingers (ZF), we decode 10 fundamental specific interactions responsible for protein–DNA recognition. The interactions include five hydrogen bond types, three atomic desolvation penalties, a favorable non-polar energy, and a novel water accessibility factor. We apply this code to three large datasets containing a total of 89 C₂H₂ transcription factor (TF) mutants on the three ZFs of EGR. Guided by molecular dynamics simulations of individual ZFs, we map the interactions into homology models that embody all feasible intra- and intermolecular bonds, selecting for each sequence the structure with the lowest free energy. These interactions reproduce the change in affinity of 35 mutants of finger I (R² = 0.998), 23 mutants of finger II (R² = 0.96) and 31 finger III human domains (R² = 0.94). Our findings reveal recognition rules that depend on DNA sequence/structure, molecular water at the interface and induced fit of the C₂H₂ TFs. Collectively, our method provides the first robust framework to decode the molecular basis of TFs binding to DNA.

Nuance in the double-helix and its role in protein-DNA recognition

It has been known for some time that the double-helix is not a uniform structure but rather exhibits sequence-specific variations that, combined with base-specific intermolecular interactions, offer the possibility of numerous modes of protein-DNA recognition. All-atom simulations have revealed mechanistic insights into the structural and energetic basis of various recognition mechanisms for a number of protein-DNA complexes while coarser grained simulations have begun to provide an understanding of the function of larger assemblies. Molecular simulations have also been applied to the prediction of transcription factor binding sites, while empirical approaches have been developed to predict nucleosome positioning. Studies that combine and integrate experimental, statistical and computational data offer the promise of rapid advances in our understanding of protein-DNA recognition mechanisms.

Insights into protein-DNA interactions through structure network analysis

Protein–DNA interactions are crucial for many cellular processes. Now with the increased availability of structures of protein–DNA complexes, gaining deeper insights into the nature of protein–DNA interactions has become possible. Earlier, investigations have characterized the interface properties by considering pairwise interactions. However, the information communicated along the interfaces is rarely a pairwise phenomenon, and we feel that a global picture can be obtained by considering a protein–DNA complex as a network of noncovalently interacting systems. Furthermore, most of the earlier investigations have been carried out from the protein point of view (protein-centric), and the present network approach aims to combine both the protein-centric and the DNA-centric points of view. Part of the study involves the development of methodology to investigate protein–DNA graphs/networks with the development of key parameters. A network representation provides a holistic view of the interacting surface and has been reported here for the first time. The second part of the study involves the analyses of these graphs in terms of clusters of interacting residues and the identification of highly connected residues (hubs) along the protein–DNA interface. A predominance of deoxyribose–amino acid clusters in β-sheet proteins, distinction of the interface clusters in helix–turn–helix, and the zipper-type proteins would not have been possible by conventional pairwise interaction analysis. Additionally, we propose a potential classification scheme for a set of protein–DNA complexes on the basis of the protein–DNA interface clusters. This provides a general idea of how the proteins interact with the different components of DNA in different complexes. Thus, we believe that the present graph-based method provides a deeper insight into the analysis of the protein–DNA recognition mechanisms by throwing more light on the nature and the specificity of these interactions.

The interaction of proteins with DNA is crucial for several cellular processes. Some insights into the mode of interaction can be obtained from the analysis of the complexed structures. Conventional analyses are based on the identification of pairwise interactions. However, a collective representation of the network of interactions and the analyses of such networks provide valuable information, which is not easy to obtain from pairwise analyses. Although the protein structure networks have been described in the literature, this is the first time that a network representation of protein–DNA is described. Construction and analysis of such networks have given valuable information on protein–DNA interactions in terms of network parameters, such as clusters of interacting residues and hubs, which are highly connected residues. Furthermore, the results also represent both the protein- and the DNA-centric viewpoints, because the analysis is carried out on combined networks. The methodology developed here can lead to predictions, such as important residues responsible for stabilizing protein–DNA interactions, and will be of interest to experimentalists.

Parameterization of azole-bridged dinuclear platinum anticancer drugs via a QM/MM force matching procedure

Azole-bridged diplatinum compounds are promising new anticancer drugs designed to induce small distortions upon DNA alkylation, able to circumvent resistance problems of existing platinum drugs. Hybrid quantum classical (QM/MM) molecular dynamics (MD) simulations of different azole-bridged platinum drugs have recently revealed the nature of the local deformations at the DNA binding site. However, the description of global slow converging rearrangements cannot be addressed by QM/MM MD due to the short time scale accessible. Extensive classical MD simulations are therefore mandatory to describe accurately the structural distortions in the DNA double helix. This issue is now addressed by developing a new set of accurate force field parameters of the platinated moiety via a recently proposed force matching procedure of the classical forces to ab initio forces obtained from QM/MM trajectories. The accuracy of our force field parameters is validated by comparison of structural properties from classical MD and hybrid QM/MM simulations. The structural characteristics of the Pt-lesion are well reproduced during classical MD compared with QM/MM simulations and available experimental data. The global distortions in the DNA duplex upon binding of dinuclear Pt-compounds are very small and rather opposite to those induced by cisplatin. Thus, the force match approach significantly extends the potentialities of molecular simulations in the study of anticancer drugs and of the interactions with their biological targets.

