Geometric analysis and comparison of protein-DNA interfaces: why is there no simple code for recognition?

Structural studies of protein-DNA complexes have shown that there are many distinct families of DNA-binding proteins, and have shown that there is no simple "code" describing side-chain/base interactions. However, systematic analysis and comparison of protein-DNA complexes has been complicated by the diversity of observed contacts, the sheer number of complexes currently available and the absence of any consistent method of comparison that retains detailed structural information about the protein-DNA interface. To address these problems, we have developed geometric methods for characterizing the local structural environment in which particular side-chain/base interactions are observed. In particular, we develop methods for analyzing and comparing spatial relationships at the protein-DNA interface. Our method involves attaching local coordinate systems to the DNA bases and to the C(alpha) atoms of the peptide backbone (these are relatively rigid structural units). We use these tools to consider how the position and orientation of the polypeptide backbone (with respect to the DNA) helps to determine what contacts are possible at any given position in a protein-DNA complex. Here, we focus on base contacts that are made in the major groove, and we use spatial relationships in analyzing: (i) the observed patterns of side-chain/base interactions; (ii) observed helix docking orientations; (iii) family/subfamily relationships among DNA-binding proteins; and (iv) broader questions about evolution, altered specificity mutants and the limits for the design of new DNA-binding proteins. Our analysis, which highlights differences in spatial relationships in different complexes and at different positions in a complex, helps explain why there is no simple, general code for protein-DNA recognition.     

Statistical analysis of 15 dimensions in the crystallization space for protein-DNA complexes

Solving the three dimensional structure of a protein-DNA complex is a prerequisite to understand, at the atomic level, the interactions between DNA-binding proteins and their target DNA sequences. Arranging these complexes into an ordered and repetitive network (a crystal, suitable for X-Ray analysis) is a time-limiting empirical step. Although it has been suggested that the crystallization space for protein-DNA complexes is probably smaller than that of non-complexed proteins, a study presenting a detailed and updated analysis of this space is still missing. Here, we analyze the successful crystallization conditions of several hundred protein-DNA complexes and present a bias-free statistical analysis of 15 crystallization parameters that include concentration, temperature, pH, precipitants, salts, divalent cations and polyamines. Our analysis shows that some crystallization parameters are interestingly restricted into narrow intervals. These restrictions could be very helpful in the design of sparse-matrix crystallization screens that target exclusively protein-DNA complexes.     

Intermolecular and intramolecular readout mechanisms in protein-DNA recognition

Protein-DNA recognition plays an essential role in the regulation of gene expression. Regulatory proteins are known to recognize specific DNA sequences directly through atomic contacts (intermolecular readout) and/or indirectly through the conformational properties of the DNA (intramolecular readout). However, little is known about the respective contributions made by these so-called direct and indirect readout mechanisms. We addressed this question by making use of information extracted from a structural database containing many protein-DNA complexes. We quantified the specificity of intermolecular (direct) readout by statistical analysis of base-amino acid interactions within protein-DNA complexes. The specificity of the intramolecular (indirect) readout due to DNA was quantified by statistical analysis of the sequence-dependent DNA conformation. Systematic comparison of these specificities in a large number of protein-DNA complexes revealed that both intermolecular and intramolecular readouts contribute to the specificity of protein-DNA recognition, and that their relative contributions vary depending upon the protein-DNA complexes. We demonstrated that combination of the intermolecular and intramolecular energies derived from the statistical analyses lead to enhanced specificity, and that the combined energy could explain experimental data on binding affinity changes caused by base mutations. These results provided new insight into the relationship between specificity and structure in the process of protein-DNA recognition, which would lead to prediction of specific protein-DNA binding sites.     

Atomistic modeling of protein-DNA interaction specificity: progress and applications

An accurate, predictive understanding of protein-DNA binding specificity is crucial for the successful design and engineering of novel protein-DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein-DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL-DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein-DNA binding prediction and engineering is a goal within reach.     

