Structures Of protein-DNA Complexes Research Articles

Local dynamics of proteins and DNA evaluated from crystallographic B-factors

Temperature displacement factors also known as B-factors provide information about local dynamics of atoms. Because dynamic behavior of biomolecules is as important as their static molecular structures we decided to analyze B-factors in almost a thousand non-redundant crystal structures of protein-DNA complexes from a well-curated dataset described in Schneider et al. Nucleic Acids Research, 42 (2014). Biopolymer residues, amino acids and nucleotides, were classified according to their molecular neighborhood as solvent-accessible, solvent-inaccessible (buried, i. e. forming the protein core), or lying at protein-protein or protein-DNA interfaces. In addition, water molecules were labeled as solely bound to the biopolymer surface as the first hydration shell or as water bridges binding protein and DNA molecules. Distributions of scaled B-factors for these types of residues confirmed several expected features of protein and DNA dynamics but they also revealed some surprising facts. Solvent-accessible amino acids have B-factors larger than residues at both biomolecular interfaces and amino acids forming protein core are restricted in their movement the most. A really unique feature of the buried amino acids is that their side chains are restricted in their movements more than the main chains. Protein interior is therefore packed significantly better than protein-protein or protein-DNA interfaces. Low values of B-factors of the waters bridging protein and DNA molecules contrast with extremely high values of DNA phosphates. Characteristic features distinguishing different types of residues quickly vanish in structures with lower resolution and some of the observed trends are a likely consequence of improper refinement protocols that may need rectifying. Acknowledgments. This study was supported by BIOCEV CZ.1.05/1.1.00/02.0109 from the ERDF and by grant P305/12/1801 from the Czech Science Foundation.

Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.

OnTheFly: a database ofDrosophila melanogastertranscription factors and their binding sites

We present OnTheFly (http://bhapp.c2b2.columbia.edu/OnTheFly/index.php), a database comprising a systematic collection of transcription factors (TFs) of Drosophila melanogaster and their DNA-binding sites. TFs predicted in the Drosophila melanogaster genome are annotated and classified and their structures, obtained via experiment or homology models, are provided. All known preferred TF DNA-binding sites obtained from the B1H, DNase I and SELEX methodologies are presented. DNA shape parameters predicted for these sites are obtained from a high throughput server or from crystal structures of protein–DNA complexes where available. An important feature of the database is that all DNA-binding domains and their binding sites are fully annotated in a eukaryote using structural criteria and evolutionary homology. OnTheFly thus provides a comprehensive view of TFs and their binding sites that will be a valuable resource for deciphering non-coding regulatory DNA.

PiDNA: predicting protein–DNA interactions with structural models

Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein–DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein–DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein–DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10 000 random sites with high accuracy. With PiDNA, the users can design biological experiments based on the predicted sequence specificity and/or request mutated structure models for further protein design. As well, it is expected that PiDNA can be incorporated with chromatin immunoprecipitation data to refine large-scale inference of in vivo protein–DNA interactions. PiDNA is available at: http://dna.bime.ntu.edu.tw/pidna.

A simple but effective modeling strategy for structural properties of non‐heme Fe(II) sites in proteins: Test of force field models and application to proteins in the AlkB family

To facilitate computational study of proteins in the AlkB family and related α-ketoglutarate/Fe(II)-dependent dioxygenases, we have tested a simple modeling strategy for the non-heme Fe(II) site in which the iron is represented by a simple +2 point charge with Lennard-Jones parameters. Calculations for an AlkB active site model in the gas phase and ∼150 ns molecular dynamics (MD) simulations for two enzyme-dsDNA complexes (E. coli AlkB-dsDNA and ABH2-dsDNA) suggest that this simple modeling strategy provides a satisfactory description of structural properties of the Fe(II) site in AlkB enzymes, provided that care is exercised to control the binding mode of carboxylate (Asp) to the iron. MD simulations using the model for AlkB-dsDNA and ABH2-dsDNA systems find that although the structural features for the latter are overall in good agreement with the crystal structure, the dsDNA, and AlkB-dsDNA interface undergo substantial changes during the MD simulations from the crystal structure. Even for ABH2, new interactions form between a long loop region and dsDNA upon structural relaxation of the loop, supporting the role of this loop in DNA binding despite the lack of interactions between them in the crystal structure. Analysis of DNA backbone torsional distributions helps identify regions that adopt strained conformations. Collectively, the results highlight that crystal packing may have a significant impact on the structure of protein-DNA complexes; the simulations also provide additional insights regarding why AlkB and ABH2 prefer single-strand and double-strand DNA, respectively, as substrate.

