Protein-RNA interactions: structural analysis and functional classes

Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction

Background

Protein-RNA interactions play important role in many biological processes such as gene regulation, replication, protein synthesis and virus assembly. Although many structures of various types of protein-RNA complexes have been determined, the mechanism of protein-RNA recognition remains elusive. We have earlier shown that the simplest electrostatic properties viz. charge, dipole and quadrupole moments, calculated from backbone atomic coordinates of proteins are biased relative to other proteins, and these quantities can be used to identify DNA-binding proteins. Closely related, RNA-binding proteins are investigated in this study. In particular, discrimination between various types of RNA-binding proteins, evolutionary conservation of these bulk electrostatic features and effect of conformational changes by complex formation are investigated. Basic binding mechanism of a putative RNA-binding protein (HI1333 from Haemophilus influenza) is suggested as a potential application of this study.

Results

We found that similar to DNA-binding proteins (DBPs), RNA-binding proteins (RBPs) also show significantly higher values of electric moments. However, higher moments in RBPs are found to strongly depend on their functional class: proteins binding to ribosomal RNA (rRNA) constitute the only class with all three of the properties (charge, dipole and quadrupole moments) being higher than control proteins. Neural networks were trained using leave-one-out cross-validation to predict RBPs from control data as well as pair-wise classification capacity between proteins binding to various RNA types. RBPs and control proteins reached up to 78% accuracy measured by the area under the ROC curve. Proteins binding to rRNA are found to be best distinguished (AUC = 79%). Changes in dipole and quadrupole moments between unbound and bound structures were small and these properties are found to be robust under complex formation.

Conclusions

Bulk electric moments of proteins considered here provide insights into target recognition by RNA-binding proteins, as well as ability to recognize one type of RBP from others. These results help in understanding the mechanism of protein-RNA recognition, and identifying RNA-binding proteins.

Systematic classification and analysis of themes in protein-DNA recognition

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

Predicting RNA-binding residues from evolutionary information and sequence conservation

Abstract

Evolution in action: N and C termini of subunits in related T = 4 viruses exchange roles as molecular switches

The T = 4 tetravirus and T = 3 nodavirus capsid proteins undergo closely similar autoproteolysis to produce the N-terminal beta and C-terminal, lipophilic gamma polypeptides. The gamma peptides and the N termini of beta also act as molecular switches that determine their quasi equivalent capsid structures. The crystal structure of Providence virus (PrV), only the second of a tetravirus (the first was NomegaV), reveals conserved folds and cleavage sites, but the protein termini have completely different structures and the opposite functions of those in NomegaV. N termini of beta form the molecular switch in PrV, whereas gamma peptides play this role in NomegaV. PrV gamma peptides instead interact with packaged RNA at the particle two-folds by using a repeating sequence pattern found in only four other RNA- or membrane-binding proteins. The disposition of peptide termini in PrV is closely related to those in nodaviruses, suggesting that PrV may be closer to the primordial T = 4 particle than NomegaV.

The 2.2-Å structure of the HP0958 protein from Helicobacter pylori reveals a kinked anti-parallel coiled-coil hairpin domain and a highly conserved ZN-ribbon domain

We have determined the 2.2-Å structure of the HP0958 protein from the human gastric pathogen Helicobacter pylori. HP0958 is essential for flagellum formation and motility. It functions as a chaperone for RpoN (σ(54)) and also controls the stability and translation of mRNA for the major flagellin subunit FlaA. The protein is composed of a highly elongated and kinked coiled-coil hairpin domain (residues 1-170), followed by a C(4) Zn-ribbon domain (residues 174-238). The Zn-ribbon domain is rich in aromatic and positively charged amino acid residues. Electrophoretic mobility shift assays identified residues in a positively charged region of the Zn-ribbon domain of HP0958 whose mutation alters the mobility of an HP0958-flaA mRNA complex. Mutation of surface residues in the coiled-coil domain did not result in an observable change in the mobility of the HP0958-flaA transcript complex. The data thus suggest the arrangement of HP0958 into distinct structural and functional domains.

Predicting in vivo binding sites of RNA-binding proteins using mRNA secondary structure

While many RNA-binding proteins (RBPs) bind RNA in a sequence-specific manner, their sequence preferences alone do not distinguish known target RNAs from other potential targets that are coexpressed and contain the same sequence motifs. Recently, the mRNA targets of dozens of RNA-binding proteins have been identified, facilitating a systematic study of the features of target transcripts. Using these data, we demonstrate that calculating the predicted structural accessibility of a putative RBP binding site allows one to significantly improve the accuracy of predicting in vivo binding for the majority of sequence-specific RBPs. In our new in silico approach, accessibility is predicted based solely on the mRNA sequence without consideration of the locations of bound trans-factors; as such, our results suggest a greater than previously anticipated role for intrinsic mRNA secondary structure in determining RBP binding target preference. Target site accessibility aids in predicting target transcripts and the binding sites for RBPs with a range of RNA-binding domains and subcellular functions. Based on this work, we introduce a new motif-finding algorithm that identifies accessible sequence-specific RBP motifs from in vivo binding data.

