Anion–π interactions in complexes of proteins and halogen-containing amino acids

We analyzed the potential influence of anion–π interactions on the stability of complexes of proteins and halogen-containing non-natural amino acids. Anion–π interactions are distance and orientation dependent and our ab initio calculations showed that their energy can be lower than ?8 kcal mol^?1, while most of their interaction energies lie in the range from ?1 to ?4 kcal mol^?1. About 20 % of these interactions were found to be repulsive. We have observed that Tyr has the highest occurrence among the aromatic residues involved in anion–π interactions, while His made the least contribution. Furthermore, our study showed that 67 % of total interactions in the dataset are multiple anion–π interactions. Most of the amino acid residues involved in anion–π interactions tend to be buried in the solvent-excluded environment. The majority of the anion–π interacting residues are located in regions with helical secondary structure. Analysis of stabilization centers for these complexes showed that all of the six residues capable of anion–π interactions are important in locating one or more of such centers. We found that anion–π interacting residues are sometimes involved in simultaneous interactions with halogens as well. With all that in mind, we can conclude that the anion–π interactions can show significant influence on molecular organization and on the structural stability of the complexes of proteins and halogen-containing non-natural amino acids. Their influence should not be neglected in supramolecular chemistry and crystal engineering fields as well.     

Solvent effect on cation–π interactions with Al3+

Cation-π interactions are known to be one of the strongest noncovalent forces in the gas phase, but they rarely occur in a fully solvated environment. The present work used two different ab initio molecular dynamics-based approaches to describe the correlation between the strength of the cation-π interactions and the number of water molecules surrounding the cation. Five different complexes between an aluminum cation and different molecules containing aromatic rings were studied, and the degree of hydration of each complex was varied. Results indicated that cation-π interactions vanish when the aluminum cation is surrounded by more than three water molecules. The results also highlighted the influence of -OH ligands on the interaction strength.     

Protein–ligand interactions: Probing the energetics of a putative cation–π interaction

In order to probe the energetics associated with a putative cation–π interaction, thermodynamic parameters are determined for complex formation between the Grb2 SH2 domain and tripeptide derivatives of RCO–pTyr–Ac6c–Asn wherein the R group is varied to include different alkyl, cycloalkyl, and aryl groups. Although an indole ring is reputed to have the strongest interaction with a guanidinium ion, binding free energies, ΔG°, for derivatives of RCO–pTyr–Ac6c–Asn bearing cyclohexyl and phenyl groups were slightly more favorable than their indolyl analog. Crystallographic analysis of two complexes reveals that test ligands bind in similar poses with the notable exception of the relative orientation and proximity of the phenyl and indolyl rings relative to an arginine residue of the domain. These spatial orientations are consistent with those observed in other cation–π interactions, but there is no net energetic benefit to such an interaction in this biological system. Accordingly, although cation–π interactions are well documented as important noncovalent forces in molecular recognition, the energetics of such interactions may be mitigated by other nonbonded interactions and solvation effects in protein–ligand associations.     

Influence of cation–π interactions to the structural stability of prokaryotic and eukaryotic translation elongation factors

We have investigated the role of cation–π interactions on translation elongation factors. In our investigation, an average of four significant cation–π interactions were found, that is, an average of one cation–π interaction per 44 residues in the ten elongation factors were observed. The analysis on the influence of short (<±4), medium (>±4 to <±20) and long (>20) range contacts showed that cation–π interactions are mainly formed by medium and long-range contacts. Arg-Tyr pair was found largest in number but energetic contribution of Arg-Trp pair was found most. Preferred secondary structural conformation analysis of the residues involved in cation–π interaction indicates that the cationic Arg prefers to be in helix and Lys having equal probability for helix and strand, whereas the aromatic Phe and Trp were found mostly in helix while Tyr in strand regions. The cation–π interaction residues involved in these proteins were found highly conserved with 48.86% residues having conservation score of ≥6. Analysis of secondary structure preference of the energetically significant cation–π residues in different solvent accessible range indicates that most of the π residues are found buried or partially buried whereas cationic residues were found mostly at the protein surface. The results presented in this study will be useful for structural stability studies in translation elongation factors.     