Aromatic-aromatic interactions in structures of proteins and protein-DNA complexes: a study based on orientation and distance.

Interactions between the aromatic amino acid residues have a significant influence on the protein structures and protein-DNA complexes. These interactions individually provide little stability to the structure; however, together they contribute significantly to the conformational stability of the protein structure. In this study, we focus on the four aromatic amino acid residues and their interactions with one another and their individual interactions with the four nucleotide bases. These are analyzed in order to determine the extent to which their orientation and the number of interactions contribute to the protein and protein-DNA complex structures.

Three enhancements to the inference of statistical protein-DNA potentials

The energetics of protein-DNA interactions are often modeled using so-called statistical potentials, that is, energy models derived from the atomic structures of protein-DNA complexes. Many statistical protein-DNA potentials based on differing theoretical assumptions have been investigated, but little attention has been paid to the types of data and the parameter estimation process used in deriving the statistical potentials. We describe three enhancements to statistical potential inference that significantly improve the accuracy of predicted protein-DNA interactions: (i) incorporation of binding energy data of protein-DNA complexes, in conjunction with their X-ray crystal structures, (ii) use of spatially-aware parameter fitting, and (iii) use of ensemble-based parameter fitting. We apply these enhancements to three widely-used statistical potentials and use the resulting enhanced potentials in a structure-based prediction of the DNA binding sites of proteins. These enhancements are directly applicable to all statistical potentials used in protein-DNA modeling, and we show that they can improve the accuracy of predicted DNA binding sites by up to 21%.

DR_bind: a web server for predicting DNA-binding residues from the protein structure based on electrostatics, evolution and geometry

DR_bind is a web server that automatically predicts DNA-binding residues, given the respective protein structure based on (i) electrostatics, (ii) evolution and (iii) geometry. In contrast to machine-learning methods, DR_bind does not require a training data set or any parameters. It predicts DNA-binding residues by detecting a cluster of conserved, solvent-accessible residues that are electrostatically stabilized upon mutation to Asp−/Glu−. The server requires as input the DNA-binding protein structure in PDB format and outputs a downloadable text file of the predicted DNA-binding residues, a 3D visualization of the predicted residues highlighted in the given protein structure, and a downloadable PyMol script for visualization of the results. Calibration on 83 and 55 non-redundant DNA-bound and DNA-free protein structures yielded a DNA-binding residue prediction accuracy/precision of 90/47% and 88/42%, respectively. Since DR_bind does not require any training using protein–DNA complex structures, it may predict DNA-binding residues in novel structures of DNA-binding proteins resulting from structural genomics projects with no conservation data. The DR_bind server is freely available with no login requirement at http://dnasite.limlab.ibms.sinica.edu.tw.

Investigating the Structural Impacts of I64T and P311S Mutations in APE1-DNA Complex: A Molecular Dynamics Approach

BackgroundElucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.Principal FindingsIn this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.SignificanceDifferent parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis.

