Structural features of protein-nucleic acid recognition sites

Binding of the bacteriophage P22 N-peptide to the boxB RNA motif studied by molecular dynamics simulations

Protein-RNA interactions are important for many cellular processes. The Nut-utilization site (N)-protein of bacteriophages contains an N-terminal arginine-rich motif that undergoes a folding transition upon binding to the boxB RNA hairpin loop target structure. Molecular dynamics simulations were used to investigate the dynamics of the P22 N-peptide-boxB complex and to elucidate the energetic contributions to binding. In addition, the free-energy changes of RNA and peptide conformational adaptation to the bound forms, as well as the role of strongly bound water molecules at the peptide-RNA interface, were studied. The influence of peptide amino acid substitutions and the salt dependence of interaction were investigated and showed good agreement with available experimental results. Several tightly bound water molecules were found at the RNA-binding interface in both the presence and absence of N-peptide. Explicit consideration of the waters resulted in shifts of calculated contributions during the energetic analysis, but overall similar binding energy contributions were found. Of interest, it was found that the electrostatic field of the RNA has a favorable influence on the coil-to-alpha-helix transition of the N-peptide already outside of the peptide-binding site. This result may have important implications for understanding peptide-RNA complex formation, which often involves coupled folding and association processes. It indicates that electrostatic interactions near RNA molecules can lead to a shift in the equilibrium toward the bound form of an interacting partner before it enters the binding pocket.

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.

Nonspecific DNA-protein interaction: why proteins can diffuse along DNA

Recent single molecule experiments have reported that DNA binding proteins (DNA-BPs) can diffuse along DNA. This suggests that interactions between proteins and DNA play a role during the target search even far from their specific site on DNA. Here we show by means of Monte Carlo simulations and analytical calculations that there is a counterintuitive repulsion between the two oppositely charged macromolecules at a nanometer range. For the concave shape of DNA-BPs, and for realistic protein charge densities, we find that the DNA-protein interaction free energy has a minimum at a finite surface-to-surface separation, in which proteins can easily slide. When a protein encounters its target, the free energy barrier is completely counterbalanced by the H-bond interaction, thus enabling the sequence recognition.

Cavities in protein-DNA and protein-RNA interfaces

An analysis of cavities present in protein–DNA and protein–RNA complexes is presented. In terms of the number of cavities and their total volume, the interfaces formed in these complexes are akin to those in transient protein–protein heterocomplexes. With homodimeric proteins protein–DNA interfaces may contain cavities involving both the protein subunits and DNA, and these are more than twice as large as cavities involving a single protein subunit and DNA. A parameter, cavity index, measuring the degree of surface complementarity, indicates that the packing of atoms in protein–protein/DNA/RNA is very similar, but it is about two times less efficient in the permanent interfaces formed between subunits in homodimers. As within the tertiary structure and protein–protein interfaces, protein–DNA interfaces have a higher inclination to be lined by β-sheet residues; from the DNA side, base atoms, in particular those in minor grooves, have a higher tendency to be located in cavities. The larger cavities tend to be less spherical and solvated. A small fraction of water molecules are found to mediate hydrogen-bond interactions with both the components, suggesting their primary role is to fill in the void left due to the local non-complementary nature of the surface patches.

Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks

Background

DNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA.

Results

Significant differences in residue preferences for specific contacts are observed, which combined with other features, lead to promising levels of prediction. In general, PSSM-based predictions, supported by secondary structure and solvent accessibility, achieve a good predictability of ~70–80%, measured by the area under the curve (AUC) of ROC graphs. The major and minor groove contact predictions stood out in terms of their poor predictability from sequences or PSSM, which was very strongly (>20 percentage points) compensated by the addition of secondary structure and solvent accessibility information, revealing a predominant role of local protein structure in the major/minor groove DNA-recognition. Following a detailed analysis of results, a web server to predict mononucleotide and dinucleotide-step contacts using PSSM was developed and made available at or .

Conclusion

Most residue-nucleotide contacts can be predicted with high accuracy using only sequence and evolutionary information. Major and minor groove contacts, however, depend profoundly on the local structure. Overall, this study takes us a step closer to the ultimate goal of predicting mutual recognition sites in protein and DNA sequences.

Identification of DNA-binding proteins using structural, electrostatic and evolutionary features

DNA-binding proteins (DBPs) participate in various crucial processes in the life-cycle of the cells, and the identification and characterization of these proteins is of great importance. We present here a random forests classifier for identifying DBPs among proteins with known 3D structures. First, clusters of evolutionarily conserved regions (patches) on the surface of proteins were detected using the PatchFinder algorithm; earlier studies showed that these regions are typically the functionally important regions of proteins. Next, we trained a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein, including its dipole moment. Using 10-fold cross-validation on a dataset of 138 DBPs and 110 proteins that do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of published methods. Furthermore, when we tested five different methods on 11 new DBPs that did not appear in the original dataset, only our method annotated all correctly. The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA.

