Protein-nucleic acid recognition: statistical analysis of atomic interactions and influence of DNA structure

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43,000 RNA-mediated interactions, and ∼12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.

Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces

Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.

Aromatic residues in RNase T stack with nucleobases to guide the sequence-specific recognition and cleavage of nucleic acids

RNase T is a classical member of the DEDDh family of exonucleases with a unique sequence preference in that its 3'-to-5' exonuclease activity is blocked by a 3'-terminal dinucleotide CC in digesting both single-stranded RNA and DNA. Our previous crystal structure analysis of RNase T-DNA complexes show that four phenylalanine residues, F29, F77, F124, and F146, stack with the two 3'-terminal nucleobases. To elucidate if the π-π stacking interactions between aromatic residues and nucleobases play a critical role in sequence-specific protein-nucleic acid recognition, here we mutated two to four of the phenylalanine residues in RNase T to tryptophan (W mutants) and tyrosine (Y mutants). The Escherichia coli strains expressing either the W mutants or the Y mutants had slow growth phenotypes, suggesting that all of these mutants could not fully substitute the function of the wild-type RNase T in vivo. DNA digestion assays revealed W mutants shared similar sequence specificity with wild-type RNase T. However, the Y mutants exhibited altered sequence-dependent activity, digesting ssDNA with both 3'-end CC and GG sequences. Moreover, the W and Y mutants had reduced DNA-binding activity and lower thermal stability as compared to wild-type RNase T. Taken together, our results suggest that the four phenylalanine residues in RNase T not only play critical roles in sequence-specific recognition, but also in overall protein stability. Our results provide the first evidence showing that the π-π stacking interactions between nucleobases and protein aromatic residues may guide the sequence-specific activity for DNA and RNA enzymes.

CarD uses a minor groove wedge mechanism to stabilize the RNA polymerase open promoter complex

A key point to regulate gene expression is at transcription initiation, and activators play a major role. CarD, an essential activator in Mycobacterium tuberculosis, is found in many bacteria, including Thermus species, but absent in Escherichia coli. To delineate the molecular mechanism of CarD, we determined crystal structures of Thermus transcription initiation complexes containing CarD. The structures show CarD interacts with the unique DNA topology presented by the upstream double-stranded/single-stranded DNA junction of the transcription bubble. We confirm that our structures correspond to functional activation complexes, and extend our understanding of the role of a conserved CarD Trp residue that serves as a minor groove wedge, preventing collapse of the transcription bubble to stabilize the transcription initiation complex. Unlike E. coli RNAP, many bacterial RNAPs form unstable promoter complexes, explaining the need for CarD.

DOI:http://dx.doi.org/10.7554/eLife.08505.001

Inside cells, molecules of double-stranded DNA encode the instructions needed to make proteins. To make a protein, the two strands of DNA that make up a gene are separated and one strand acts as a template to make molecules of messenger ribonucleic acid (or mRNA for short). This process is called transcription. The mRNA is then used as a template to assemble the protein. An enzyme called RNA polymerase carries out transcription and is found in all cells ranging from bacteria to humans and other animals.

Bacteria have the simplest form of RNA polymerase and provide an excellent system to study how it controls transcription. It is made up of several proteins that work together to make RNA using DNA as a template. However, it requires the help of another protein called sigma factor to direct it to regions of DNA called promoters, which are just before the start of the gene. When RNA polymerase and the sigma factor interact the resulting group of proteins is known as the RNA polymerase ‘holoenzyme’.

Transcription takes place in several stages. To start with, the RNA polymerase holoenzyme locates and binds to promoter DNA. Next, it separates the two strands of DNA and exposes a portion of the template strand. At this point, the DNA and the holoenzyme are said to be in an ‘open promoter complex’ and the section of promoter DNA that is within it is known as a ‘transcription bubble’. Another protein called CarD helps to speed up transcription but it is not clear how this stage of the process works.

Bae et al. have now used X-ray crystallography to reveal the structure of CarD bound to the RNA polymerase holoenyzme and a DNA promoter. The structures show that one part of CarD interacts with the DNA at the start of the transcription bubble, and another part binds to the RNA polymerase. CarD fits between the two strands of DNA in the promoter, like a wedge, to keep the strands apart. Therefore, CarD stabilizes the open promoter complex and prevents the transcription bubble from collapsing.