Structure-based prediction of DNA target sites by regulatory proteins

Regulatory proteins play a critical role in controlling complex spatial and temporal patterns of gene expression in higher organism, by recognizing multiple DNA sequences and regulating multiple target genes. Increasing amounts of structural data on the protein-DNA complex provides clues for the mechanism of target recognition by regulatory proteins. The analyses of the propensities of base-amino acid interactions observed in those structural data show that there is no one-to-one correspondence in the interaction, but clear preferences exist. On the other hand, the analysis of spatial distribution of amino acids around bases shows that even those amino acids with strong base preference such as Arg with G are distributed in a wide space around bases. Thus, amino acids with many different geometries can form a similar type of interaction with bases. The redundancy and structural flexibility in the interaction suggest that there are no simple rules in the sequence recognition, and its prediction is not straightforward. However, the spatial distributions of amino acids around bases indicate a possibility that the structural data can be used to derive empirical interaction potentials between amino acids and bases. Such information extracted from structural databases has been successfully used to predict amino acid sequences that fold into particular protein structures. We surmised that the structures of protein-DNA complexes could be used to predict DNA target sites for regulatory proteins, because determining DNA sequences that bind to a particular protein structure should be similar to finding amino acid sequences that fold into a particular structure. Here we demonstrate that the structural data can be used to predict DNA target sequences for regulatory proteins. Pairwise potentials that determine the interaction between bases and amino acids were empirically derived from the structural data. These potentials were then used to examine the compatibility between DNA sequences and the protein-DNA complex structure in a combinatorial "threading" procedure. We applied this strategy to the structures of protein-DNA complexes to predict DNA binding sites recognized by regulatory proteins. To test the applicability of this method in target-site prediction, we examined the effects of cognate and noncognate binding, cooperative binding, and DNA deformation on the binding specificity, and predicted binding sites in real promoters and compared with experimental data. These results show that target binding sites for several regulatory proteins are successfully predicted, and our data suggest that this method can serve as a powerful tool for predicting multiple target sites and target genes for regulatory proteins.     

Nonsequence-specific DNA recognition: a structural perspective

Chromosomal proteins that form essential architectural components of chromatin bind and bend DNA with an intrinsic low degree of sequence preference. Comparisons made between two recently determined structures of high mobility group (HMG) protein-DNA complexes and other nonsequence-specific protein-DNA complexes reveal the structural basis of this important mode of DNA binding.     

Structure-based prediction of DNA-binding sites on proteins using the empirical preference of electrostatic potential and the shape of molecular surfaces

Protein-DNA interactions play an essential role in the genetic activities of life. Many structures of protein-DNA complexes are already known, but the common rules on how and where proteins bind to DNA have not emerged. Many attempts have been made to predict protein-DNA interactions using structural information, but the success rate is still about 80%. We analyzed 63 protein-DNA complexes by focusing our attention on the shape of the molecular surface of the protein and DNA, along with the electrostatic potential on the surface, and constructed a new statistical evaluation function to make predictions of DNA interaction sites on protein molecular surfaces. The shape of the molecular surface was described by a combination of local and global average curvature, which are intended to describe the small convex and concave and the large-scale concave curvatures of the protein surface preferentially appearing at DNA-binding sites. Using these structural features, along with the electrostatic potential obtained by solving the Poisson-Boltzmann equation numerically, we have developed prediction schemes with 86% and 96% accuracy for DNA-binding and non-DNA-binding proteins, respectively.     

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 beta-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.     

Protein-DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins

Amino acid residues, which play important roles in protein function, are often conserved. Here, we analyze thermodynamic and structural data of protein-DNA interactions to explore a relationship between free energy, sequence conservation and structural cooperativity. We observe that the most stabilizing residues or putative hotspots are those which occur as clusters of conserved residues. The higher packing density of the clusters and available experimental thermodynamic data of mutations suggest cooperativity between conserved residues in the clusters. Conserved singlets contribute to the stability of protein-DNA complexes to a lesser extent. We also analyze structural features of conserved residues and their clusters and examine their role in identifying DNA-binding sites. We show that about half of the observed conserved residue clusters are in the interface with the DNA, which could be identified from their amino acid composition; whereas the remaining clusters are at the protein-protein or protein-ligand interface, or embedded in the structural scaffolds. In protein-protein interfaces, conserved residues are highly correlated with experimental residue hotspots, contributing dominantly and often cooperatively to the stability of protein-protein complexes. Overall, the conservation patterns of the stabilizing residues in DNA-binding proteins also highlight the significance of clustering as compared to single residue conservation.     

Influence of DNA stiffness in protein-DNA recognition

Protein-DNA recognition plays an essential role in the regulation of gene expression. The protein-DNA binding specificity is based on direct atomic contacts between protein and DNA and/or the conformational properties of DNA. In this work, we have analyzed the influence of DNA stiffness (E) to the specificity of protein-DNA complexes. The average DNA stiffness parameters for several protein-DNA complexes have been computed using the structure based sequence dependent stiffness scale. The relationship between DNA stiffness and experimental protein-DNA binding specificity has been brought out. We have investigated the importance of DNA stiffness with the aid of experimental free energy changes (DeltaDeltaG) due to binding in several protein-DNA complexes, such as, ETS proteins, 434, lambda, Mnt and trp repressors, 434 cro protein, EcoRV endonuclease V and zinc fingers. We found a correlation in the range 0.65-0.97 between DeltaDeltaG and E in these examples. Further, we have qualitatively analyzed the effect of mutations in the target sequence of lambda repressor and we observed that the DNA stiffness could correctly identify 70% of the correct bases among the considered nine positions.     