Dissection, residue conservation, and structural classification of protein-DNA interfaces

The basic DNA-binding modules of 128 protein-DNA interfaces have been analyzed. Although these are less planar, like the protein-protein interfaces, the protein-DNA interfaces can also be dissected into core regions in which all the fully-buried atoms are located, and rim regions having atoms with residual accessibilities. The sequence entropy of the core residues is smaller than those in the rim, indicating that the former are better conserved and possibly contribute more towards the binding free energy, as has been implicated in protein-protein interactions. On the protein side, 1014 A(2) of the surface is buried of which 63% belong to the core. There are some differences in the propensities of residues to occur in the core and the rim. In the DNA strands, the nucleotide(s) containing fully-buried atoms in all three components usually occupy central positions of the binding region. A new classification scheme for the interfaces has been introduced based on the composition of secondary structural elements of residues and the results compared with the conventional classification of DNA-binding proteins, as well as the protein class of the molecule. It appears that a common framework may be developed to understand both protein-protein and protein-DNA interactions.

Molecular recognition and self-assembly special feature: Encapsulation and characterization of proton-bound amine homodimers in a water-soluble, self-assembled supramolecular host

Cyclic amines can be encapsulated in a water-soluble self-assembled supramolecular host upon protonation. The hydrogen-bonding ability of the cyclic amines, as well as the reduced degrees of rotational freedom, allows for the formation of proton-bound homodimers inside of the assembly that are otherwise not observable in aqueous solution. The generality of homodimer formation was explored with small N-alkyl aziridines, azetidines, pyrrolidines, and piperidines. Proton-bound homodimer formation is observed for N-alkylaziridines (R = methyl, isopropyl, tert-butyl), N-alkylazetidines (R = isopropyl, tert-butyl), and N-methylpyrrolidine. At high concentration, formation of a proton-bound homotrimer is observed in the case of N-methylaziridine. The homodimers stay intact inside the assembly over a large concentration range, thereby suggesting cooperative encapsulation. Both G3(MP2)B3 and G3B3 calculations of the proton-bound homodimers were used to investigate the enthalpy of the hydrogen bond in the proton-bound homodimers and suggest that the enthalpic gain upon formation of the proton-bound homodimers may drive guest encapsulation.

Insights on protein-DNA recognition by coarse grain modelling

Coarse grain modelling of macromolecules is a new approach, potentially well adapted to answer numerous issues, ranging from physics to biology. We propose here an original DNA coarse grain model specifically dedicated to protein-DNA docking, a crucial, but still largely unresolved, question in molecular biology. Using a representative set of protein-DNA complexes, we first show that our model is able to predict the interaction surface between the macromolecular partners taken in their bound form. In a second part, the impact of the DNA sequence and electrostatics, together with the DNA and protein conformations on docking is investigated. Our results strongly suggest that the overall DNA structure mainly contributes in discriminating the interaction site on cognate proteins. Direct electrostatic interactions between phosphate groups and amino acid side chains strengthen the binding. Overall, this work demonstrates that coarse grain modeling can reveal itself a precious auxiliary for a general and complete description and understanding of protein-DNA association mechanisms.

XPA gene, its product and biological roles

The 31 kDa XPA protein is part of the core incision complex of the mammalian nucleotide excision repair (NER) system and interacts with DNA as well as with many other NER subunits. In the absence of XPA, no incision complex can form and no excision of damaged DNA damage occurs. A comparative analysis of the DNA-binding properties in the presence of different substrate conformations indicated that XPA protein interacts preferentially with kinked DNA backbones. The DNA-binding domain of XPA protein displays a positively charged deft that is involved in an indirect readout mechanism, presumably by detecting the increased negative potential encountered at sharp DNA bends. We propose that this indirect recognition function contributes to damage verification by probing the susceptibility of the DNA substrate to be kinked during the assembly of NER complexes.

