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.     

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.     

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-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.     

An ultra‐high affinity protein–protein interface displaying sequence‐robustness

Protein–protein interactions are crucial in biology and play roles in for example, the immune system, signaling pathways, and enzyme regulation. Ultra‐high affinity interactions (K _d <0.1 nM) occur in these systems, however, structures and energetics behind stability of ultra‐high affinity protein–protein complexes are not well understood. Regulation of the starch debranching barley limit dextrinase (LD) and its endogenous cereal type inhibitor (LDI) exemplifies an ultra‐high affinity complex (K _d of 42 pM). In this study the LD–LDI complex is investigated to unveil how robust the ultra‐high affinity is to LDI sequence variation at the protein–protein interface and whether alternative sequences can retain the ultra‐high binding affinity. The interface of LD–LDI was engineered using computational protein redesign aiming at identifying LDI variants predicted to retain ultra‐high binding affinity. These variants present a very diverse set of mutations going beyond conservative and alanine substitutions typically used to probe interfaces. Surface plasmon resonance analysis of the LDI variants revealed that high affinity of LD–LDI requires interactions of several residues at the rim of the protein interface, unlike the classical hotspot arrangement where key residues are found at the center of the interface. Notably, substitution of interface residues in LDI, including amino acids with functional groups different from the wild‐type, could occur without loss of affinity. This demonstrates that ultra‐high binding affinity can be conferred without hotspot residues, thus making complexes more robust to mutational drift in evolution. The present mutational analysis also demonstrates how energetic coupling can emerge between residues at large distances at the interface.     

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.     

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.     

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.     

Subunit interfaces of oligomeric hyperthermophilic enzymes display enhanced compactness

Enzymes from thermophilic and, particularly, from hyperthermophilic organisms are surprisingly stable. Understanding the molecular origin of protein thermostability and thermoactivity attracted the interest of many scientists both for the perspective comprehension of the principles of protein structure and for the possible biotechnological applications through protein engineering. Comparative studies at sequence and structure levels were aimed at detecting significant differences of structural parameters related to protein stability between thermophilic and hyperthermophilic proteins and their mesophilic homologs. In a recent work, we focused attention on structural adaptation occurring at the subunit interface of oligomeric hyper- and thermostable enzymes. A set of structural and chemico-physical parameters were compared to those observed at the corresponding interfaces of homologous mesophilic proteins. Among the most significant variations, a general increase of interface apolarity and packing density in hyperthermophilic enzymes were found. This work was therefore aimed at elucidating whether the increased packing observed is reached also through the reduction of interface cavity number and volume. The results indicate that number of cavities tends to be relatively constant while cavity volume tends to decrease in the hyperthermophilic interfaces. The cavity apolarity increases in thermophiles but, apparently, not in hyperthermophiles. Moreover, interface hot spot residues of the mesophilic interfaces tend to be conserved in the extremophilic counterparts.     

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.     

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.     

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.     

Crystal Structure of a Four-Layer Aggregate of Engineered TMV CP Implies the Importance of Terminal Residues for Oligomer Assembly

Background

Crystal structures of the tobacco mosaic virus (TMV) coat protein (CP) in its helical and disk conformations have previously been determined at the atomic level. For the helical structure, interactions of proteins and nucleic acids in the main chains were clearly observed; however, the conformation of residues at the C-terminus was flexible and disordered. For the four-layer aggregate disk structure, interactions of the main chain residues could only be observed through water–mediated hydrogen bonding with protein residues. In this study, the effects of the C-terminal peptides on the interactions of TMV CP were investigated by crystal structure determination.

Methodology/Principal Findings

The crystal structure of a genetically engineered TMV CP was resolved at 3.06 Å. For the genetically engineered TMV CP, a six-histidine (His) tag was introduced at the N-terminus, and the C-terminal residues 155 to 158 were truncated (N-His-TMV CP¹⁹). Overall, N-His-TMV CP¹⁹ protein self-assembled into the four-layer aggregate form. The conformations of residues Gln36, Thr59, Asp115 and Arg134 were carefully analyzed in the high radius and low radius regions of N-His-TMV CP¹⁹, which were found to be significantly different from those observed previously for the helical and four-layer aggregate forms. In addition, the aggregation of the N-His-TMV CP¹⁹ layers was found to primarily be mediated through direct hydrogen-bonding. Notably, this engineered protein also can package RNA effectively and assemble into an infectious virus particle.

