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.     

A rapid and sensitive high-throughput screening method to identify compounds targeting protein–nucleic acids interactions

DNA-binding and RNA-binding proteins are usually considered ‘undruggable’ partly due to the lack of an efficient method to identify inhibitors from existing small molecule repositories. Here we report a rapid and sensitive high-throughput screening approach to identify compounds targeting protein–nucleic acids interactions based on protein–DNA or protein–RNA interaction enzyme-linked immunosorbent assays (PDI-ELISA or PRI-ELISA). We validated the PDI-ELISA method using the mammalian high-mobility-group protein AT-hook 2 (HMGA2) as the protein of interest and netropsin as the inhibitor of HMGA2–DNA interactions. With this method we successfully identified several inhibitors and an activator for HMGA2–DNA interactions from a collection of 29 DNA-binding compounds. Guided by this screening excise, we showed that netropsin, the specific inhibitor of HMGA2–DNA interactions, strongly inhibited the differentiation of the mouse pre-adipocyte 3T3-L1 cells into adipocytes, most likely through a mechanism by which the inhibition is through preventing the binding of HMGA2 to the target DNA sequences. This method should be broadly applicable to identify compounds or proteins modulating many DNA-binding or RNA-binding proteins.     

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.     

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.     

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Cα deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.     

Highly conserved amino acids in Pax and Ets proteins are required for DNA binding and ternary complex assembly

Combinatorial association of DNA-binding proteins on composite binding sites enhances their nucleotide sequence specificity and functional synergy. As a paradigm for these interactions, Pax-5 (BSAP) assembles ternary complexes with Ets proteins on the B cell-specific mb-1 promoter through interactions between their respective DNA-binding domains. Pax-5 recruits Ets-1 to bind the promoter, but not the closely related Ets protein SAP1a. Here we show that, while several different mutations increase binding of SAP1a to an optimized Ets binding site, only conversion of Val68 to an acidic amino acid facilitates ternary complex assembly with Pax-5 on the mb-1 promoter. This suggests that enhanced DNA binding by SAP1a is not sufficient for recruitment by Pax-5, but instead involves protein–protein interactions mediated by the acidic side chain. Recruitment of Ets proteins by Pax-5 requires Gln22 within the N-terminal β-hairpin motif of its paired domain. The β-hairpin also participates in recognition of a subset of Pax-5-binding sites. Thus, Pax-5 incorporates protein–protein interaction and DNA recognition functions in a single motif. The Caenorhabditis elegans Pax protein EGL-38 also binds specifically to the mb-1 promoter and recruits murine Ets-1 or the C.elegans Ets protein T08H4.3, but not the related LIN-1 protein. Together, our results define specific amino acid requirements for Pax–Ets ternary complex assembly and show that the mechanism is conserved between evolutionarily related proteins of diverse animal species. Moreover, the data suggest that interactions between Pax and Ets proteins are an important mechanism that regulates fundamental biological processes in worms and humans.     

Accurate Prediction of Peptide Binding Sites on Protein Surfaces

Many important protein–protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled) but where no structure of the protein–peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein–peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.     

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.     

M-ORBIS: Mapping of mOleculaR Binding sItes and Surfaces

M-ORBIS is a Molecular Cartography approach that performs integrative high-throughput analysis of structural data to localize all types of binding sites and associated partners by homology and to characterize their properties and behaviors in a systemic way. The robustness of our binding site inferences was compared to four curated datasets corresponding to protein heterodimers and homodimers and protein–DNA/RNA assemblies. The Molecular Cartographies of structurally well-detailed proteins shows that 44% of their surfaces interact with non-solvent partners. Residue contact frequencies with water suggest that ∼86% of their surfaces are transiently solvated, whereas only 15% are specifically solvated. Our analysis also reveals the existence of two major binding site families: specific binding sites which can only bind one type of molecule (protein, DNA, RNA, etc.) and polyvalent binding sites that can bind several distinct types of molecule. Specific homodimer binding sites are for instance nearly twice as hydrophobic than previously described and more closely resemble the protein core, while polyvalent binding sites able to form homo and heterodimers more closely resemble the surfaces involved in crystal packing. Similarly, the regions able to bind DNA and to alternatively form homodimers, are more hydrophobic and less polar than previously described DNA binding sites.     