Protein–protein interaction and quaternary structure

Protein–protein recognition plays an essential role in structure and function. Specific non-covalent interactions stabilize the structure of macromolecular assemblies, exemplified in this review by oligomeric proteins and the capsids of icosahedral viruses. They also allow proteins to form complexes that have a very wide range of stability and lifetimes and are involved in all cellular processes. We present some of the structure-based computational methods that have been developed to characterize the quaternary structure of oligomeric proteins and other molecular assemblies and analyze the properties of the interfaces between the subunits. We compare the size, the chemical and amino acid compositions and the atomic packing of the subunit interfaces of protein–protein complexes, oligomeric proteins, viral capsids and protein–nucleic acid complexes. These biologically significant interfaces are generally close-packed, whereas the non-specific interfaces between molecules in protein crystals are loosely packed, an observation that gives a structural basis to specific recognition. A distinction is made within each interface between a core that contains buried atoms and a solvent accessible rim. The core and the rim differ in their amino acid composition and their conservation in evolution, and the distinction helps correlating the structural data with the results of site-directed mutagenesis and in vitro studies of self-assembly.

Computational structural analysis: multiple proteins bound to DNA

Background

With increasing numbers of crystal structures of protein∶DNA and protein∶protein∶DNA complexes publically available, it is now possible to extract sufficient structural, physical-chemical and thermodynamic parameters to make general observations and predictions about their interactions. In particular, the properties of macromolecular assemblies of multiple proteins bound to DNA have not previously been investigated in detail.

Methodology/Principal Findings

We have performed computational structural analyses on macromolecular assemblies of multiple proteins bound to DNA using a variety of different computational tools: PISA; PROMOTIF; X3DNA; ReadOut; DDNA and DCOMPLEX. Additionally, we have developed and employed an algorithm for approximate collision detection and overlapping volume estimation of two macromolecules. An implementation of this algorithm is available at http://promoterplot.fmi.ch/Collision1/. The results obtained are compared with structural, physical-chemical and thermodynamic parameters from protein∶protein and single protein∶DNA complexes. Many of interface properties of multiple protein∶DNA complexes were found to be very similar to those observed in binary protein∶DNA and protein∶protein complexes. However, the conformational change of the DNA upon protein binding is significantly higher when multiple proteins bind to it than is observed when single proteins bind. The water mediated contacts are less important (found in less quantity) between the interfaces of components in ternary (protein∶protein∶DNA) complexes than in those of binary complexes (protein∶protein and protein∶DNA).The thermodynamic stability of ternary complexes is also higher than in the binary interactions. Greater specificity and affinity of multiple proteins binding to DNA in comparison with binary protein-DNA interactions were observed. However, protein-protein binding affinities are stronger in complexes without the presence of DNA.

Conclusions/Significance

Our results indicate that the interface properties: interface area; number of interface residues/atoms and hydrogen bonds; and the distribution of interface residues, hydrogen bonds, van der Walls contacts and secondary structure motifs are independent of whether or not a protein is in a binary or ternary complex with DNA. However, changes in the shape of the DNA reduce the off-rate of the proteins which greatly enhances the stability and specificity of ternary complexes compared to binary ones.

Nonexponential kinetics of DNA escape from alpha-hemolysin nanopores

Throughput and resolution of DNA sequence detection technologies employing nanometer scale pores hinge on accurate kinetic descriptions of DNA motion in nanopores. We present the first detailed experimental study of DNA escape kinetics from alpha-hemolysin nanopores and show that anomalously long escape times for some events result in nonexponential kinetics. From the distribution of first-passage times, we determine that the energy barrier to escape follows a Poisson-like distribution, most likely due to stochastic weak binding events between the DNA and amino acid residues in the pore.

Structural segments and residue propensities in protein-RNA interfaces: comparison with protein-protein and protein-DNA complexes

The interface of a protein molecule that is involved in binding another protein, DNA or RNA has been characterized in terms of the number of unique secondary structural segments (SSSs), made up of stretches of helix, strand and non-regular (NR) regions. On average 10-11 segments define the protein interface in protein-protein (PP) and protein-DNA (PD) complexes, while the number is higher (14) for protein-RNA (PR) complexes. While the length of helical segments in PP interaction increases with the interface area, this is not the case in PD and PR complexes. The propensities of residues to occur in the three types of secondary structural elements (SSEs) in the interface relative to the corresponding elements in the protein tertiary structures have been calculated. Arg, Lys, Asn, Tyr, His and Gln are preferred residues in PR complexes; in addition, Ser and Thr are also favoured in PD interfaces.

Characterization of alpha helices interacting with nucleic acids

Protein-nucleic acid interactions play a vital role in most genetic processes. An enhanced insight into such interactions can be obtained from the structure database of these complexes. Here, we report an overall survey on the geometry of alpha helices which interact with nucleic acids through hydrogen bonds and/or non-bonded interactions. Using the program RADIL based on an algorithm developed from this laboratory, 161 alpha helices in 70 non-redundant nucleic acid binding protein chains solved using X-ray crystallography are analysed. The helical geometry has been characterized as bent, canonical, terminally or completely distorted. The analysis reveals that approximately 70% of the alpha helices possess distortions of any one kind, viz., bend, terminal distortion or complete distortion. Nearly one-third of the total helices possess bends, with a majority of the bending occurring in 5-15 degrees range. In addition, a majority of the bent helices approach the nucleic acid helix in a perpendicular direction. The program RADIL has been useful in characterizing the nucleic acid-induced structural variations in alpha helices, however small they may be.