These findings reveal a previously unseen mechanism involved in activating transcription and will guide further experiments probing the role of CarD in living cells. Another study by Bae, Feklistov et al.—which involves some of the same researchers as this study—reveals that the sigma factor also binds to DNA at the start of the transcription bubble. The general principles outlined by these studies may help to identify other proteins that regulate transcription.

DOI:http://dx.doi.org/10.7554/eLife.08505.002

RNA-binding residues prediction using structural features

Background

RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, it’s important to identify RNA-binding residues in proteins.

Results

In this work, we propose a set of new structural features for RNA-binding residue prediction. A set of template patches are first extracted from RNA-binding interfaces. To construct structural features for a residue, we compare its surrounding patches with each template patch and use the accumulated distances as its structural features. These new features provide sufficient structural information of surrounding surface of a residue and they can be used to measure the structural similarity between the surface surrounding two residues. The new structural features, together with other sequence features, are used to predict RNA-binding residues using ensemble learning technique.

Conclusions

The experimental results reveal the effectiveness of the proposed structural features. In addition, the clustering results on template patches exhibit distinct structural patterns of RNA-binding sites, although the sequences of template patches in the same cluster are not conserved. We speculate that RNAs may have structure preferences when binding with proteins.

Dissection and prediction of RNA-binding sites on proteins

RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.

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.

In vitro identification of nonalcoholic fatty liver disease-related protein hnRNPM

AIM: To study the formation of intracellular glyceraldehyde-derived advanced glycation end products (Glycer-AGEs) in the presence of high concentrations of fructose.

METHODS: Cells of the human hepatocyte cell line Hep3B were incubated with or without fructose for five days, and the corresponding cell lysates were separated by two-dimensional gradient sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Glycer-AGEs were detected with the anti-Glycer-AGEs antibody. Furthermore, the identification of the proteins that are modified by glyceraldehyde in the presence of high concentrations of fructose was conducted using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The protein and mRNA levels were determined by Western blotting and real-time reverse transcription PCR, respectively.

RESULTS: The results of the two-dimensional gradient sodium dodecyl sulfate-polyacrylamide gel electrophoresis indicated a greater amount of Glycer-AGEs in the sample exposed to high concentrations of fructose than in the control. The detected Glycer-AGEs showed isoelectric points in the range of 8.0-9.0 and molecular weights in the range of 60-80 kDa. The heterogeneous nuclear ribonucleoprotein M (hnRNPM), which plays an important role in regulating gene expression by processing heterogeneous nuclear RNAs to form mature mRNAs, was identified as a modified protein using MALDI-TOF-MS. Increasing the concentration of fructose in the medium induced a concentration-dependent increase in the generated Glycer-AGEs. Furthermore, in an experiment using glyceraldehyde, which is a precursor of Glycer-AGEs, hnRNPM was found to be more easily glycated than the other proteins.

CONCLUSION: The results suggest that glyceraldehyde-modified hnRNPM alters gene expression. This change may cause adverse effects in hepatocytes and may serve as a target for therapeutic intervention.

Serine and Cysteine π-Interactions in Nature: A Comparison of the Frequency, Structure, and Stability of Contacts Involving Oxygen and Sulfur

Despite many DNA–protein π-interactions in high-resolution crystal structures, only four X–H···π or X···π interactions were found between serine (Ser) or cysteine (Cys) and DNA nucleobase π-systems in over 100 DNA–protein complexes (where X = O for Ser and X = S for Cys). Nevertheless, 126 non-covalent contacts occur between Ser or Cys and the aromatic amino acids in many binding arrangements within proteins. Furthermore, Ser and Cys protein–protein π-interactions occur with similar frequencies and strengths. Most importantly, due to the great stability that can be provided to biological macromolecules (up to –20 kJ mol–1 for neutral π-systems or –40 kJ mol–1 for cationic π-systems), Ser and Cys π-interactions should be considered when analyzing protein stability and function.

