Protein-RNA interactions: a structural analysis

A detailed computational analysis of 32 protein–RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein–double-stranded DNA and protein–single-stranded DNA complexes. The interface properties of the protein–RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein–RNA and protein–DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein–RNA complexes, backbone contacts were more dominant in the protein–DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level.     

Beauty Is in the Eye of the Beholder: Proteins Can Recognize Binding Sites of Homologous Proteins in More than One Way

Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein–protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution.     

Protein–DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.     

Probing binding hot spots at protein–RNA recognition sites

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.     

Determination of protein-DNA binding constants and specificities from statistical analyses of single molecules: MutS-DNA interactions

Atomic force microscopy (AFM) is a powerful technique for examining the conformations of protein–DNA complexes and determining the stoichiometries and affinities of protein–protein complexes. We extend the capabilities of AFM to the determination of protein–DNA binding constants and specificities. The distribution of positions of the protein on the DNA fragments provides a direct measure of specificity and requires no knowledge of the absolute binding constants. The fractional occupancies of the protein at a given position in conjunction with the protein and DNA concentrations permit the determination of the absolute binding constants. We present the theoretical basis for this analysis and demonstrate its utility by characterizing the interaction of MutS with DNA fragments containing either no mismatch or a single mismatch. We show that MutS has significantly higher specificities for mismatches than was previously suggested from bulk studies and that the apparent low specificities are the result of high affinity binding to DNA ends. These results resolve the puzzle of the apparent low binding specificity of MutS with the expected high repair specificities. In conclusion, from a single set of AFM experiments, it is possible to determine the binding affinity, specificity and stoichiometry, as well as the conformational properties of the protein–DNA complexes.     

The molecular mechanism of DNA damage recognition by MutS homologs and its consequences for cell death response

We determined the molecular mechanism of cell death response by MutS homologs in distinction to the repair event. Key protein–DNA contacts differ in the interaction of MutS homologs with cisplatinated versus mismatched DNA. Mutational analyses of protein–DNA contacts, which were predicted by molecular dynamics (MD) simulations, were performed. Mutations in suggested interaction sites can affect repair and cell death response independently, and to different extents. A glutamate residue is identified as the key contact with cisplatin-DNA. Mutation of the residue increases cisplatin resistance due to increased non-specific DNA binding. In contrast, the conserved phenylalanine that is instrumental and indispensable for mismatch recognition during repair is not required for cisplatin cytotoxicity. These differences in protein–DNA interactions are translated into localized conformational changes that affect nucleotide requirements and inter-subunit interactions. Specifically, the ability for ATP binding/hydrolysis has little consequence for the MMR-dependent damage response. As a consequence, intersubunit contacts are altered that most likely affect the interaction with downstream proteins. We here describe the interaction of MutS homologs with DNA damage, as it differs from the interaction with a mismatch, and its structural translation into all other functional regions of the protein as a mechanism to initiate cell death response and concomitantly inhibit repair.     

A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys

RNA-binding proteins play many essential roles in the regulation of gene expression in the cell. Despite the significant increase in the number of structures for RNA–protein complexes in the last few years, the molecular basis of specificity remains unclear even for the best-studied protein families. We have developed a distance and orientation-dependent hydrogen-bonding potential based on the statistical analysis of hydrogen-bonding geometries that are observed in high-resolution crystal structures of protein–DNA and protein–RNA complexes. We observe very strong geometrical preferences that reflect significant energetic constraints on the relative placement of hydrogen-bonding atom pairs at protein–nucleic acid interfaces. A scoring function based on the hydrogen-bonding potential discriminates native protein–RNA structures from incorrectly docked decoys with remarkable predictive power. By incorporating the new hydrogen-bonding potential into a physical model of protein–RNA interfaces with full atom representation, we were able to recover native amino acids at protein–RNA interfaces.     