Prediction of interacting single-stranded RNA bases by protein-binding patterns

Prediction of protein-RNA interactions at the atomic level of detail is crucial for our ability to understand and interfere with processes such as gene expression and regulation. Here, we investigate protein binding pockets that accommodate extruded nucleotides not involved in RNA base pairing. We observed that most of the protein-interacting nucleotides are part of a consecutive fragment of at least two nucleotides whose rings have significant interactions with the protein. Many of these share the same protein binding cavity and more than 30% of such pairs are pi-stacked. Since these local geometries cannot be inferred from the nucleotide identities, we present a novel framework for their prediction from the properties of protein binding sites. First, we present a classification of known RNA nucleotide and dinucleotide protein binding sites and identify the common types of shared 3-D physicochemical binding patterns. These are recognized by a new classification methodology that is based on spatial multiple alignment. The shared patterns reveal novel similarities between dinucleotide binding sites of proteins with different overall sequences, folds and functions. Given a protein structure, we use these patterns for the prediction of its RNA dinucleotide binding sites. Based on the binding modes of these nucleotides, we further predict an RNA fragment that interacts with those protein binding sites. With these knowledge-based predictions, we construct an RNA fragment that can have a previously unknown sequence and structure. In addition, we provide a drug design application in which the database of all known small-molecule binding sites is searched for regions similar to nucleotide and dinucleotide binding patterns, suggesting new fragments and scaffolds that can target them.

Dissecting protein-RNA recognition sites

We analyze the protein–RNA interfaces in 81 transient binary complexes taken from the Protein Data Bank. Those with tRNA or duplex RNA are larger than with single-stranded RNA, and comparable in size to protein–DNA interfaces. The protein side bears a strong positive electrostatic potential and resembles protein–DNA interfaces in its amino acid composition. On the RNA side, the phosphate contributes less, and the sugar much more, to the interaction than in protein–DNA complexes. On average, protein–RNA interfaces contain 20 hydrogen bonds, 7 that involve the phosphates, 5 the sugar 2′OH, and 6 the bases, and 32 water molecules. The average H-bond density per unit buried surface area is less with tRNA or single-stranded RNA than with duplex RNA. The atomic packing is also less compact in interfaces with tRNA. On the protein side, the main chain NH and Arg/Lys side chains account for nearly half of all H-bonds to RNA; the main chain CO and side chain acceptor groups, for a quarter. The 2′OH is a major player in protein–RNA recognition, and shape complementarity an important determinant, whereas electrostatics and direct base–protein interactions play a lesser part than in protein–DNA recognition.

Lipophilic peptides for gene delivery

DNA transfections are widely performed in research laboratories and in vivo gene delivery holds the promise for curing many diseases. The synthetic carriers or vectors for DNA are typically cationic lipids. However, in biology, the recognition of nucleic acids by proteins involves both electrostatic and stacking contributions. As such we have prepared a series of new lipophilic peptide vectors that possess lysine and tryptophan amino acids for evaluation. These lipophilic peptides show minimal cytotoxicity and enhanced in vitro gene transfection activity.

3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures

We present a set of protocols showing how to use the 3DNA suite of programs to analyze, rebuild and visualize three-dimensional nucleic-acid structures. The software determines a wide range of conformational parameters, including the identities and rigid-body parameters of interacting bases and base-pair steps, the nucleotides comprising helical fragments, the area of overlap of stacked bases and so on. The reconstruction of three-dimensional structure takes advantage of rigorously defined rigid-body parameters, producing rectangular block representations of the nucleic-acid bases and base pairs and all-atom models with approximate sugar-phosphate backbones. The visualization components create vector-based drawings and scenes that can be rendered as raster-graphics images, allowing for easy generation of publication-quality figures. The utility programs use geometric variables to control the view and scale of an object, for comparison of related structures. The commands run in seconds even for large structures. The software and related information are available at http://3dna.rutgers.edu/.