Quantitative analysis of the interaction between l-methionine derivative and oligonucleotides

This study explores the use of l-methionine derivative as a potential affinity ligand for nucleic acids purification. The l-methionine derivative is synthesized by activation of the carboxylic acid group with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide follow by immobilization on amine sensor surface, previously activated and treated with ethylenediamine. Their affinity towards oligonucleotides has been determined by surface plasmon resonance biosensor. The highest affinity is found for cytosine and thymine, followed by adenine, whereas the lowest affinity is found for guanine. For hetero-oligonucleotides the affinity order is CCCTTT > CCCAAA ≈ AAATTT > GGGTTT, showing that nucleotides with cytosine have the highest affinity, and the presence of guanine reduces the affinity, corroborating with the results obtained with homo-oligonucleotides.

DNA-protein π-interactions in nature: abundance, structure, composition and strength of contacts between aromatic amino acids and DNA nucleobases or deoxyribose sugar

Four hundred twenty-eight high-resolution DNA-protein complexes were chosen for a bioinformatics study. Although 164 crystal structures (38% of those searched) contained no interactions, 574 discrete π-contacts between the aromatic amino acids and the DNA nucleobases or deoxyribose were identified using strict criteria, including visual inspection. The abundance and structure of the interactions were determined by unequivocally classifying the contacts as either π-π stacking, π-π T-shaped or sugar-π contacts. Three hundred forty-four nucleobase-amino acid π-π contacts (60% of all interactions identified) were identified in 175 of the crystal structures searched. Unprecedented in the literature, 230 DNA-protein sugar-π contacts (40% of all interactions identified) were identified in 137 crystal structures, which involve C-H···π and/or lone-pair···π interactions, contain any amino acid and can be classified according to sugar atoms involved. Both π-π and sugar-π interactions display a range of relative monomer orientations and therefore interaction energies (up to -50 (-70) kJ mol(-1) for neutral (charged) interactions as determined using quantum chemical calculations). In general, DNA-protein π-interactions are more prevalent than perhaps currently accepted and the role of such interactions in many biological processes may yet to be uncovered.

Aromatic interactions modulate the 5'-base selectivity of the DNA-binding autoantibody ED-10

We present detailed computational analyses of the binding of four dinucleotides to a highly sequence-selective single-stranded DNA (ssDNA) binding antibody (ED-10) and selected point mutants. Anti-DNA antibodies are central to the pathogenesis of systemic lupus erythematosus (SLE), and a more complete understanding of the mode of binding of DNA and other ligands will be necessary to elucidate the role of anti-DNA antibodies in the kidney inflammation associated with SLE. Classical molecular mechanics based molecular dynamics simulations and density functional theory (DFT) computations were applied to pinpoint the origin of selectivity for the 5'-nucleotide. In particular, the strength of interactions between each nucleotide and the surrounding residues were computed using MMGBSA as well as DFT applied to a cluster model of the binding site. The results agree qualitatively with experimental binding free energies, and indicate that π-stacking, CH/π, NH/π, and hydrogen-bonding interactions all contribute to 5'-base selectivity in ED-10. Most importantly, the selectivity for dTdC over dAdC arises primarily from differences in the strength of π-stacking and XH/π interactions with the surrounding aromatic residues; hydrogen bonds play little role. These data suggest that a key Tyr residue, which is not present in other anti-DNA antibodies, plays a key role in the 5'-base selectivity, while we predict that the mutation of a single Trp residue can tune the selectivity for dTdC over dAdC.

Bioinformatic analysis of the protein/DNA interface

To investigate the principles driving recognition between proteins and DNA, we analyzed more than thousand crystal structures of protein/DNA complexes. We classified protein and DNA conformations by structural alphabets, protein blocks [de Brevern, Etchebest and Hazout (2000) (Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Prots. Struct. Funct. Genet., 41:271-287)] and dinucleotide conformers [Svozil, Kalina, Omelka and Schneider (2008) (DNA conformations and their sequence preferences. Nucleic Acids Res., 36:3690-3706)], respectively. Assembling the mutually interacting protein blocks and dinucleotide conformers into 'interaction matrices' revealed their correlations and conformer preferences at the interface relative to their occurrence outside the interface. The analyzed data demonstrated important differences between complexes of various types of proteins such as transcription factors and nucleases, distinct interaction patterns for the DNA minor groove relative to the major groove and phosphate and importance of water-mediated contacts. Water molecules mediate proportionally the largest number of contacts in the minor groove and form the largest proportion of contacts in complexes of transcription factors. The generally known induction of A-DNA forms by complexation was more accurately attributed to A-like and intermediate A/B conformers rare in naked DNA molecules.