Assessment of the optimization of affinity and specificity at protein–DNA interfaces

The biological functions of DNA-binding proteins often require that they interact with their targets with high affinity and/or high specificity. Here, we describe a computational method that estimates the extent of optimization for affinity and specificity of amino acids at a protein–DNA interface based on the crystal structure of the complex, by modeling the changes in binding-free energy associated with all individual amino acid and base substitutions at the interface. The extent to which residues are predicted to be optimal for specificity versus affinity varies within a given protein–DNA interface and between different complexes, and in many cases recapitulates previous experimental observations. The approach provides a complement to traditional methods of mutational analysis, and should be useful for rapidly formulating hypotheses about the roles of amino acid residues in protein–DNA interfaces.     

Revealing A-T and G-C Hoogsteen base pairs in stressed protein-bound duplex DNA

Watson–Crick base pairs (bps) are the fundamental unit of genetic information and the building blocks of the DNA double helix. However, A-T and G-C can also form alternative ‘Hoogsteen’ bps, expanding the functional complexity of DNA. We developed ‘Hoog-finder’, which uses structural fingerprints to rapidly screen Hoogsteen bps, which may have been mismodeled as Watson–Crick in crystal structures of protein–DNA complexes. We uncovered 17 Hoogsteen bps, 7 of which were in complex with 6 proteins never before shown to bind Hoogsteen bps. The Hoogsteen bps occur near mismatches, nicks and lesions and some appear to participate in recognition and damage repair. Our results suggest a potentially broad role for Hoogsteen bps in stressed regions of the genome and call for a community-wide effort to identify these bps in current and future crystal structures of DNA and its complexes.     

Pushing the limits of what is achievable in protein–DNA docking: benchmarking HADDOCK’s performance

The intrinsic flexibility of DNA and the difficulty of identifying its interaction surface have long been challenges that prevented the development of efficient protein–DNA docking methods. We have demonstrated the ability our flexible data-driven docking method HADDOCK to deal with these before, by using custom-built DNA structural models. Here we put our method to the test on a set of 47 complexes from the protein–DNA docking benchmark. We show that HADDOCK is able to predict many of the specific DNA conformational changes required to assemble the interface(s). Our DNA analysis and modelling procedure captures the bend and twist motions occurring upon complex formation and uses these to generate custom-built DNA structural models, more closely resembling the bound form, for use in a second docking round. We achieve throughout the benchmark an overall success rate of 94% of one-star solutions or higher (interface root mean square deviation ≤4 Å and fraction of native contacts >10%) according to CAPRI criteria. Our improved protocol successfully predicts even the challenging protein–DNA complexes in the benchmark. Finally, our method is the first to readily dock multiple molecules (N > 2) simultaneously, pushing the limits of what is currently achievable in the field of protein–DNA docking.     

DNA phosphate crowding correlates with protein cationic side chain density and helical curvature in protein/DNA crystal structures

Sequence-specific binding of proteins to their DNA targets involves a complex spectrum of processes that often induce DNA conformational variation in the bound complex. The forces imposed by protein binding that cause the helical deformations are intimately interrelated and difficult to parse or rank in importance. To investigate the role of electrostatics in helical deformation, we quantified the relationship between protein cationic residue density (Cpc) and DNA phosphate crowding (Cpp). The correlation between Cpc and Cpp was then calculated for a subset of 58 high resolution protein–DNA crystal structures. Those structures containing strong Cpc/Cpp correlation (>±0.25) were likely to contain DNA helical curvature. Further, the correlation factor sign predicted the direction of helical curvature with positive (16 structures) and negative (seven structures) correlation containing concave (DNA curved toward protein) and convex (DNA curved away from protein) curvature, respectively. Protein–DNA complexes without significant Cpc/Cpp (36 structures) correlation (-0.25<0<0.25) tended to contain DNA without significant curvature. Interestingly, concave and convex complexes also include more arginine and lysine phosphate contacts, respectively, whereas linear complexes included essentially equivalent numbers of Lys/Arg phosphate contacts. Together, these findings suggest an important role for electrostatic interactions in protein–DNA complexes involving helical curvature.     