A knowledge-based potential function predicts the specificity and relative binding energy of RNA-binding proteins

RNA-protein interactions are fundamental to gene expression. Thus, the molecular basis for the sequence dependence of protein-RNA recognition has been extensively studied experimentally. However, there have been very few computational studies of this problem, and no sustained attempt has been made towards using computational methods to predict or alter the sequence-specificity of these proteins. In the present study, we provide a distance-dependent statistical potential function derived from our previous work on protein-DNA interactions. This potential function discriminates native structures from decoys, successfully predicts the native sequences recognized by sequence-specific RNA-binding proteins, and recapitulates experimentally determined relative changes in binding energy due to mutations of individual amino acids at protein-RNA interfaces. Thus, this work demonstrates that statistical models allow the quantitative analysis of protein-RNA recognition based on their structure and can be applied to modeling protein-RNA interfaces for prediction and design purposes.

Hydrogen bonds in protein–DNA complexes: Where geometry meets plasticity

Recognition of a DNA sequence by a protein is achieved by interface-coupled chemical and shape complementation. This complementation between the two molecules is clearly directional and is determined by the specific chemical contacts including mainly hydrogen bonds. Directionality is an instrumental property of hydrogen bonding as it influences molecular conformations, which also affects DNA-protein recognition. The prominent elements in the recognition of a particular DNA sequence by a protein are the hydrogen-bond donors and acceptors of the base pairs into the grooves of the DNA that must interact with complementary moieties of the protein partner. Protein side chains make most of the crucial contacts through bidentate and complex hydrogen-bonding interactions with DNA base edges hence conferring remarkable specificity.

WATGEN: an algorithm for modeling water networks at protein-protein interfaces

Water molecules at protein-protein interfaces contribute to the close packing of atoms and ensure complementarity between the protein surfaces, as well as mediating polar interactions. Therefore, modeling of interface water is of importance in understanding the structural basis of biomolecular association. We present an algorithm, WATGEN, which predicts locations for water molecules at a protein-protein or protein-peptide interface, given the atomic coordinates of the protein and peptide. A key element of the WATGEN algorithm is the prediction of water sites that can form multiple hydrogen bonds that bridge the binding interface. Trial calculations were performed on water networks predicted by WATGEN at 126 protein-peptide interfaces (X-ray resolutions <or= 2.0 A), using different criteria for water placement. The energies of the predicted water networks were evaluated in AMBER8 and used in the choice of parameters for WATGEN. The 126 interfaces include 1264 experimentally determined bridging water sites, and the WATGEN algorithm predicts 72 and 88% of these sites within 1.5 and 2.0 A, respectively. The predicted number of water molecules at each interface was much higher than the number of water molecules identified experimentally. Therefore, random placement of the same number of water molecules as that predicted at each interface was performed as a control, and resulted in only 22 and 40% of water sites placed within 1.5 and 2.0 A of experimental sites, respectively. Based on these data, we conclude that WATGEN can accurately predict the location of water molecules at a protein-peptide interface, and this may be of value for understanding the energetics and specificity of biomolecular association.

Prediction of DNA-binding residues from sequence

MOTIVATION

Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.

RESULTS

It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein-DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.

AVAILABILITY

http://cubic.bioc.columbia.edu/services/disis.

Role of hydrogen bonds in protein-DNA recognition: effect of nonplanar amino groups

Amino groups are one of the various types of hydrogen bond donors, abundantly found in protein main chains, protein side chains, and DNA bases. The polar hydrogen atoms of these groups exhibit short ranged, specific, and directional hydrogen bonds, which play a decisive role in the specificity and stability of protein-DNA complexes. To date, planar amino groups are only considered for the analysis of protein-DNA interfacial hydrogen bonds. This assumption regarding hydrogen atom positions possibly failed to establish the expected role of hydrogen bonds in protein-DNA recognition. We have performed ab initio quantum chemical studies on amino acid side chains and DNA bases containing amino groups as well as on specific hydrogen bonded residue pairs selected from high-resolution protein-DNA complex crystal structures. Our results suggest that occurrences of pyramidal amino groups are more probable in comparison with the usually adopted planar geometry. This increases the quality of the existing hydrogen bonds in almost all cases. Further, detailed analysis of protein-DNA interfacial hydrogen bonds in 107 crystal structures using the in-house program "pyrHBfind" indicates that consideration of energetically more preferred nonplanar amino groups improves the geometry of hydrogen bonds and also gives rise to new contacts amounting to nearly 14.5% of the existing interactions. Large improvements have been observed specifically for the amino groups of guanine, which faces the DNA minor groove and thus helps to resolve the problem of insufficient directional contacts observed in many minor groove binding complexes. Apart from guanine, improvement observed for asparagine, glutamine, adenine, or cytosine also indicates that the consideration of nonplanar amino groups leads to a more realistic scenario of hydrogen bonds occurring between protein and DNA residues.