Predicting DNA-binding sites of proteins based on sequential and 3D structural information

Protein-DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein-DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein-DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein-DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein-DNA interaction.

Sequence-specific transitions of the torsion angle gamma change the polar-hydrophobic profile of the DNA grooves: implication for indirect protein-DNA recognition

Variations of the shape and polarity of the DNA grooves caused by changes of the DNA conformation play an important role in the DNA readout. Despite the fact that non-canonical trans and gauche- conformations of the DNA backbone angle γ (O5'-C5'-C4'-C3') are frequently found in the DNA crystal structures, their possible role in the DNA recognition has not been studied systematically. In order to fill in this gap, we analyze the available high-resolution crystal structures of the naked and complexed DNA. The analysis shows that the non-canonical γ angle conformations are present both in the naked and bound DNA, more often in the bound vs. naked DNA, and in the nucleotides with the A-like vs. the B-like sugar pucker. The alternative angle γ torsions are more frequently observed in the purines with the A-like sugar pucker and in the pyrimidines with the B-like sugar conformation. The minor groove of the nucleotides with non-canonical γ angle conformation is more polar, while the major groove is more hydrophobic than in the nucleotides with the classical γ torsions due to variations in exposure of the polar and hydrophobic groups of the DNA backbone. The propensity of the nucleotides with different γ angle conformations to participate in the protein-nucleic acid contacts in the minor and major grooves is connected with their sugar pucker and sequence-specific. Our findings imply that the angle γ transitions contribute to the process of the protein-DNA recognition due to modification of the polar/hydrophobic profile of the DNA grooves.

Automatic workflow for the classification of local DNA conformations

Background

A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism.

Results

A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a non-hierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures.

Conclusions

In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations.

Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins

Protein-DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at http://www.biotech.iitm.ac.in/DNA-protein/. Further, we have grouped the methods based on their complexity and analyzed the performance. The information gained in this work could be effectively used to select the best method for designing experiments.

Binding analysis between L-histidine immobilized and oligonucleotides by SPR and NMR

Saturation transfer difference (STD) NMR technique and surface plasmon resonance (SPR) are used to study amino acid affinity supports-nucleotides interactions with L-histidine amino acid immobilized on a surface as model support. We have immobilized L-histidine ligand on a carboxymethyldextran-modified gold surface intended for surface plasmon resonance and we analyze the binding profiles of synthetic polynucleotides (1-6 base, sugar and backbone) by determining the equilibrium dissociation constant (KD). The SPR binding profile (square-shaped) is identical for all the complexes and the highest binding affinity can be found for polyA₆ followed by polyG₆. As expected, the 5'-mononucleotides have the lowest affinity. To further study the structural aspects of the interaction we investigate the polynucleotide binding preferences to L-histidine chromatography support by STD-NMR spectroscopy. These results revealed that an increase in the number of bases and backbone to 6 units leads to more contacts with the support, where the main driving force for the interaction with polynucleotides are through the base, except for polyC₆, which is mainly through sugar-phosphate backbone. Therefore, the combination of SPR measurements with STD-NMR technique allowed to establish fine details of the molecular recognition process involved in amino acid affinity supports-nucleotides complexes.