The archaeo-eukaryotic primase of plasmid pRN1 requires a helix bundle domain for faithful primer synthesis

The plasmid pRN1 encodes for a multifunctional replication protein with primase, DNA polymerase and helicase activity. The minimal region required for primase activity encompasses amino-acid residues 40–370. While the N-terminal part of that minimal region (residues 47–247) folds into the prim/pol domain and bears the active site, the structure and function of the C-terminal part (residues 248–370) is unknown. Here we show that the C-terminal part of the minimal region folds into a compact domain with six helices and is stabilized by a disulfide bond. Three helices superimpose well with the C-terminal domain of the primase of the bacterial broad host range plasmid RSF1010. Structure-based site-directed mutagenesis shows that the C-terminal helix of the helix bundle domain is required for primase activity although it is distant to the active site in the crystallized conformation. Furthermore, we identified mutants of the C-terminal domain, which are defective in template binding, dinucleotide formation and conformation change prior to DNA extension.

PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences

The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email. The prediction results are provided in sequence format with predicted RBRs highlighted, in text format with the SVM threshold score indicated and as a graph which enables users to quickly identify those residues above any specific SVM threshold. The graph effectively enables the increase or decrease of the false positive rate. When tested on a non-redundant data set of 42 protein sequences not used in training, the PiRaNhA server achieved an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers. The PiRaNhA prediction server is freely available at http://www.bioinformatics.sussex.ac.uk/PIRANHA.

Prediction of protein-RNA binding sites by a random forest method with combined features

MOTIVATION

Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide.

RESULTS

In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence- and structure-based features. Specifically, the interaction propensity represents a binding specificity of a protein residue to the interacting RNA nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure-based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein-RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85 and AUC of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein-RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues.

AVAILABILITY

All the source data and code are available at http://www.aporc.org/doc/wiki/PRNA or http://www.sysbio.ac.cn/datatools.asp

CONTACT

lnchen@sibs.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Simple, recurring RNA binding sites for L-arginine

Seven new arginine binding motifs have been selected from a heterogeneous RNA pool containing 17, 25, and 50mer randomized tracts, yielding 131 independently derived binding sites that are multiply isolated. The shortest 17mer random region is sufficient to build varied arginine binding sites using five different conserved motifs (motifs 1a, 1b, 1c, 2, and 4). Dissociation constants are in the fractional millimolar to millimolar range. Binding sites are amino acid side-chain specific and discriminate moderately between L- and D-stereoisomers of arginine, suggesting a molecular focus on side-chain guanidinium. An arginine coding triplet (codon/anticodon) is highly conserved within the largest family of Arg sites (72% of all sequences), as has also been found in minimal, most prevalent RNA binding sites for Ile, His, and Trp.

Geometric similarity between protein-RNA interfaces

A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.

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 overlap of small molecule and protein binding sites within families of protein structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators.

Proteins function through their interactions with other biological molecules, including other proteins. Often times, these interactions underlie cellular processes that go awry in disease. Therefore, modulating these interactions with small molecules is an active area of research for new drugs to treat diseases and new chemical tools to dissect cellular interaction networks. However, targeting protein–protein interactions has proven to be more challenging than the typical drug targets found on individual proteins. Here, we present a computational approach that aims to help in this challenge by identifying regions of protein–protein interfaces that may be amenable to targeting by small molecules. Through a comprehensive analysis of all known protein structures, we identify closely related proteins that in one case bind a protein and in another case bind a small molecule. We find that a significant number of protein–protein interactions occur through surface regions that bind small molecules in related proteins. These “bi-functional” positions, which can bind both proteins and ligands, will serve as an additional piece of structural information that can aid experimentalists in developing small molecules that modulate protein interactions.