Protein-RNA interactions: structural analysis and functional classes

A data set of 89 protein-RNA complexes has been extracted from the Protein Data Bank, and the nucleic acid recognition sites characterized through direct contacts, accessible surface area, and secondary structure motifs. The differences between RNA recognition sites that bind to RNAs in functional classes has also been analyzed. Analysis of the complete data set revealed that van der Waals interactions are more numerous than hydrogen bonds and the contacts made to the nucleic acid backbone occur more frequently than specific contacts to nucleotide bases. Of the base-specific contacts that were observed, contacts to guanine and adenine occurred most frequently. The most favored amino acid-nucleotide pairings observed were lysine-phosphate, tyrosine-uracil, arginine-phosphate, phenylalanine-adenine and tryptophan-guanine. The amino acid propensities showed that positively charged and polar residues were favored as expected, but also so were tryptophan and glycine. The propensities calculated for the functional classes showed trends similar to those observed for the complete data set. However, the analysis of hydrogen bond and van der Waal contacts showed that in general proteins complexed with messenger RNA, transfer RNA and viral RNA have more base specific contacts and less backbone contacts than expected, while proteins complexed with ribosomal RNA have less base-specific contacts than the expected. Hence, whilst the types of amino acids involved in the interfaces are similar, the distribution of specific contacts is dependent upon the functional class of the RNA bound.

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.

Effects of Hydrogen-Bonding and Stacking Interactions with Amino Acids on the Acidity of Uracil

The effects of hydrogen-bonding interactions with amino acids on the (N1) acidity of uracil are evaluated using (B3LYP) density functional theory. Many different binding arrangements of each amino acid to three uracil binding sites are considered. The effects on the uracil acidity are found to significantly depend upon the nature of the amino acid and the binding orientation, but weakly depend on the binding site. Our results reveal that in some instances small models for the amino acids can be used, while for other amino acids larger models are required to properly describe the binding to uracil. The gas-phase acidity of uracil is found to increase by up to approximately 60 kJ mol(-1) due to discrete hydrogen-bonding interactions. Although (MP2) stacking interactions with aromatic amino acids decrease the acidity of uracil, unexpected increases in the acidity are found when any of the aromatic amino acids, or the backbone, hydrogen bond to uracil. Consideration of enzymatic and aqueous environments leads to decreases in the effects of the amino acids on the acidity of uracil. However, we find that the magnitude of the decrease varies with the nature of the molecule bound, as well as the (gas-phase) binding orientations and strengths, and therefore solvation effects should be considered on a case-by-case basis in future work. Nevertheless, the effects of amino acid interactions within enzymatic environments are as much as approximately 35 kJ mol(-1). The present study has general implications for understanding the nature of active site amino acids in enzymes, such as DNA repair enzymes, that catalyze reactions involving anionic nucleobase intermediates.

Macromolecular recognition in the Protein Data Bank

Crystal structures deposited in the Protein Data Bank illustrate the diversity of biological macromolecular recognition: transient interactions in protein-protein and protein-DNA complexes and permanent assemblies in homodimeric proteins. The geometric and physical chemical properties of the macromolecular interfaces that may govern the stability and specificity of recognition are explored in complexes and homodimers compared with crystal-packing interactions. It is found that crystal-packing interfaces are usually much smaller; they bury fewer atoms and are less tightly packed than in specific assemblies. Standard-size interfaces burying 1200-2000 A2 of protein surface occur in protease-inhibitor and antigen-antibody complexes that assemble with little or no conformation changes. Short-lived electron-transfer complexes have small interfaces; the larger size of the interfaces observed in complexes involved in signal transduction and homodimers correlates with the presence of conformation changes, often implicated in biological function. Results of the CAPRI (critical assessment of predicted interactions) blind prediction experiment show that docking algorithms efficiently and accurately predict the mode of assembly of proteins that do not change conformation when they associate. They perform less well in the presence of large conformation changes and the experiment stimulates the development of novel procedures that can handle such changes.

Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction

Protein-RNA interactions play essential roles in a number of regulatory mechanisms for gene expression such as RNA splicing, transport, translation and post-transcriptional control. As the number of available protein-RNA complex 3D structures has increased, it is now possible to statistically examine protein-RNA interactions based on 3D structures. We performed computational analyses of 86 representative protein-RNA complexes retrieved from the Protein Data Bank. Interface residue propensity, a measure of the relative importance of different amino acid residues in the RNA interface, was calculated for each amino acid residue type (residue singlet interface propensity). In addition to the residue singlet propensity, we introduce a new residue-based propensity, which gives a measure of residue pairing preferences in the RNA interface of a protein (residue doublet interface propensity). The residue doublet interface propensity contains much more information than the sum of two singlet propensities alone. The prediction of the RNA interface using the two types of propensities plus a position-specific multiple sequence profile can achieve a specificity of about 80%. The prediction method was then applied to the 3D structure of two mRNA export factors, TAP (Mex67) and UAP56 (Sub2). The prediction enables us to point out candidate RNA interfaces, part of which are consistent with previous experimental studies and may contribute to elucidation of atomic mechanisms of mRNA export.