Local conformational changes in the DNA interfaces of proteins

When a protein binds to DNA, a conformational change is often induced so that the protein will fit into the DNA structure. Therefore, quantitative analyses were conducted to understand the conformational changes in proteins. The results showed that conformational changes in DNA interfaces are more frequent than in non-interfaces, and DNA interfaces have more conformational variations in the DNA-free form. As expected, the former indicates that interaction with DNA has some influence on protein structure. The latter suggests that the intrinsic conformational flexibility of DNA interfaces is important for adjusting their conformation for DNA. The amino acid propensities of the conformationally changed regions in DNA interfaces indicate that hydrophilic residues are preferred over the amino acids that appear in the conformationally unchanged regions. This trend is true for disordered regions, suggesting again that intrinsic flexibility is of importance not only for DNA binding but also for interactions with other molecules. These results demonstrate that fragments destined to be DNA interfaces have an intrinsic flexibility and are composed of amino acids with the capability of binding to DNA. This information suggests that the prediction of DNA binding sites may be improved by the integration of amino acid preference for DNA and one for disordered regions.

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.

On docking, scoring and assessing protein-DNA complexes in a rigid-body framework

We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA.

Identification of novel DNA-binding proteins using DNA-affinity chromatography/pull down

This units presents methods through which one may isolate and identify novel bacterial DNA-binding proteins. Briefly, the DNA sequence of interest is affixed to beads, and then incubated with bacterial cytoplasmic extract. Washes with buffers containing nonspecific DNA and low-salt concentrations will remove non-adhering and low-specificity DNA-binding proteins, while subsequent washes with higher salt concentrations will elute more specific DNA-binding proteins. Eluted proteins may then be identified by standard proteomic techniques.

Arginine methylation of RNA-binding proteins regulates cell function and differentiation

Arginine methylation is a post-translational modification that regulates protein function. RNA-binding proteins are an important class of cell-function mediators, some of which are methylated on arginine. Early studies of RNA-binding proteins and arginine methylation are briefly introduced, and the enzymes that mediate this post-translational modification are described. We review the most common RNA-binding domains and briefly discuss how they associate with RNAs. We address the following groups of RNA-binding proteins: hnRNP, Sm, Piwi, Vasa, FMRP, and HuD. hnRNPs were the first RNA-binding proteins found to be methylated on arginine. The Sm proteins function in RNA processing and germ cell specification. The Piwi proteins are largely germ cell specific and are also required for germ cell production, as is Vasa. FMRP participates in germ cell formation in Drosophila, but is more widely known for its neuronal function. Similarly, HuD plays a role in nervous system development and function. We review the effects of arginine methylation on the function of each protein, then conclude by addressing remaining questions and future directions of arginine methylation as an important and emerging area of regulation.

A new residue-nucleotide propensity potential with structural information considered for discriminating protein-RNA docking decoys

Understanding the key factors that influence the preferences of residue-nucleotide interactions in specific protein-RNA interactions has remained a research focus. We propose an effective approach to derive residue-nucleotide propensity potentials through considering both the types of residues and nucleotides, and secondary structure information of proteins and RNAs from the currently largest nonredundant and nonribosomal protein-RNA interaction database. To test the validity of the potentials, we used them to select near-native structures from protein-RNA docking poses. The results show that considering secondary structure information, especially for RNAs, greatly improves the predictive power of pair potentials. The success rate is raised from 50.7 to 65.5% for the top 2000 structures, and the number of cases in which a near-native structure is ranked in top 50 is increased from 7 to 13 out of 17 cases. Furthermore, the exclusion of ribosomes from the database contributes 8.3% to the success rate. In addition, some very interesting findings follow: (i) the protein secondary structure element π-helix is strongly associated with RNA-binding sites; (ii) the nucleotide uracil occurs frequently in the most preferred pairs in which the unpaired and non-Watson-Crick paired uracils are predominant, which is probably significant in evolution. The new residue-nucleotide potentials can be helpful for the progress of protein-RNA docking methods, and for understanding the mechanisms of protein-RNA interactions.