Landscape of π–π and sugar–π contacts in DNA–protein interactions

There were 1765 contacts identified between DNA nucleobases or deoxyribose and cyclic (W, H, F, Y) or acyclic (R, E, D) amino acids in 672 X-ray structures of DNA–protein complexes. In this first study to compare π-interactions between the cyclic and acyclic amino acids, visual inspection was used to categorize amino acid interactions as nucleobase π–π (according to biological edge) or deoxyribose sugar–π (according to sugar edge). Overall, 54% of contacts are nucleobase π–π interactions, which involve all amino acids, but are more common for Y, F, and R, and involve all DNA nucleobases with similar frequencies. Among binding arrangements, cyclic amino acids prefer more planar (stacked) π-systems than the acyclic counterparts. Although sugar–π interactions were only previously identified with the cyclic amino acids and were found to be less common (38%) than nucleobase–cyclic amino acid contacts, sugar–π interactions are more common than nucleobase π–π contacts for the acyclic series (61% of contacts). Similar to DNA–protein π–π interactions, sugar–π contacts most frequently involve Y and R, although all amino acids adopt many binding orientations relative to deoxyribose. These DNA–protein π-interactions stabilize biological systems, by up to approximately ?40 kJ mol^?1 for neutral nucleobase or sugar–amino acid interactions, but up to approximately ?95 kJ mol^?1 for positively or negatively charged contacts. The high frequency and strength, despite variation in structure and composition, of these π-interactions point to an important function in biological systems.     

Aromatic-aromatic interactions: analysis of π-π interactions in interleukins and TNF proteins

Aromatic-aromatic hydrogen bonds are important in many areas of chemistry, biology and materials science. In this study we haveanalyzed the roles played by the π-π interactions in interleukins (ILs) and tumor necrosis factor (TNF) proteins. Majority of π-πinteracting residues are conserved in ILs and TNF proteins. The accessible surface area calculations in these proteins reveal thatthese interactions might be important in stabilizing the inner core regions of these proteins. In addition to π-π interactions, thearomatic residues also form π-networks in ILs and TNF proteins. The results obtained in the present study indicate that π-πinteractions and π-π networks play important roles in the structural stability of ILs and TNF proteins.     

Nature of cation-π interactions and their role in structural stability of immunoglobulin proteins

Cation-π interactions are known to be important contributors to protein stability and ligand-protein interactions. In this study, we have analyzed the influence of cation-π interactions in single chain immunoglobulin proteins. We observed 87 cation-π interactions in a data set of 33 proteins. These interactions are mainly formed by long-range contacts, and there is preference of Arg over Lys in these interactions. Arg-Tyr interactions are predominant among the various pairs analyzed. Despite the scarcity of interactions involving Trp, the average energy for Trp-cation interactions is quite high. This information suggests that the cation-π interactions involving Trp might be of high relevance to the proteins. Secondary structure analysis reveals that cation-π interactions are formed preferably between residues in which at least one is in β-strand. Proteins having β-strand regions have the highest number of cation-π interaction-forming residues.     

Investigations on the role of π–π interactions and π–π networks in eNOS and nNOS proteins

π–π Interactions play an important role in the stability of protein structures. In the present study, we have analyzed the influence of π–π interactions in eNOS and nNOS proteins. The contribution of these π–π interacting residues in sequential separation, secondary structure involvement, solvent accessibility and stabilization centers has been evaluated. π–π interactions stabilize the core regions within eNOS and nNOS proteins. π–π interacting residues are evolutionary conserved. There is a significant number of π–π interactions in spite of the lesser natural occurrences of π-residues in eNOS and nNOS proteins. In addition to π–π interactions, π residues also form π–π networks in both eNOS and nNOS proteins which might play an important role in the structural stability of these protein structures.     

Statistical mechanical treatment of α-helices and extended structures in proteins with inclusion of short- and medium-range interactions

A statistical mechanical model of protein conformation with medium-range interactions between theith and (i+k)^th residues (k<-4) is presented. Two two-state models, an α-helix-coil and an extended-structure-coil model, are formulated using the same form of the partition function, but the two models are applied independently to predict the locations of α-helical, extended, and coil segments; in the relatively few cases (<2%) where the predictions from the two models are in conflict, the prediction is scored as an incorrect one. Two independent sets of statistical weights (one set for each model) are derived to describe the interactions between the 20 amino acid residues for each range of interactionk; they are evaluated by minimizing an objective function so that the probability profiles for the α-helix or extended structure, respectively, in proteins computed from these statistical weights correlate optimally with the experimentally observed native conformations of these proteins. Examination of the resulting statistical weights shows that those for the interactions between hydrophobic residues and between a hydrophobic and a hydrophilic residue have reasonable magnitudes compared to what would be expected from the spatial arrangements of the side chains in the α-helix and the extended structure, and that those for the α-helix-coil model correlate well with experimentally determined values of the Zimm-Bragg parameterss and σ of the helix-coil transition theory. From the point of view of a method to predict the conformational states (i.e., α-helix, extended structure, and coil) of each residue, the statistical weights (as inall empirical prediction schemes) depend very much on the proteins used for the data base, since the presently available set of proteins of known structure is still too small for very high predictability; as a result, the correctness of the prediction is not very good for proteins not included in the data base. However, the correctness of the prediction, at least for the 37 proteins utilized as the data base in this study, is 91% and 87% for the α-helix-coil and the extended-structure-coil models, respectively; further, 79% of all the residues are predicted correctly when both the α-helix-coil and extended-structure-coil models are applied independently.     