Orientation preferences of backbone secondary amide functional groups in peptide nucleic acid complexes: quantum chemical calculations reveal an intrinsic preference of cationic D-amino acid-based chiral PNA analogues for the P-form

Geometric descriptions of nonideal interresidue hydrogen bonding and backbone-base water bridging in the minor groove are established in terms of polyamide backbone carbonyl group orientation from analyses of residue junction conformers in experimentally determined peptide nucleic acid (PNA) complexes. Two types of interresidue hydrogen bonding are identified in PNA conformers in heteroduplexes with nucleic acids that adopt A-like basepair stacking. Quantum chemical calculations on the binding of a water molecule to an O2 base atom in glycine-based PNA thymine dimers indicate that junctions modeled with P-form backbone conformations are lower in energy than a dimer comprising the predominant conformation observed in A-like helices. It is further shown in model systems that PNA analogs based on D-lysine are better able to preorganize in a conformation exclusive to P-form helices than is glycine-based PNA. An intrinsic preference for this conformation is also exhibited by positively charged chiral PNA dimers carrying 3-amino-D-alanine or 4-aza-D-leucine residue units that provide for additional rigidity by side-chain hydrogen bonding to the backbone carbonyl oxygen. Structural modifications stabilizing P-form helices may obviate the need for large heterocycles to target DNA pyrimidine bases via PNA.DNA-PNA triplex formation. Quantum chemical modeling methods are used to propose candidate PNA Hoogsteen strand designs.

Structural assessment of single amino acid mutations: application to TP53 function

Single amino acid substitution is the type of protein alteration most related to human diseases. Current studies seek primarily to distinguish neutral mutations from harmful ones. Very few methods offer an explanation of the final prediction result in terms of the probable structural or functional effect on the protein. In this study, we describe the use of three novel parameters to identify experimentally-verified critical residues of the TP53 protein (p53). The first two parameters make use of a surface clustering method to calculate the protein surface area of highly conserved regions or regions with high nonlocal atomic interaction energy (ANOLEA) score. These parameters help identify important functional regions on the surface of a protein. The last parameter involves the use of a new method for pseudobinding free-energy estimation to specifically probe the importance of residue side-chains to the stability of protein fold. A decision tree was designed to optimally combine these three parameters. The result was compared to the functional data stored in the International Agency for Research on Cancer (IARC) TP53 mutation database. The final prediction achieved a prediction accuracy of 70% and a Matthews correlation coefficient of 0.45. It also showed a high specificity of 91.8%. Mutations in the 85 correctly identified important residues represented 81.7% of the total mutations recorded in the database. In addition, the method was able to correctly assign a probable functional or structural role to the residues. Such information could be critical for the interpretation and prediction of the effect of missense mutations, as it not only provided the fundamental explanation of the observed effect, but also helped design the most appropriate laboratory experiment to verify the prediction results.

Protein-RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures

MOTIVATION

The recognition of specific RNA sequences and structures by proteins is critical to our understanding of RNA processing, gene expression and viral replication. The diversity of RNA structures suggests that RNA recognition is substantially different than that of DNA.

RESULTS

The atomic coordinates of 41 protein-RNA complexes have been used to probe composite nucleoside binding pockets that form the structural and chemical underpinnings of base recognition. Composite nucleoside binding pockets were constructed using three-dimensional superpositions of each RNA nucleoside. Unlike protein-DNA interactions which are dominated by accessibility, RNA recognition frequently occurs in non-canonical and single-strand-like structures that allow interactions to occur from a much wider set of geometries and make fuller use of unique base shapes and hydrogen-bonding ability. By constructing composites that include all van der Waals, hydrogen-bonding, stacking and general non-polar interactions made to a particular nucleoside, the strategies employed are made readily visible. Protein-RNA interactions can result in the formation of a glove-like tight binding pocket around RNA bases, but the size, shape and non-polar binding patterns differ between specific RNA bases. We show that adenine can be distinguished from guanine based on the size and shape of the binding pocket and steric exclusion of the guanine N2 exocyclic amino group. The unique shape and hydrogen-bonding pattern for each RNA base allow proteins to make specific interactions through a very small number of contacts, as few as two in some cases.