Structure-Activity Relationship Studies of HIV-1 Integrase Oligonucleotide Inhibitors

Integration of human immunodeficiency virus type 1 DNA into an infected cell genome is one of the key steps of the viral replication cycle. Therefore, viral enzyme integrase, which realizes the integration, represents an attractive and validated target for the development of new antiviral drugs. In this paper, the anti-integrase activity of a series of conjugates of single-stranded oligonucleotides with hydrophobic molecules was tested, and the structure-activity relationships were also analyzed. Both oligonucleotide and hydrophobic parts of the conjugates influenced the inhibitory potency. Conjugates of 11-mer phosphorothioate oligonucleotides with 6-carboxy-4,7,2',4',5',7'-hexachlorofluorescein (HEX) were found to be the most efficient inhibitors (IC50 = 20 nM) and might be considered as lead compounds for further development of integrase inhibitors.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

Glucose autoxidation induces functional damage to proteins via modification of critical arginine residues

Nonenzymatic modification of proteins in hyperglycemia is a major mechanism causing diabetic complications. These modifications can have pathogenic consequences when they target active site residues, thus affecting protein function. In the present study, we examined the role of glucose autoxidation in functional protein damage using lysozyme and RGD-α3NC1 domain of collagen IV as model proteins in vitro. We demonstrated that glucose autoxidation induced inhibition of lysozyme activity as well as NC1 domain binding to α(V)β(3) integrin receptor via modification of critical arginine residues by reactive carbonyl species (RCS) glyoxal (GO) and methylglyoxal while nonoxidative glucose adduction to the protein did not affect protein function. The role of RCS in protein damage was confirmed using pyridoxamine which blocked glucose autoxidation and RCS production, thus protecting protein function, even in the presence of high concentrations of glucose. Glucose autoxidation may cause protein damage in vivo since increased levels of GO-derived modifications of arginine residues were detected within the assembly interface of collagen IV NC1 domains isolated from renal ECM of diabetic rats. Since arginine residues are frequently present within protein active sites, glucose autoxidation may be a common mechanism contributing to ECM protein functional damage in hyperglycemia and oxidative environment. Our data also point out the pitfalls in functional studies, particularly in cell culture experiments, that involve glucose treatment but do not take into account toxic effects of RCS derived from glucose autoxidation.

Screening nucleotide binding to amino acid-coated supports by surface plasmon resonance and nuclear magnetic resonance

Here, we describe a rapid and efficient screening method using surface plasmon resonance (SPR) and saturation transfer difference-nuclear magnetic resonance (STD-NMR) spectroscopy to yield information regarding the residues involved in nucleotide binding to amino acid-coated supports. The aim of this work was to explore the use of these spectroscopic techniques to study amino acid-nucleotide interactions in order to improve the binding specificity of the amino acid ligands used to purify plasmid DNA. For SPR, we present a strategy that immobilizes arginine and lysine on a surface as model supports, and we analyze binding responses when synthetic homo-deoxyoligonucleotides are injected over the amino acid surface. The binding responses are detectable and reproducible despite the small size of the immobilized amino acids. Using STD-NMR, we performed epitope mapping of homo-deoxyoligonucleotides bound to L-arginine-bisoxyran-Sepharose and L-lysine-Sepharose supports. Polynucleotide binding preferences differed; for example, polyC interacted preferentially through its backbone with the two supports, whereas polyT bound the supports through its thymine moiety. STD-NMR combined with SPR measurements was successfully used to screen amino acid-nucleotide interactions and determine the binding affinities of the complexes.

Rapid evaluation of the binding energies in hydrogen-bonded amide-thymine and amide-uracil dimers in gas phase

The binding energies and the equilibrium hydrogen bond distances as well as the potential energy curves of 48 hydrogen-bonded amide-thymine and amide-uracil dimers are evaluated from the analytic potential energy function established in our lab recently. The calculation results show that the potential energy curves obtained from the analytic potential energy function are in good agreement with those obtained from MP2/6-311+G** calculations by including the BSSE correction. For all the 48 dimers, the analytic potential energy function yields the binding energies of the MP2/6-311+G** with BSSE correction within the error limits of 0.50 kcal/mol for 46 dimers, only two differences are larger than 0.50 kcal/mol and the largest one is only 0.60 kcal/mol. The analytic potential energy function produces the equilibrium hydrogen bond distances of the MP2/6-311+G** with BSSE correction within the error limits of 0.050 Å for all the 48 dimers. The analytic potential energy function is further applied to four more complicated hydrogen-bonded amide-base systems involving amino acid side chain and β-sheet. The values of the binding energies and equilibrium hydrogen bond distances obtained from the analytic potential energy function are also in good agreement with those obtained from MP2 calculations with the BSSE correction. These results demonstrate that the analytic potential energy function can be used to evaluate the binding energies in hydrogen-bonded amide-base dimers quickly and accurately.