Tn5 transposase active site mutants

Tn5 transposase (Tnp) is a 53.3-kDa protein that is encoded by and facilitates movement of transposon Tn5. Tnp monomers contain a single active site that is responsible for catalyzing a series of four DNA breaking/joining reactions at one transposon end. Based on primary sequence homology and protein structural information, we designed and constructed a series of plasmids that encode for Tnps containing active site mutations. Following Tnp expression and purification, the active site mutants were tested for their ability to form protein-DNA complexes and perform each of the four catalytic steps in the transposition pathway in vitro. The results demonstrate that Asp-97, Asp-188, and Glu-326, visible in the active site of Tn5 crystal structures, are absolutely required for all catalytic steps. Mutations within a series of amino acid residues that are conserved in the IS4 family of transposases and retroviral integrases also impair Tnp catalytic activity. Mutations at either Tyr-319 or Arg-322 reduce both hairpin resolution and strand transfer activity within protein-DNA complexes. Mutations at Lys-333 reduce the ability of Tnps to form protein-DNA complexes, whereas mutations at the less strongly conserved Lys-330 have less of an effect on both synaptic complex formation and catalytic activity.     

Determination of the three-dimensional structure of the Mrf2-DNA complex using paramagnetic spin labeling

Understanding the mechanism of protein-DNA interactions at the molecular level is one of the main focuses in structural and molecular biological investigations. At present, NMR spectroscopy is the only approach that can provide atomic details of protein-DNA recognition in solution. However, determining the structures of protein-DNA complexes using NMR spectroscopy has been dependent on the observation of intermolecular nuclear Overhauser effects (NOE) and their assignments, which are difficult to obtain in many cases. In this study, we have shown that intermolecular distance constraints derived from a single spin-label in combination with docking calculations have defined many specific contacts of the complex between the AT-rich interaction domain (ARID) of Mrf2 and its target DNA. Mrf2 contacts DNA mainly using the two flexible loops, L1 and L2. While the L1 loop contacts the phosphate backbone, L2 and several residues in the adjacent helices interact with AT base pairs in the major groove of DNA. Despite the structural diversity in the ARID family of DNA-binding proteins, Mrf2 maintains contacts with DNA similar to those observed in the homologous Dri-DNA complex.     

Insights into the thermal stabilization and conformational transitions of DNA by hyperthermophile protein Sso7d: molecular dynamics simulations and MM-PBSA analysis

In the assembly of DNA-protein complex, the DNA kinking plays an important role in nucleoprotein structures and gene regulation. Molecular dynamics (MD) simulations were performed on specific protein-DNA complexes in this study to investigate the stability and structural transitions of DNA depending on temperature. Furthermore, we introduced the molecular mechanics/Poisson–Boltzmann surface area (MM-PBSA) approach to analyze the interactions between DNA and protein in hyperthermophile. Focused on two specific Sso7d-DNA complexes (PDB codes: 1BNZ and 1BF4), we performed MD simulations at four temperatures (300, 360, 420, and 480?K) and MM-PBSA at 300 and 360?K to illustrate detailed information on the changes of DNA. Our results show that Sso7d stabilizes DNA duplex over a certain temperature range and DNA molecules undergo B-like to A-like form transitions in the binary complex with the temperature increasing, which are consistent with the experimental data. Our work will contribute to a better understanding of protein-DNA interaction.     

Insights into the thermal stabilization and conformational transitions of DNA by hyperthermophile protein Sso7d: molecular dynamics simulations and MM-PBSA analysis

In the assembly of DNA-protein complex, the DNA kinking plays an important role in nucleoprotein structures and gene regulation. Molecular dynamics (MD) simulations were performed on specific protein-DNA complexes in this study to investigate the stability and structural transitions of DNA depending on temperature. Furthermore, we introduced the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) approach to analyze the interactions between DNA and protein in hyperthermophile. Focused on two specific Sso7d-DNA complexes (PDB codes: 1BNZ and 1BF4), we performed MD simulations at four temperatures (300, 360, 420, and 480?K) and MM-PBSA at 300 and 360?K to illustrate detailed information on the changes of DNA. Our results show that Sso7d stabilizes DNA duplex over a certain temperature range and DNA molecules undergo B-like to A-like form transitions in the binary complex with the temperature increasing, which are consistent with the experimental data. Our work will contribute to a better understanding of protein-DNA interaction.