The efficiency of different salts to screen charge interactions in proteins: a Hofmeister effect?

Understanding the screening by salts of charge-charge interactions in proteins is important for at least two reasons: a), screening by intracellular salt concentration may modulate the stability and interactions of proteins in vivo; and b), the in vitro experimental estimation of the contributions from charge-charge interactions to molecular processes involving proteins is generally carried out on the basis of the salt effect on process energetics, under the assumption that these interactions are screened out by moderate salt concentrations. Here, we explore experimentally the extent to which the screening efficiency depends on the nature of the salt. To this end, we have carried out an energetic characterization of the effect of NaCl (a nondenaturing salt), guanidinium chloride (a denaturing salt), and guanidinium thiocyanate (a stronger denaturant) on the stability of the wild-type form and a T14K variant of Escherichia coli thioredoxin. Our results suggest that the efficiency of different salts to screen charge-charge interactions correlates with their denaturing strength and with the position of the constituent ions in the Hofmeister rankings. This result appears consistent with the plausible relation of the Hofmeister rankings with the extent of solute accumulation/exclusion from protein surfaces.     

Investigations on unconventional hydrogen bonds in RNA binding proteins: The role of CH⋯OC interactions

We have investigated the roles played by CH⋯OC interactions in RNA binding proteins. There was an average of 78 CH⋯OC interactions per protein and also there was an average of one significant CH⋯OC interaction for every 6 residues in the 59 RNA binding proteins studied. Main chain–Main chain (MM) CH⋯OC interactions are the predominant type of interactions in RNA binding proteins. The donor atom contribution to CH⋯OC interactions was mainly from aliphatic residues. The acceptor atom contribution for MM CH⋯OC interactions was mainly from Val, Phe, Leu, Ile, Arg and Ala. The secondary structure preference analysis of CH⋯OC interacting residues showed that, Arg, Gln, Glu and Tyr preferred to be in helix, while Ala, Asp, Cys, Gly, Ile, Leu, Lys, Met, Phe, Trp and Val preferred to be in strand conformation. Most of the CH⋯OC interacting polar amino acid residues were solvent exposed while, majority of the CH⋯OC interacting non polar residues were excluded from the solvent. Long and medium-range CH⋯OC interactions are the predominant type of interactions in RNA binding proteins. More than 50% of CH⋯OC interacting residues had a higher conservation score. Significant percentage of CH⋯OC interacting residues had one or more stabilization centers. Sixty-six percent of the theoretically predicted stabilizing residues were also involved in CH⋯OC interactions and hence these residues may also contribute additional stability to RNA binding proteins.     

Aromatic side-chain interactions in proteins. Near- and far-sequence His–X pairs

Several studies have analysed aromatic interactions, involving mostly phenylalanine, tyrosine and tryptophan. Only a few studies have considered histidine as an interacting aromatic residue. An extensive analysis of aromatic His–X interactions is performed here on a data set of 593 PDB structures: 68% of the histidine are involved in aromatic pairs and 1271 non-redundant His–X pairs were analysed. Thirty percent of these pairs involve an aromatic partner less than 6 apart in the sequence. These near-sequence pairs correspond to conformations which stabilise secondary structures, mainly α-helices when the residues are 4 apart and β-strands when they are 2 apart in the sequence. The partners of the other His–X pairs (887, 70%) are more than 5 apart in the sequence. Of these far-sequence pairs, 35% bridge beta strands and only 9% helices. The near-sequence pairs are sterically constrained as supported by conformer distribution. The X partners of far-sequence His–X pairs are mainly “above” the histidine ring with tilted and normal rings, corresponding to a “T shape; face to edge” orientation. Phenylalanine, the only aromatic residue with no heteroatom, is a disfavoured partner, whereas histidine is the preferred one. Heteroatom–heteroatom interactions are favoured in near-sequence as well as in far-sequence His–His, His–Trp and His–Tyr pairs.     