The role of RNA sequence and structure in RNA--protein interactions

We investigate the sequence and structural properties of RNA--protein interaction sites in 211 RNA--protein chain pairs, the largest set of RNA--protein complexes analyzed to date. Statistical analysis confirms and extends earlier analyses made on smaller data sets. There are 24.6% of hydrogen bonds between RNA and protein that are nucleobase specific, indicating the importance of both nucleobase-specific and -nonspecific interactions. While there is no significant difference between RNA base frequencies in protein-binding and non-binding regions, distinct preferences for RNA bases, RNA structural states, protein residues, and protein secondary structure emerge when nucleobase-specific and -nonspecific interactions are considered separately. Guanine nucleobase and unpaired RNA structural states are significantly preferred in nucleobase-specific interactions; however, nonspecific interactions disfavor guanine, while still favoring unpaired RNA structural states. The opposite preferences of nucleobase-specific and -nonspecific interactions for guanine may explain discrepancies between earlier studies with regard to base preferences in RNA--protein interaction regions. Preferences for amino acid residues differ significantly between nucleobase-specific and -nonspecific interactions, with nonspecific interactions showing the expected bias towards positively charged residues. Irregular protein structures are strongly favored in interactions with the protein backbone, whereas there is little preference for specific protein secondary structure in either nucleobase-specific interaction or -nonspecific interaction. Overall, this study shows strong preferences for both RNA bases and RNA structural states in protein--RNA interactions, indicating their mutual importance in protein recognition.

Amino acids-nucleotides biomolecular recognition: from biological occurrence to affinity chromatography

In this review, the protein-DNA interactions are discussed considering different perspectives, and the biological occurrence of this interaction is explained at atomic level. The evaluation of the amino acid-nucleotide recognition has been investigated analysing datasets for predicting the association preferences and the geometry that favours the interaction. Based on this knowledge, an affinity chromatographic method was developed also exploiting this biological favoured contact. In fact, the implementation of this technique brings the possibility to apply the concept of molecular interactions to the development of new purification methodologies. In addition, the integration of the information recovered by all the different perspectives can bring new insights about some biological mechanisms, though not totally clarified.

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.

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.

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.

Cooperativity and specificity of Cys2His2 zinc finger protein-DNA interactions: a molecular dynamics simulation study

Cys(2)His(2) zinc finger proteins are one of the most frequently observed DNA-binding motifs in eukaryotes. They have been widely used as a framework for designing new DNA-binding proteins. In this work, the binding affinity and conformational change of the Zif268-DNA complex were successfully reproduced with MD simulations and MM-PBSA analysis. The following new discoveries on the zinc finger protein-DNA interactions were obtained by careful energy decomposition analysis. First, a dramatic increase in the binding affinity was observed when the third zinc finger is added, indicating a cooperative nature. This cooperativity is shown to be a consequence of the small but distinctive conformational change of DNA, which enables a tight fit of the protein into the major groove of DNA. Second, specificity of the amino acid-nucleotide recognitions observed in the crystal structure is explained as originating from the ability of specific side chains and bases to take the optimal geometries for favorable interactions between polar groups. The success of the current approach implies that similar methods could be further applied to the study of protein-DNA interactions involving longer polyfingers or different linkers between fingers to provide insights for design of novel zinc finger proteins.

DNABINDPROT: fluctuation-based predictor of DNA-binding residues within a network of interacting residues