Structure Prediction of an Unusual Protein-DNA Complex Hu with a DNA Four-Way Junction

The prediction of the structure of proteins, DNA, and protein-DNA complexes has long been one of the major challenges to the field of computational biophysics. The Holliday Junction (4WJ) is a four-way junction of DNA strand exchange that is involved in homologous recombination in a wide range of living organisms. Based on experimental data, the junction is observed to be capable of adopting two known forms in particular: an open planar form that accounts for branch migration, and a stationary stacked-X form stabilized by physiological salt conditions. The prokaryotic protein HU (heat unstable) is known to bind damaged, mismatched, or overhanging duplex DNA with high affinity, as well as 4WJs in the stacked-X conformation. FRET data reveal a 2:1 HU:4WJ binding stoichiometry for the complex, but the exact structure and orientation of the HU proteins is unknown. A second series of FRET studies provide some distance information that has been used to guide the construction of possible complexes using the graphics program Pymol. The plausible structures have been evaluated for energetic and dynamic stability using Molecular Dynamics (MD) simulations done in explicit solvent. Both modeled DNA and DNA crystal structure coordinates were utilized in the calculations. The MD simulations were carried out using the AMBER suite of programs on the Wesleyan PC cluster.

Sequence-Dependent Mechanics of DNA

Double-stranded DNA is a semiflexible polymer that can naturally bend on length scales comparable to the size of large DNA-protein complexes like nucleosomes or protein-mediated DNA loops. The sequence of the substrate DNA does not only provide biochemical binding sites for the proteins, but also affects the local mechanical properties of the DNA. Notably, sequence can affect the intrinsic curvature of the DNA, as well as its bendability, or elasticity. While intrinsic bends in DNA and their role in protein-DNA complex formation are well studied, sequence-dependent elasticity still remains only vaguely explored. In order to separate sequence effects on elasticity from those on intrinsic curvature, we have designed sequences of DNA which have nearly identical curvatures but varied AT content and directly measured their mechanical elasticity using constant force axial optical tweezers. We found the persistence length to be highly dependent on the AT content of the DNA, differing almost thirty percent between sequences with nearly identical curvature but different sequence composition. This is a departure from conventional dinucleotide and trinucleotide models, which predict a much smaller difference between the two sequences, but consistent with estimates obtained from the crystallographic structures of protein-DNA complexes.

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Systematic Classification and Analysis of Themes in Protein−DNA Recognition

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

An effective approach for generating a three-Cys2His2 zinc-finger-DNA complex model by docking

BackgroundDetermination of protein-DNA complex structures with both NMR and X-ray crystallography remains challenging in many cases. High Ambiguity-Driven DOCKing (HADDOCK) is an information-driven docking program that has been used to successfully model many protein-DNA complexes. However, a protein-DNA complex model whereby the protein wraps around DNA has not been reported. Defining the ambiguous interaction restraints for the classical three-Cys2His2 zinc-finger proteins that wrap around DNA is critical because of the complicated binding geometry. In this study, we generated a Zif268-DNA complex model using three different sets of ambiguous interaction restraints (AIRs) to study the effect of the geometric distribution on the docking and used this approach to generate a newly reported Sp1-DNA complex model.ResultsThe complex models we generated on the basis of two AIRs with a good geometric distribution in each domain are reasonable in terms of the number of models with wrap-around conformation, interface root mean square deviation, AIR energy and fraction native contacts. We derived the modeling approach for generating a three-Cys2His2 zinc-finger-DNA complex model according to the results of docking studies using the Zif268-DNA and other three crystal complex structures. Furthermore, the Sp1-DNA complex model was calculated with this approach, and the interactions between Sp1 and DNA are in good agreement with those previously reported.ConclusionsOur docking data demonstrate that two AIRs with a reasonable geometric distribution in each of the three-Cys2His2 zinc-finger domains are sufficient to generate an accurate complex model with protein wrapping around DNA. This approach is efficient for generating a zinc-finger protein-DNA complex model for unknown complex structures in which the protein wraps around DNA. We provide a flowchart showing the detailed procedures of this approach.

Origins of Specificity in Protein-DNA Recognition

Specific interactions between proteins and DNA are fundamental to many biological processes. In this review, we provide a revised view of protein-DNA interactions that emphasizes the importance of the three-dimensional structures of both macromolecules. We divide protein-DNA interactions into two categories: those when the protein recognizes the unique chemical signatures of the DNA bases (base readout) and those when the protein recognizes a sequence-dependent DNA shape (shape readout). We further divide base readout into those interactions that occur in the major groove from those that occur in the minor groove. Analogously, the readout of the DNA shape is subdivided into global shape recognition (for example, when the DNA helix exhibits an overall bend) and local shape recognition (for example, when a base pair step is kinked or a region of the minor groove is narrow). Based on the >1500 structures of protein-DNA complexes now available in the Protein Data Bank, we argue that individual DNA-binding proteins combine multiple readout mechanisms to achieve DNA-binding specificity. Specificity that distinguishes between families frequently involves base readout in the major groove, whereas shape readout is often exploited for higher resolution specificity, to distinguish between members within the same DNA-binding protein family.

Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies

BackgroundKnowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.ResultsThe position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.ConclusionsA novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.

Tangle analysis of difference topology experiments: Applications to a Mu protein-DNA complex

We develop topological methods for analyzing difference topology experiments involving 3-string tangles. Difference topology is a novel technique used to unveil the structure of stable protein-DNA complexes involving two or more DNA segments. We analyze such experiments for the Mu protein-DNA complex. We characterize the solutions to the corresponding tangle equations by certain knotted graphs. By investigating planarity conditions on these graphs we show that there is a unique biologically relevant solution. That is, we show there is a unique rational tangle solution, which is also the unique solution with small crossing number.

The role of DNA shape in protein–DNA recognition

The recognition of specific DNA sequences by proteins is thought to depend on two types of mechanisms: one that involves the formation of hydrogen bonds with specific bases, primarily in the major groove, and one involving sequence-dependent deformations of the DNA helix. By comprehensively analyzing the three dimensional structures of protein-DNA complexes, we show that the binding of arginines to narrow minor grooves is a widely used mode for protein-DNA recognition. This readout mechanism exploits the phenomenon that narrow minor grooves strongly enhance the negative electrostatic potential of the DNA. The nucleosome core particle offers a striking example of this effect. Minor groove narrowing is often associated with the presence of A-tracts, AT-rich sequences that exclude the flexible TpA step. These findings suggest that the ability to detect local variations in DNA shape and electrostatic potential is a general mechanism that enables proteins to use information in the minor groove, which otherwise offers few opportunities for the formation of base-specific hydrogen bonds, to achieve DNA binding specificity.

PDA: an automatic and comprehensive analysis program for protein-DNA complex structures

BackgroundKnowledge of protein-DNA interactions at the structural-level can provide insights into the mechanisms of protein-DNA recognition and gene regulation. Although over 1400 protein-DNA complex structures have been deposited into Protein Data Bank (PDB), the structural details of protein-DNA interactions are generally not available. In addition, current approaches to comparison of protein-DNA complexes are mainly based on protein sequence similarity while the DNA sequences are not taken into account. With the number of experimentally-determined protein-DNA complex structures increasing, there is a need for an automatic program to analyze the protein-DNA complex structures and to provide comprehensive structural information for the benefit of the whole research community.ResultsWe developed an automatic and comprehensive protein-DNA complex structure analysis program, PDA (for protein-DNA complex structure analyzer). PDA takes PDB files as inputs and performs structural analysis that includes 1) whole protein-DNA complex structure restoration, especially the reconstruction of double-stranded DNA structures; 2) an efficient new approach for DNA base-pair detection; 3) systematic annotation of protein-DNA interactions; and 4) extraction of DNA subsequences involved in protein-DNA interactions and identification of protein-DNA binding units. Protein-DNA complex structures in current PDB were processed and analyzed with our PDA program and the analysis results were stored in a database. A dataset useful for studying protein-DNA interactions involved in gene regulation was generated using both protein and DNA sequences as well as the contact information of the complexes. WebPDA was developed to provide a web interface for using PDA and for data retrieval.ConclusionPDA is a computational tool for structural annotations of protein-DNA complexes. It provides a useful resource for investigating protein-DNA interactions. Data from the PDA analysis can also facilitate the classification of protein-DNA complexes and provide insights into rational design of benchmarks. The PDA program is freely available at .

Prediction of DNA-binding residues from protein sequence information using random forests

BackgroundProtein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.ResultsA new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.ConclusionThe results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF has thus been developed to make the RF classifier accessible to the biological research community.