Conclusion

The terminal sequence of amino acids influences the conformation and interactions of the four-layer aggregate. Direct protein–protein interactions are observed in the major overlap region when residues Gly155 to Thr158 at the C-terminus are truncated. This engineered TMV CP is reassembled by direct protein–protein interaction and maintains the normal function of the four-layer aggregate of TMV CP in the presence of RNA.     

MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming

Structural comparison of multiple-chain protein complexes is essential in many studies of protein–protein interactions. We develop a new algorithm, MM-align, for sequence-independent alignment of protein complex structures. The algorithm is built on a heuristic iteration of a modified Needleman–Wunsch dynamic programming (DP) algorithm, with the alignment score specified by the inter-complex residue distances. The multiple chains in each complex are first joined, in every possible order, and then simultaneously aligned with cross-chain alignments prevented. The alignments of interface residues are enhanced by an interface-specific weighting factor. MM-align is tested on a large-scale benchmark set of 205 × 3897 non-homologous multiple-chain complex pairs. Compared with a naïve extension of the monomer alignment program of TM-align, the alignment accuracy of MM-align is significantly higher as judged by the average TM-score of the physically-aligned residues. MM-align is about two times faster than TM-align because of omitting the cross-alignment zone of the DP matrix. It also shows that the enhanced alignment of the interfaces helps in identifying biologically relevant protein complex pairs.     

Recognition of Interaction Interface Residues in Low-Resolution Structures of Protein Assemblies Solely from the Positions of Cα Atoms

Background

The number of available structures of large multi-protein assemblies is quite small. Such structures provide phenomenal insights on the organization, mechanism of formation and functional properties of the assembly. Hence detailed analysis of such structures is highly rewarding. However, the common problem in such analyses is the low resolution of these structures. In the recent times a number of attempts that combine low resolution cryo-EM data with higher resolution structures determined using X-ray analysis or NMR or generated using comparative modeling have been reported. Even in such attempts the best result one arrives at is the very course idea about the assembly structure in terms of trace of the Cα atoms which are modeled with modest accuracy.

Methodology/Principal Findings

In this paper first we present an objective approach to identify potentially solvent exposed and buried residues solely from the position of Cα atoms and amino acid sequence using residue type-dependent thresholds for accessible surface areas of Cα. We extend the method further to recognize potential protein-protein interface residues.

Conclusion/ Significance

Our approach to identify buried and exposed residues solely from the positions of Cα atoms resulted in an accuracy of 84%, sensitivity of 83–89% and specificity of 67–94% while recognition of interfacial residues corresponded to an accuracy of 94%, sensitivity of 70–96% and specificity of 58–94%. Interestingly, detailed analysis of cases of mismatch between recognition of interface residues from Cα positions and all-atom models suggested that, recognition of interfacial residues using Cα atoms only correspond better with intuitive notion of what is an interfacial residue. Our method should be useful in the objective analysis of structures of protein assemblies when positions of only Cα positions are available as, for example, in the cases of integration of cryo-EM data and high resolution structures of the components of the assembly.     

Crystal Structure of the Complex between Pseudomonas Effector AvrPtoB and the Tomato Pto Kinase Reveals Both a Shared and a Unique Interface Compared with AvrPto-Pto

Resistance to bacterial speck disease in tomato (Solanum lycopersicum) is activated upon recognition by the host Pto kinase of either one of two sequence-unrelated effector proteins, AvrPto or AvrPtoB, from Pseudomonas syringae pv tomato (Pst). Pto induces Pst immunity by acting in concert with the Prf protein. The recently reported structure of the AvrPto-Pto complex revealed that interaction of AvrPto with Pto appears to relieve an inhibitory effect of Pto, allowing Pto to activate Prf. Here, we present the crystal structure of the Pto binding domain of AvrPtoB (residues 121 to 205) at a resolution of 1.9Å and of the AvrPtoB_121-205–Pto complex at a resolution of 3.3 Å. AvrPtoB_121-205 exhibits a tertiary fold that is completely different from that of AvrPto, and its conformation remains largely unchanged upon binding to Pto. In common with AvrPto-Pto, the AvrPtoB-Pto complex relies on two interfaces. One of these interfaces is similar in both complexes, although the primary amino acid sequences from the two effector proteins are very different. Amino acid substitutions in Pto at the other interface disrupt the interaction of AvrPtoB-Pto but not that of AvrPto-Pto. Interestingly, substitutions in Pto affecting this unique interface also cause Pto to induce Prf-dependent host cell death independently of either effector protein.     