NMR detection of side chain–side chain hydrogen bonding interactions in 13C/15N-labeled proteins

We describe the direct observation of side chain–side chain hydrogen bonding interactions in proteins with sensitivity-enhanced NMR spectroscopy. Specifically, the remote correlation between the guanidinium nitrogen ¹⁵N of arginine 71, which serves as the hydrogen donor, and the acceptor carboxylate carbon ¹³CO₂ of aspartate 100 in a 12 kDa protein, human FKBP12, is detected via the trans-hydrogen bond ^3h J _{N CO2} coupling by employing a novel HNCO-type experiment, soft CPD-HNCO. The ^3h J _{N CO2} coupling constant appears to be even smaller than the average value of backbone ^3h J _NC couplings, consistent with more extensive local dynamics in protein side chains. The identification of trans-hydrogen bond J-couplings between protein side chains should provide useful markers for monitoring hydrogen bonding interactions that contribute to the stability of protein folds, to alignments within enzyme active sites and to recognition events at macromolecular interfaces.     

Subtype-specific roles of phospholipase C-β via differential interactions with PDZ domain proteins

Since we first identified the PLC-β isozyme, enormous studies have been conducted to investigate the functional roles of this protein (Min et al., 1993; Suh et al.,1988). It is now well-known that the four PLC-β subtypes are major effector molecules in GPCR-mediated signaling, especially for intracellular Ca2+ signaling. Nonetheless, it is still poorly understood why multiple PLC-β subtype exist. Most cells express multiple subtypes of PLC-β in different combinations, and each subtype is involved in somewhat different signaling pathways. Therefore, studying the differential roles of each PLC-β subtype is a very interesting issue. In this regard, we focus here on PDZ domain proteins which are novel PLC-β interacting proteins. As scaffolders, PDZ domain proteins recruit various target proteins ranging from membrane receptors to cytoskeletal proteins to assemble highly organized signaling complexes; this can give rise to efficiency and diversity in cellular signaling. Because PLC-β subtypes have different PDZ-binding motifs, it is possible that they are engaged with different PDZ domain proteins, and in turn participate in distinct physiological responses. To date, several PDZ domain proteins, such as the NHERF family, Shank2, and Par-3, have been reported to selectively interact with certain PLC-β subtypes and GPCRs. Systematic predictions of potential binding partners also suggests differential binding properties between PLC-β subtypes. Furthermore, we elucidated parallel signaling processes for multiple PLC-β subtypes, which still perform distinct functions resulting from differential interactions with PDZ domain proteins within a single cell. Therefore, these results highlight the novel function of PDZ domain proteins as intermediaries in subtype-specific role of PLC-β in GPCR-mediated signaling. Future studies will focus on the physiological meanings of this signaling complex formation by different PDZ domain proteins and PLC-β subtypes. It has been observed for a long time that the expression of certain PLC-β subtype fluctuates during diverse physiological conditions. For example, the expression of PLC-β1 is selectively increased during myoblast and adipocyte differentiation (Faenza et al., 2004; O'Carroll et al., 2009). Likewise, PLC-β2 is highly up-regulated during breast cancer progression and plays a critical role in cell migration and mitosis (Bertagnolo et al., 2007). Although PLC-β3 is selectively down-regulated in neuroendocrine tumors, the expression of PLC-β1 is increased in small cell lung carcinoma (Stalberg et al., 2003; Strassheim et al., 2000). In our hypothetical model, it is most likely that up- and down regulation of certain PLC-β subtypes are due to their selective coupling with specific GPCR-mediated signaling, implicated in these pathophysiologic conditions. Therefore, better understanding of selective coupling between PLC-β subtypes, PDZ domain proteins, and GPCRs will shed light on new prognosis and therapy of diverse diseases, and provide potential targets for drug development.     

NMR relaxation parameters of methyl groups as a tool to map the interfaces of helix–helix interactions in membrane proteins

A number of recent advances in the field of magic-angle-spinning (MAS) solid-state NMR have enabled its application to a range of biological systems of ever increasing complexity. To retain biological relevance, these samples are increasingly studied in a hydrated state. At the same time, experimental feasibility requires the sample preparation process to attain a high sample concentration within the final MAS rotor. We discuss these considerations, and how they have led to a number of different approaches to MAS NMR sample preparation. We describe our experience of how custom-made (or commercially available) ultracentrifugal devices can facilitate a simple, fast and reliable sample preparation process. A number of groups have since adopted such tools, in some cases to prepare samples for sedimentation-style MAS NMR experiments. Here we argue for a more widespread adoption of their use for routine MAS NMR sample preparation.