Structure of the DNA-binding domain of NgTRF1 reveals unique features of plant telomere-binding proteins

Telomeres are protein–DNA elements that are located at the ends of linear eukaryotic chromosomes. In concert with various telomere-binding proteins, they play an essential role in genome stability. We determined the structure of the DNA-binding domain of NgTRF1, a double-stranded telomere-binding protein of tobacco, using multidimensional NMR spectroscopy and X-ray crystallography. The DNA-binding domain of NgTRF1 contained the Myb-like domain and C-terminal Myb-extension that is characteristic of plant double-stranded telomere-binding proteins. It encompassed amino acids 561–681 (NgTRF1^561–681), and was composed of 4 α-helices. We also determined the structure of NgTRF1^561–681 bound to plant telomeric DNA. We identified several amino acid residues that interacted directly with DNA, and confirmed their role in the binding of NgTRF1 to telomere using site-directed mutagenesis. Based on a structural comparison of the DNA-binding domains of NgTRF1 and human TRF1 (hTRF1), NgTRF1 has both common and unique DNA-binding properties. Interaction of Myb-like domain with telomeric sequences is almost identical in NgTRF1^561–681 with the DNA-binding domain of hTRF1. The interaction of Arg-638 with the telomeric DNA, which is unique in NgTRF1^561–681, may provide the structural explanation for the specificity of NgTRF1 to the plant telomere sequences, (TTTAGGG)_n.     

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.     

A phage display selection of engrailed homeodomain mutants and the importance of residue Q50

Mutants of engrailed homeodomain (HD) that retain DNA-binding activity were isolated using a phage display selection. This selection was used to enrich for active DNA-binding clones from a complex library consisting of over a billion members. A more focused library of mutant homeodomains consisting of all possible amino acid combinations at two DNA-contacting residues (I47 and Q50) was constructed and screened for members capable of binding tightly and specifically to the engrailed consensus sequence, TAATTA. The isolated mutants largely recapitulated the distribution of amino acids found at these positions in natural homeodomains thus validating the in vitro selection conditions. In particular, the unequivocal advantage enjoyed by glutamine at residue 50 is surprising in light of reports that minimize the importance of this residue. Here, the subtle contributions of residue Q50 are demonstrated to play a functionally important role in specific recognition of DNA. These results highlight the complex subtlety of protein–DNA interactions, underscoring the value of the first reported in vitro selection of a homeodomain.     

The Modular Organization of Domain Structures: Insights into Protein–Protein Binding

Domains are the building blocks of proteins and play a crucial role in protein–protein interactions. Here, we propose a new approach for the analysis and prediction of domain–domain interfaces. Our method, which relies on the representation of domains as residue-interacting networks, finds an optimal decomposition of domain structures into modules. The resulting modules comprise highly cooperative residues, which exhibit few connections with other modules. We found that non-overlapping binding sites in a domain, involved in different domain–domain interactions, are generally contained in different modules. This observation indicates that our modular decomposition is able to separate protein domains into regions with specialized functions. Our results show that modules with high modularity values identify binding site regions, demonstrating the predictive character of modularity. Furthermore, the combination of modularity with other characteristics, such as sequence conservation or surface patches, was found to improve our predictions. In an attempt to give a physical interpretation to the modular architecture of domains, we analyzed in detail six examples of protein domains with available experimental binding data. The modular configuration of the TEM1-β-lactamase binding site illustrates the energetic independence of hotspots located in different modules and the cooperativity of those sited within the same modules. The energetic and structural cooperativity between intramodular residues is also clearly shown in the example of the chymotrypsin inhibitor, where non–binding site residues have a synergistic effect on binding. Interestingly, the binding site of the T cell receptor β chain variable domain 2.1 is contained in one module, which includes structurally distant hot regions displaying positive cooperativity. These findings support the idea that modules possess certain functional and energetic independence. A modular organization of binding sites confers robustness and flexibility to the performance of the functional activity, and facilitates the evolution of protein interactions.