DNABINDPROT is designed to predict DNA-binding residues, based on the fluctuations of residues in high-frequency modes by the Gaussian network model. The residue pairs that display high mean-square distance fluctuations are analyzed with respect to DNA binding, which are then filtered with their evolutionary conservation profiles and ranked according to their DNA-binding propensities. If the analyses are based on the exact outcome of fluctuations in the highest mode, using a conservation threshold of 5, the results have a sensitivity, specificity, precision and accuracy of 9.3%, 90.5%, 18.1% and 78.6%, respectively, on a dataset of 36 unbound–bound protein structure pairs. These values increase up to 24.3%, 93.4%, 45.3% and 83.3% for the respective cases, when the neighboring two residues are considered. The relatively low sensitivity appears with the identified residues being selective and susceptible more for the binding core residues rather than all DNA-binding residues. The predicted residues that are not tagged as DNA-binding residues are those whose fluctuations are coupled with DNA-binding sites. They are in close proximity as well as plausible for other functional residues, such as ligand and protein–protein interaction sites. DNABINDPROT is free and open to all users without login requirement available at: http://www.prc.boun.edu.tr/appserv/prc/dnabindprot/.

Structure of bacteriophage T4 endonuclease II mutant E118A, a tetrameric GIY-YIG enzyme

Coliphage T4 endonuclease II (EndoII), encoded by gene denA, is a small (16 kDa, 136 aa) enzyme belonging to the GIY-YIG family of endonucleases, which lacks a C-terminal domain corresponding to that providing most of the binding energy in the structurally characterized GIY-YIG endonucleases, I-TevI and UvrC. In vivo, it is involved in degradation of host DNA, permitting scavenging of host-derived nucleotides for phage DNA synthesis. EndoII primarily catalyzes single-stranded nicking of DNA; 5- to 10-fold less frequently double-stranded breaks are produced. The Glu118Ala mutant of EndoII was crystallized in space group P2(1) with four monomers in the asymmetric unit. The fold of the EndoII monomer is similar to that of the catalytic domains of UvrC and I-TevI. In contrast to these enzymes, EndoII forms a striking X-shaped tetrameric structure composed as a dimer of dimers, with a protruding hairpin domain not present in UvrC or I-TevI providing most of the dimerization and tetramerization interfaces. A bound phosphate ion in one of the four active sites of EndoII likely mimics the scissile phosphate in a true substrate complex. In silico docking experiments showed that a protruding loop containing a nuclease-associated modular domain 3 element is likely to be involved in substrate binding, as well as residues forming a separate nucleic acid binding surface adjacent to the active site. The positioning of these sites within the EndoII primary dimer suggests that the substrate would bind to a primary EndoII dimer diagonally over the active sites, requiring significant distortion of the enzyme or the substrate DNA, or both, for simultaneous nicking of both DNA strands. The scarcity of potential nucleic acid binding residues between the active sites indicates that EndoII may bind its substrate inefficiently across the two sites in the dimer, offering a plausible explanation for the catalytic preponderance of single-strand nicks. Mutations analyzed in earlier functional studies are discussed in their structural context.

Analysis of interactions between ribosomal proteins and RNA structural motifs

Background

One important goal of structural bioinformatics is to recognize and predict the interactions between protein binding sites and RNA. Recently, a comprehensive analysis of ribosomal proteins and their interactions with rRNA has been done. Interesting results emerged from the comparison of r-proteins within the small subunit in T. thermophilus and E. coli, supporting the idea of a core made by both RNA and proteins, conserved by evolution. Recent work showed also that ribosomal RNA is modularly composed. Motifs are generally single-stranded sequences of consecutive nucleotides (ssRNA) with characteristic folding. The role of these motifs in protein-RNA interactions has been so far only sparsely investigated.

Results

This work explores the role of RNA structural motifs in the interaction of proteins with ribosomal RNA (rRNA). We analyze composition, local geometries and conformation of interface regions involving motifs such as tetraloops, kink turns and single extruded nucleotides. We construct an interaction map of protein binding sites that allows us to identify the common types of shared 3-D physicochemical binding patterns for tetraloops. Furthermore, we investigate the protein binding pockets that accommodate single extruded nucleotides either involved in kink-turns or in arbitrary RNA strands. This analysis reveals a new structural motif, called tripod.

It corresponds to small pockets consisting of three aminoacids arranged at the vertices of an almost equilateral triangle. We developed a search procedure for the recognition of tripods, based on an empirical tripod fingerprint.

Conclusion

A comparative analysis with the overall RNA surface and interfaces shows that contact surfaces involving RNA motifs have distinctive features that may be useful for the recognition and prediction of interactions.