Analysis of interactions between ribosomal proteins and RNA structural motifs

Background

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

Results

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

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

Conclusion

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

Mapping of interaction sites of the Schizosaccharomyces pombe protein Translin with nucleic acids and proteins: a combined molecular genetics and bioinformatics study

Translin is a single-stranded RNA- and DNA-binding protein, which has been highly conserved in eukaryotes, from man to Schizosaccharomyces pombe. TRAX is a Translin paralog associated with Translin, which has coevolved with it. We generated structural models of the S. pombe Translin (spTranslin), based on the solved 3D structure of the human ortholog. Using several bioinformatics computation tools, we identified in the equatorial part of the protein a putative nucleic acids interaction surface, which includes many polar and positively charged residues, mostly arginines, surrounding a shallow cavity. Experimental verification of the bioinformatics predictions was obtained by assays of nucleic acids binding to amino acid substitution variants made in this region. Bioinformatics combined with yeast two-hybrid assays and proteomic analyses of deletion variants, also identified at the top of the spTranslin structure a region required for interaction with spTRAX, and for spTranslin dimerization. In addition, bioinformatics predicted the presence of a second protein-protein interaction site at the bottom of the spTranslin structure. Similar nucleic acid and protein interaction sites were also predicted for the human Translin. Thus, our results appear to generally apply to the Translin family of proteins, and are expected to contribute to a further elucidation of their functions.

RNA-binding residues in sequence space: conservation and interaction patterns

RNA-binding proteins (RBPs) perform fundamental and diverse functions within the cell. Approximately 15% of proteins sequences are annotated as RNA-binding, but with a significant number of proteins without functional annotation, many RBPs are yet to be identified. A percentage of uncharacterised proteins can be annotated by transferring functional information from proteins sharing significant sequence homology. However, genomes contain a significant number of orphan open reading frames (ORFs) that do not share significant sequence similarity to other ORFs, but correspond to functional proteins. Hence methods for protein function annotation that go beyond sequence homology are essential. One method of annotation is the identification of ligands that bind to proteins, through the characterisation of binding site residues. In the current work RNA-binding residues (RBRs) are characterised in terms of their evolutionary conservation and the patterns they form in sequence space. The potential for such characteristics to be used to identify RBPs from sequence is then evaluated. In the current work the conservation of residues in 261 RBPs is compared for (a) RBRs vs. non-RBRs surface residues, and for (b) specific and non-specific RBRs. The analysis shows that RBRs are more conserved than other surface residues, and RBRs hydrogen-bonded to the RNA backbone are more conserved than those making hydrogen bonds to RNA bases. This observed conservation of RBRs was then used to inform the construction of RBR sequence patterns from known protein-RNA structures. A series of RBR patterns were generated for a case study protein aspartyl-tRNA synthetase bound to tRNA; and used to differentiate between RNA-binding and non-RNA-binding protein sequences. Six sequence patterns performed with high precision values of >80% and recall values 7 times that of an homology search. When the method was expanded to the complete dataset of 261 proteins, many patterns were of poor predictive value, as they had not been manipulated on a family-specific basis. However, two patterns with precision values > or = 85% were used to make function predictions for a set of hypothetical proteins. This revealed a number of potential RBPs that require experimental verification.

Ligand binding and substrate discrimination by UDP-galactopyranose mutase

Galactofuranose (Galf) residues are present in cell wall glycoconjugates of numerous pathogenic microbes. Uridine 5'-diphosphate (UDP) Galf, the biosynthetic precursor of Galf-containing glycoconjugates, is produced from UDP-galactopyranose (UDP-Galp) by the flavoenzyme UDP-galactopyranose mutase (UGM). The gene encoding UGM (glf) is essential for the viability of pathogens, including Mycobacterium tuberculosis, and this finding underscores the need to understand how UGM functions. Considerable effort has been devoted to elucidating the catalytic mechanism of UGM, but progress has been hindered by a lack of structural data for an enzyme-substrate complex. Such data could reveal not only substrate binding interactions but how UGM can act preferentially on two very different substrates, UDP-Galp and UDP-Galf, yet avoid other structurally related UDP sugars present in the cell. Herein, we describe the first structure of a UGM-ligand complex, which provides insight into the catalytic mechanism and molecular basis for substrate selectivity. The structure of UGM from Klebsiella pneumoniae bound to the substrate analog UDP-glucose (UDP-Glc) was solved by X-ray crystallographic methods and refined to 2.5 A resolution. The ligand is proximal to the cofactor, a finding that is consistent with a proposed mechanism in which the reduced flavin engages in covalent catalysis. Despite this proximity, the glucose ring of the substrate analog is positioned such that it disfavors covalent catalysis. This orientation is consistent with data indicating that UDP-Glc is not a substrate for UGM. The relative binding orientations of UDP-Galp and UDP-Glc were compared using saturation transfer difference NMR. The results indicate that the uridine moiety occupies a similar location in both ligand complexes, and this relevant binding mode is defined by our structural data. In contrast, the orientations of the glucose and galactose sugar moieties differ. To understand the consequences of these differences, we derived a model for the productive UGM-substrate complex that highlights interactions that can contribute to catalysis and substrate discrimination.