AVAILABILITY

The program ENTANGLE is available from http://www.bioc.rice.edu/~shamoo

Classification of Protein-DNA Complexes Based on Structural Descriptors

We attempt to classify protein-DNA complexes by using a set of 11 descriptors, mainly characterizing protein-DNA interactions, including the number of atomic contacts at major and minor grooves, conformational deviations from standard B- and A-DNA forms, widths of DNA grooves, GC content, specificity measures of direct and indirect readouts, and buried surface area at the complex interface. The cluster analyses were carried out for a unique set of 62 complexes including a variety of protein motifs, and 7 distinct clusters were revealed from the analyses. We found that some proteins with the same motif are classified into different clusters, whereas different proteins with distinct motifs are classified into the same cluster. These results suggest that the conventional motif-based classification of DNA binding proteins may not necessarily correspond to structural and functional properties of protein-DNA complexes, and that the present classification will help to identify common properties and rules that govern protein-DNA recognition.

BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences

BindN (http://bioinformatics.ksu.edu/bindn/) takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pK(a) value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.

Design of accurate predictors for DNA-binding sites in proteins using hybrid SVM-PSSM method

In this paper, we investigate the design of accurate predictors for DNA-binding sites in proteins from amino acid sequences. As a result, we propose a hybrid method using support vector machine (SVM) in conjunction with evolutionary information of amino acid sequences in terms of their position-specific scoring matrices (PSSMs) for prediction of DNA-binding sites. Considering the numbers of binding and non-binding residues in proteins are significantly unequal, two additional weights as well as SVM parameters are analyzed and adopted to maximize net prediction (NP, an average of sensitivity and specificity) accuracy. To evaluate the generalization ability of the proposed method SVM-PSSM, a DNA-binding dataset PDC-59 consisting of 59 protein chains with low sequence identity on each other is additionally established. The SVM-based method using the same six-fold cross-validation procedure and PSSM features has NP=80.15% for the training dataset PDNA-62 and NP=69.54% for the test dataset PDC-59, which are much better than the existing neural network-based method by increasing the NP values for training and test accuracies up to 13.45% and 16.53%, respectively. Simulation results reveal that SVM-PSSM performs well in predicting DNA-binding sites of novel proteins from amino acid sequences.

How does a protein search for the specific site on DNA: The role of disorder

Proteins can locate their specific targets on DNA up to two orders of magnitude faster than the Smoluchowski three-dimensional diffusion rate. This happens due to nonspecific adsorption of proteins to DNA and subsequent one-dimensional sliding along DNA. We call such a one-dimensional route towards the target an "antenna." We studied the role of the dispersion of nonspecific binding energies within the antenna due to a quasirandom sequence of natural DNA. A random energy profile for sliding proteins slows the searching rate for the target. We show that this slowdown is different for macroscopic and mesoscopic antennas.

Molecular flexibility in protein–DNA interactions

In living cells protein-DNA interactions are fundamental processes. Here, we compare the 3D structures of several DNA-binding proteins frequently determined with and without attached DNA. We studied the global structure (backbone-traces) as well as the local structure (binding sites) by comparing pair-wise the related atoms. The DNA-interaction sites of uncomplexed proteins show conspicuously high local structural flexibility. Binding to DNA results in specific local conformations, which are clearly distinct from the unbound states. The adaptation of the protein's binding site to DNA can never be described by the lock and key model but in all cases by the induced fit model. Conformational changes in the seven protein backbone traces take place in different ways. Two of them dock onto DNA without a significant change, while the other five proteins are characterized by a backbone conformation change caused by DNA docking. In the case of three proteins of the latter group the DNA-complexed conformation also occurs in a few uncomplexed structures. This behavior can be described by a conformational ensemble, which is narrowed down by DNA docking until only one single DNA-complexed conformation occurs. Different docking models are discussed and each of the seven proteins is assigned to one of them.

Weight matrices for protein-DNA binding sites from a single co-crystal structure

Transcription-factor proteins bind to specific DNA sequences to regulate gene expression in cells. DNA-binding sites are often identified using weight matrices calculated from multiple known binding sites. However, in many cases the number of examples is limited. Here, we report on an atomistic method that starts from an x-ray co-crystal structure of the protein bound to one particular DNA sequence, and infers other binding sites, which are used to construct a weight matrix. The emphasis of the paper is on using the Wang-Landau Monte Carlo algorithm to efficiently sample high-affinity binding sites, which demonstrates that sampling can produce accurate weight matrices in analogy to bioinformatics approaches. For cases of low complexity, we compare to the exhaustive (but slow) dead-end elimination algorithm. To recover crystal binding sites, it is important to include bound water in the protein-DNA interface. Our approach can, in principle, even be applied when no native protein-DNA co-crystal structure is available, only the structure of a closely related homologous protein whose amino-acid sequence is changed to the protein of interest.