Structural motifs in protein cores and at protein–protein interfaces are different

Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.     

Solution measurement of DNA curvature in papillomavirus E2 binding sites

‘Indirect readout’ refers to the proposal that proteins can recognize the intrinsic three-dimensional shape or flexibility of a DNA binding sequence apart from direct protein contact with DNA base pairs. The differing affinities of human papillomavirus (HPV) E2 proteins for different E2 binding sites have been proposed to reflect indirect readout. DNA bending has been observed in X-ray structures of E2 protein–DNA complexes. X-ray structures of three different E2 DNA binding sites revealed differences in intrinsic curvature. DNA sites with intrinsic curvature in the direction of protein-induced bending were bound more tightly by E2 proteins, supporting the indirect readout model. We now report solution measurements of intrinsic DNA curvature for three E2 binding sites using a sensitive electrophoretic phasing assay. Measured E2 site curvature agrees well the predictions of a dinucleotide model and supports an indirect readout hypothesis for DNA recognition by HPV E2.     

DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces

Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-targeting proteins. Characteristics of such binding sites may form the basis for predicting DNA-binding sites from the structures of proteins alone. Such an approach has been successfully developed for predicting protein–protein interface. Here this approach is adapted for predicting DNA-binding sites. We used a representative set of 264 protein–DNA complexes from the Protein Data Bank to analyze characteristics and to train and test a neural network predictor of DNA-binding sites. The input to the predictor consisted of PSI-blast sequence profiles and solvent accessibilities of each surface residue and 14 of its closest neighboring residues. Predicted DNA-contacting residues cover 60% of actual DNA-contacting residues and have an accuracy of 76%. This method significantly outperforms previous attempts of DNA-binding site predictions. Its application to the prion protein yielded a DNA-binding site that is consistent with recent NMR chemical shift perturbation data, suggesting that it can complement experimental techniques in characterizing protein–DNA interfaces.     

Hydration of protein–RNA recognition sites

We investigate the role of water molecules in 89 protein–RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein–RNA interfaces are hydrated less than protein–DNA interfaces, but more than protein–protein interfaces. Majority of the waters at protein–RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein–DNA interfaces, mainly due to the presence of the 2′OH, the ribose in protein–RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein–RNA interfaces is hydrated more than the major groove, while in protein–DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein–RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein–RNA recognition and should be carefully treated while engineering protein–RNA interfaces.     

Identification of structural transitions in bacterial fatty acid binding proteins that permit ligand entry and exit at membranes

Fatty acid (FA) transfer proteins extract FA from membranes and sequester them to facilitate their movement through the cytosol. Detailed structural information is available for these soluble protein–FA complexes, but the structure of the protein conformation responsible for FA exchange at the membrane is unknown. Staphylococcus aureus FakB1 is a prototypical bacterial FA transfer protein that binds palmitate within a narrow, buried tunnel. Here, we define the conformational change from a “closed” FakB1 state to an “open” state that associates with the membrane and provides a path for entry and egress of the FA. Using NMR spectroscopy, we identified a conformationally flexible dynamic region in FakB1, and X-ray crystallography of FakB1 mutants captured the conformation of the open state. In addition, molecular dynamics simulations show that the new amphipathic α-helix formed in the open state inserts below the phosphate plane of the bilayer to create a diffusion channel for the hydrophobic FA tail to access the hydrocarbon core and place the carboxyl group at the phosphate layer. The membrane binding and catalytic properties of site-directed mutants were consistent with the proposed membrane docked structure predicted by our molecular dynamics simulations. Finally, the structure of the bilayer-associated conformation of FakB1 has local similarities with mammalian FA binding proteins and provides a conceptual framework for how these proteins interact with the membrane to create a diffusion channel from the FA location in the bilayer to the protein interior.