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.

An approach to the construction of tailor-made amphiphilic peptides that strongly and selectively bind to hairpin RNA targets

The hairpin RNA motif is one of the most frequently observed secondary structures and is often targeted by therapeutic agents. An amphiphilic peptide with seven lysine and eight leucine residues and its derivatives were designed for use as ligands against RNA hairpin motifs. We hypothesized that variations in both the hydrophobic leucine-rich and hydrophilic lysine-rich spheres of these amphiphilic peptides would create extra attractive interactions with hairpin RNA targets. A series of alanine-scanned peptides were probed to identify the most influential lysine residues in the hydrophilic sphere. The binding affinities of these modified peptides with several hairpins, such as RRE, TAR from HIV, a short hairpin from IRES of HCV, and a hairpin from the 16S A-site stem from rRNA, were determined. Since the hairpin from IRES of HCV was the most susceptible to the initial series of alanine-scanned peptides, studies investigating how further variations in the peptides effect binding employed the IRES hairpin. Next, the important Lys residues were substituted by shorter chain amines, such as ornithine, to place the peptide deeper into the hairpin groove. In a few cases, a 70-fold improved binding was observed for peptides that contained the specifically located shorter amine side chains. To further explore changes in binding affinities brought about by alterations in the hydrophobic sphere, tryptophan residues were introduced in place of leucine. A few peptides with tryptophan in specific positions also displayed 70-fold improved binding affinities. Finally, double mutant peptides incorporating both specifically located shorter amine side chains in the hydrophilic region and tryptophan residues in the hydrophobic region were synthesized. The binding affinities of peptides containing the simple double modification were observed to be 80 times lower, and their binding specificities were increased 40-fold. The results of this effort provide important information about strategies that can be used to prepare peptides that both strongly and selectively target hairpin RNAs. Specifically, the findings indicate that tailor-made amphiphilic peptide ligands against certain hairpin RNAs can be obtained if the RNA target possesses a deep groove in which both the hydrophobic and hydrophilic spheres of the peptide interact.

Investigation of atomic level patterns in protein--small ligand interactions

BACKGROUND

Shape complementarity and non-covalent interactions are believed to drive protein-ligand interaction. To date protein-protein, protein-DNA, and protein-RNA interactions were systematically investigated, which is in contrast to interactions with small ligands. We investigate the role of covalent and non-covalent bonds in protein-small ligand interactions using a comprehensive dataset of 2,320 complexes.

METHODOLOGY AND PRINCIPAL FINDINGS

We show that protein-ligand interactions are governed by different forces for different ligand types, i.e., protein-organic compound interactions are governed by hydrogen bonds, van der Waals contacts, and covalent bonds; protein-metal ion interactions are dominated by electrostatic force and coordination bonds; protein-anion interactions are established with electrostatic force, hydrogen bonds, and van der Waals contacts; and protein-inorganic cluster interactions are driven by coordination bonds. We extracted several frequently occurring atomic-level patterns concerning these interactions. For instance, 73% of investigated covalent bonds were summarized with just three patterns in which bonds are formed between thiol of Cys and carbon or sulfur atoms of ligands, and nitrogen of Lys and carbon of ligands. Similar patterns were found for the coordination bonds. Hydrogen bonds occur in 67% of protein-organic compound complexes and 66% of them are formed between NH- group of protein residues and oxygen atom of ligands. We quantify relative abundance of specific interaction types and discuss their characteristic features. The extracted protein-organic compound patterns are shown to complement and improve a geometric approach for prediction of binding sites.

CONCLUSIONS AND SIGNIFICANCE

We show that for a given type (group) of ligands and type of the interaction force, majority of protein-ligand interactions are repetitive and could be summarized with several simple atomic-level patterns. We summarize and analyze 10 frequently occurring interaction patterns that cover 56% of all considered complexes and we show a practical application for the patterns that concerns interactions with organic compounds.

Common physical basis of macromolecule-binding sites in proteins

Protein–DNA/RNA/protein interactions play critical roles in many biological functions. Previous studies have focused on the different features characterizing the different macromolecule-binding sites and approaches to detect these sites. However, no common unique signature of these sites had been reported. Thus, this work aims to provide a ‘common’ principle dictating the location of the different macromolecule-binding sites founded upon fundamental principles of binding thermodynamics. To achieve this aim, a comprehensive set of structurally nonhomologous DNA-, RNA-, obligate protein- and nonobligate protein-binding proteins, both free and bound to their respective macromolecules, was created and a novel strategy for detecting clusters of residues with electrostatic or steric strain given the protein structure was developed. The results show that regardless of the macromolecule type, the binding strength and conformational changes upon binding, macromolecule-binding sites are energetically less stable than nonmacromolecule-binding sites. They also reveal new energetic features distinguishing DNA- from RNA-binding sites and obligate protein- from nonobligate protein-binding sites in both free/bound protein structures.