Integration of New Genes into Cellular Networks,and Their Structural Maturation

It has been recently discovered that new genes can originate de novo from noncoding DNA, and several biological traits including expression or sequence composition form a continuum from noncoding sequences to conserved genes. In this article, using yeast genes I test whether the integration of new genes into cellular networks and their structural maturation shows such a continuum by analyzing their changes with gene age. I show that 1) The number of regulatory, protein–protein, and genetic interactions increases continuously with gene age, although with very different rates. New regulatory interactions emerge rapidly within a few million years, while the number of protein–protein and genetic interactions increases slowly, with a rate of 2–2.25 × 10⁻⁸/year and 4.8 × 10⁻⁸/year, respectively. 2) Gene essentiality evolves relatively quickly: the youngest essential genes appear in proto-genes ∼14 MY old. 3) In contrast to interactions, the secondary structure of proteins and their robustness to mutations indicate that new genes face a bottleneck in their evolution: proto-genes are characterized by high β-strand content, high aggregation propensity, and low robustness against mutations, while conserved genes are characterized by lower strand content and higher stability, most likely due to the higher probability of gene loss among young genes and accumulation of neutral mutations.     

Discarding functional residues from the substitution table improves predictions of active sites within three-dimensional structures

Substitutions of individual amino acids in proteins may be under very different evolutionary restraints depending on their structural and functional roles. The Environment Specific Substitution Table (ESST) describes the pattern of substitutions in terms of amino acid location within elements of secondary structure, solvent accessibility, and the existence of hydrogen bonds between side chains and neighbouring amino acid residues. Clearly amino acids that have very different local environments in their functional state compared to those in the protein analysed will give rise to inconsistencies in the calculation of amino acid substitution tables. Here, we describe how the calculation of ESSTs can be improved by discarding the functional residues from the calculation of substitution tables. Four categories of functions are examined in this study: protein–protein interactions, protein–nucleic acid interactions, protein–ligand interactions, and catalytic activity of enzymes. Their contributions to residue conservation are measured and investigated. We test our new ESSTs using the program CRESCENDO, designed to predict functional residues by exploiting knowledge of amino acid substitutions, and compare the benchmark results with proteins whose functions have been defined experimentally. The new methodology increases the Z-score by 98% at the active site residues and finds 16% more active sites compared with the old ESST. We also find that discarding amino acids responsible for protein–protein interactions helps in the prediction of those residues although they are not as conserved as the residues of active sites. Our methodology can make the substitution tables better reflect and describe the substitution patterns of amino acids that are under structural restraints only.     

Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Identification of a protein–protein interaction between KCNE1 and the activation gate machinery of KCNQ1

KCNQ1 channels assemble with KCNE1 transmembrane (TM) peptides to form voltage-gated K⁺ channel complexes with slow activation gate opening. The cytoplasmic C-terminal domain that abuts the KCNE1 TM segment has been implicated in regulating KCNQ1 gating, yet its interaction with KCNQ1 has not been described. Here, we identified a protein–protein interaction between the KCNE1 C-terminal domain and the KCNQ1 S6 activation gate and S4–S5 linker. Using cysteine cross-linking, we biochemically screened over 300 cysteine pairs in the KCNQ1–KCNE1 complex and identified three residues in KCNQ1 (H363C, P369C, and I257C) that formed disulfide bonds with cysteine residues in the KCNE1 C-terminal domain. Statistical analysis of cross-link efficiency showed that H363C preferentially reacted with KCNE1 residues H73C, S74C, and D76C, whereas P369C showed preference for only D76C. Electrophysiological investigation of the mutant K⁺ channel complexes revealed that the KCNQ1 residue, H363C, formed cross-links not only with KCNE1 subunits, but also with neighboring KCNQ1 subunits in the complex. Cross-link formation involving the H363C residue was state dependent, primarily occurring when the KCNQ1–KCNE1 complex was closed. Based on these biochemical and electrophysiological data, we generated a closed-state model of the KCNQ1–KCNE1 cytoplasmic region where these protein–protein interactions are poised to slow activation gate opening.