Magnitude and direction of DNA bending induced by screw-axis orientation: influence of sequence, mismatches and abasic sites

DNA-bending flexibility is central for its many biological functions. A new bending restraining method for use in molecular mechanics calculations and molecular dynamics simulations was developed. It is based on an average screw rotation axis definition for DNA segments and allows inducing continuous and smooth bending deformations of a DNA oligonucleotide. In addition to controlling the magnitude of induced bending it is also possible to control the bending direction so that the calculation of a complete (2-dimensional) directional DNA-bending map is now possible. The method was applied to several DNA oligonucleotides including A(adenine)-tract containing sequences known to form stable bent structures and to DNA containing mismatches or an abasic site. In case of G:A and C:C mismatches a greater variety of conformations bent in various directions compared to regular B-DNA was found. For comparison, a molecular dynamics implementation of the approach was also applied to calculate the free energy change associated with bending of A-tract containing DNA, including deformations significantly beyond the optimal curvature. Good agreement with available experimental data was obtained offering an atomic level explanation for stable bending of A-tract containing DNA molecules. The DNA-bending persistence length estimated from the explicit solvent simulations is also in good agreement with experiment whereas the adiabatic mapping calculations with a GB solvent model predict a bending rigidity roughly two times larger.

DNA sequence-specific recognition by a transcriptional regulator requires indirect readout of A-tracts

The bacteriophage Ø29 transcriptional regulator p4 binds to promoters of different intrinsic activities. The p4–DNA complex contains two identical protomers that make similar interactions with the target sequence 5′-AACTTTTT-15 bp-AAAATGTT-3′. To define how the various elements in the target sequence contribute to p4's affinity, we studied p4 binding to a series of mutated binding sites. The binding specificity depends critically on base pairs of the target sequence through both direct as well as indirect readout. There is only one specific contact between a base and an amino acid residue; other contacts take place with the phosphate backbone. Alteration of direct amino acid–base contacts, or mutation of non-contacted A·T base pairs at A-tracts abolished binding. We generated three 5 ns molecular dynamics (MD) simulations to investigate the basis for the p4–DNA complex specificity. Recognition is controlled by the protein and depends on DNA dynamic properties. MD results on protein–DNA contacts and the divergence of p4 affinity to modified binding sites reveal an inherent asymmetry, which is required for p4-specific binding and may be crucial for transcription regulation.

Indirect recognition in sequence-specific DNA binding by Escherichia coli integration host factor: the role of DNA deformation energy

Integration host factor (IHF) is a bacterial histone-like protein whose primary biological role is to condense the bacterial nucleoid and to constrain DNA supercoils. It does so by binding in a sequence-independent manner throughout the genome. However, unlike other structurally related bacterial histone-like proteins, IHF has evolved a sequence-dependent, high affinity DNA-binding motif. The high affinity binding sites are important for the regulation of a wide range of cellular processes. A remarkable feature of IHF is that it employs an indirect readout mechanism to bind and wrap DNA at both the nonspecific and high affinity (sequence-dependent) DNA sites. In this study we assessed the contributions of pre-formed and protein-induced DNA conformations to the energetics of IHF binding. Binding energies determined experimentally were compared with energies predicted for the IHF-induced deformation of the DNA helix (DNA deformation energy) in the IHF-DNA complex. Combinatorial sets of de novo DNA sequences were designed to systematically evaluate the influence of sequence-dependent structural characteristics of the conserved IHF recognition elements of the consensus DNA sequence. We show that IHF recognizes pre-formed conformational characteristics of the consensus DNA sequence at high affinity sites, whereas at all other sites relative affinity is determined by the deformational energy required for nearest-neighbor base pairs to adopt the DNA structure of the bound DNA-IHF complex.

Alteration of sequence specificity of the type II restriction endonuclease HincII through an indirect readout mechanism

The functional and structural consequences of a mutation of the DNA intercalating residue of HincII, Q138F, are presented. Modeling has suggested that the DNA intercalation by Gln-138 results in DNA distortions potentially used by HincII in indirect readout of its cognate DNA, GTYRAC (Y = C or T, R = A or G) (Horton, N. C., Dorner, L. F., and Perona, J. J. (2002) Nat. Struct. Biol. 9, 42-47). Kinetic data presented here indicate that the mutation of glutamine 138 to phenylalanine (Q138F) results in a change in sequence specificity at the center two base pairs of the cognate recognition site. We show that the preference of HincII for cutting, but not binding, the three cognate sites differing in the center two base pairs has been altered by the mutation Q138F. Five new crystal structures are presented including Q138F HincII bound to GTTAAC and GTCGAC both with and without Ca2+ as well as the structure of wild type HincII bound to GTTAAC. The Q138F HincII/DNA structures show conformational changes in the protein, bound DNA, and at the protein-DNA interface, consistent with the formation of adaptive complexes. Analysis of these structures and the effect of Ca2+ binding on the protein-DNA interface illuminates the origin of the altered specificity by the mutation Q138F in the HincII enzyme.