[Trial to predict interactions between proteins and biomolecules based on their three-dimensional structures]

A vast amount of DNA sequence data, protein three-dimensional (3D) structure data, and RNA expression data have been produced by the efforts of genome sequencing, structural genomics, and omics projects, and we are at the stage where comprehensive views of cell activity and molecular mechanisms of life can be deduced. But in reality, we are inundated with massive amounts of data and are still in the process of finding ways to fully utilize the data. In this report, I would like to present our observations on the growth of protein 3D structure data and our effort to deduce the functions from the 3D structures. We found that the 3D structure of quite a high proportion of proteins derived from genome sequences can be now predicted and methods to predict the functions from 3D structures are in high demand. The methods we have developed can be used to predict some functions, namely RNA and ligand interfaces, based on those 3D structures and DNA sequences with relatively high accuracy. The methods enable predictions that are accurate enough to help with deducing the atomic structures of the 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.

Classifying RNA-binding proteins based on electrostatic properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs.

Gene expression in all living organisms is regulated by a complex set of events at both transcriptional and posttranscriptional levels. RNA-binding proteins play a key role in posttranscriptional events including splicing, stability, transport, and translation. Nowadays, there is increasing evidence that many other cellular processes may be mediated by RNA. Identifying new proteins involved in interaction with RNA is thus essential to unraveling the cellular processes in which these interactions are involved. In the current study we present a successful computational approach for classifying RNA-binding proteins and distinguishing them from other proteins based on structural and electrostatic properties. We test the method on a unique protein domain, the RNA recognition motif (RRM), which mediates both RNA and protein interactions. We show that we can discriminate RNA-binding RRMs from protein-binding RRMs. Further, we demonstrate that we can classify known RNA-binding proteins based on their RNA target (mRNA, rRNA, or tRNA). Our method does not rely on any kind of evolutionary information and thus can be applied to identify RNA-binding proteins with novel modes of RNA recognition.

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.

Evaluating conformational changes in protein structures binding RNA

Many protein-RNA recognition events are known to exhibit conformational changes from qualitative observations of individual complexes. However, a quantitative estimation of conformational changes is required if protein-RNA docking and template-based methods for RNA binding site prediction are to be developed. This study presents the first quantitative evaluation of conformational changes that occur when proteins bind RNA. The analysis of twelve RNA-binding proteins in the bound and unbound states using error-scaled difference distance matrices is presented. The binding site residues are mapped to each structure, and the conformational changes that affect these residues are evaluated. Of the twelve proteins four exhibit greater movements in nonbinding site residues, and a further four show the greatest movements in binding site residues. The remaining four proteins display no significant conformational change. When interface residues are found to be in conformationally variable regions of the protein they are typically seen to move less than 2 A between the bound and unbound conformations. The current data indicate that conformational changes in the binding site residues of RNA binding proteins may not be as significant as previously suggested, but a larger data set is required before wider conclusions may be drawn. The implications of the observed conformational changes for protein function prediction are discussed.

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.

Predicting RNA-binding sites from the protein structure based on electrostatics, evolution and geometry

An RNA-binding protein places a surface helix, β-ribbon, or loop in an RNA helix groove and/or uses a cavity to accommodate unstacked bases. Hence, our strategy for predicting RNA-binding residues is based on detecting a surface patch and a disparate cleft. These were generated and scored according to the gas-phase electrostatic energy change upon mutating each residue to Asp⁻/Glu⁻ and each residue's relative conservation. The method requires as input the protein structure and sufficient homologous sequences to define each residue's relative conservation. It yields as output a priority list of surface patch residues followed by a backup list of surface cleft residues distant from the patch residues for experimental testing of RNA binding. Among the 69 structurally non-homologous proteins tested, 81% possess a RNA-binding site with at least 70% of the maximum number of true positives in randomly generated patches of the same size as the predicted site; only two proteins did not contain any true RNA-binding residues in both predicted regions. Regardless of the protein conformational changes upon RNA-binding, the prediction accuracies based on the RNA-free/bound protein structures were found to be comparable and their binding sites overlapped as long as there are no disordered RNA-binding regions in the free structure that are ordered in the corresponding RNA-bound protein structure.