Protein interactions within the SET1 complex and their roles in the regulation of histone 3 lysine 4 methylation

Set1 is the catalytic subunit and the central component of the evolutionarily conserved Set1 complex (Set1C) that methylates histone 3 lysine 4 (H3K4). Here we have determined protein/protein interactions within the complex and related the substructure to function. The loss of individual Set1C subunits differentially affects Set1 stability, complex integrity, global H3K4 methylation, and distribution of H3K4 methylation along active genes. The complex requires Set1, Swd1, and Swd3 for integrity, and Set1 amount is greatly reduced in the absence of the Swd1-Swd3 heterodimer. Bre2 and Sdc1 also form a heteromeric subunit, which requires the SET domain for interaction with the complex, and Sdc1 strongly interacts with itself. Inactivation of either Bre2 or Sdc1 has very similar effects. Neither is required for complex integrity, and their removal results in an increase of H3K4 mono- and dimethylation and a severe decrease of trimethylation at the 5′ end of active coding regions but a decrease of H3K4 dimethylation at the 3′ end of coding regions. Cells lacking Spp1 have a reduced amount of Set1 and retain a fraction of trimethylated H3K4, whereas cells lacking Shg1 show slightly elevated levels of both di- and trimethylation. Set1C associates with both serine 5- and serine 2-phosphorylated forms of polymerase II, indicating that the association persists to the 3′ end of transcribed genes. Taken together, our results suggest that Set1C subunits stimulate Set1 catalytic activity all along active genes.

Colloid probe AFM study of thermal collapse and protein interactions of poly(N-isopropylacrylamide) coatings

Graft coatings of poly(N-isopropylacrylamide) (pNIPAM) are of considerable interest for the reversible control of bio-interfacial interactions. In this study, graft coatings were prepared by free radical polymerisation from surface-bound polymerisable groups, on silicon wafers and quartz crystal microbalance (QCM) sensors. QCM with dissipation monitoring showed a gradual, extended phase change as the temperature increased. Colloid probe atomic force microscopy (CP-AFM) revealed a marked change in the compressibility of the coating below and above the lower critical solution temperature (LCST). Force curves showed an approximate 9-fold reduction in thickness between 24 °C and 38 °C, yet the collapsed coating at 38 °C still had a thickness significantly higher than the ellipsometrically determined dry thickness, indicating a residual extent of hydration above the LCST. At all temperatures, interaction force curves showed steric repulsion, though over different distances. There was little hysteresis between approach and retract force curves, which is evidence for almost instantaneous relaxation of the coating upon decompression. CP-AFM using a probe coated with bovine serum albumin (BSA) showed repulsive interactions with little approach/retraction hysteresis below the LCST, indicating lack of adhesion between the pNIPAM coating and the BSA-coated probe. In contrast, above the LCST the force curves on retraction were characteristic of adhesion, while the approach curves were still repulsive, and the adhesion increased in strength as the temperature was increased further beyond the LCST. Thus, QCM-D and CP-AFM data correlated well in showing a gradual nature of the phase transition and a concomitant gradual change in the interaction force with BSA.

Iteratively predict protein functions from protein-protein interactions

Current similarity-based approaches of predicting protein functions from protein-protein interaction (PPI) data usually make use of available information in the PPI network to predict functions of un-annotated proteins, and the prediction is a one-off procedure. However the interactions between proteins are more likely to be mutual rather than static and mono-directed. In other words, the un-annotated proteins, once their functions are predicted, will in turn affect the similarities between proteins. In this paper, we propose an innovative iteration algorithm that incorporates this dynamic feature of protein interaction into the protein function prediction, aiming to achieve higher prediction accuracies and get more reasonable results. With our algorithm, instead of one-off function predictions, functions are assigned to an unannotated protein iteratively until the functional similarities between proteins achieve a stable state. The experimental results show that our iterative method can provide better prediction results than one-off prediction methods with higher prediction accuracies, and is stable for large protein datasets.

Specificity and affinity motifs for Grb2 SH2-ligand interactions

Protein–protein interactions are often mediated by the recognition of short continuous amino acid stretches on target proteins by specific binding domains. Affinity-based selection strategies have successfully been used to define recognition motifs for a large series of such protein domains. However, in many biological systems specificity of interaction may be of equal or greater importance than affinity. To address this issue we have developed a peptide library screening technology that can be used to directly define ligands for protein domains based on both affinity and specificity of interaction. We demonstrate the value of this approach by the selection of peptide ligands that are either highly specific for the Grb2 Src homology 2 (SH2) domain or that are cross-reactive between a group of related SH2 domains. Examination of previously identified physiological ligands for the Grb2 SH2 domain suggests that for these ligands regulation of the specificity of interaction may be an important factor for in vivo ligand selection.

Interacting amino acid preferences of 3D pattern pairs at the binding sites of transient and obligate protein complexes

To assess the physico-chemical characteristics of protein-protein interactions, protein sequences and overall structural folds have been analyzed previously. To highlight this, discovery and examination of amino acid patterns at the binding sites defined by structural proximity in 3-dimensional (3D) space are essential. In this paper, we investigate the interacting preferences of 3D pattern pairs discovered separately in transient and obligate protein complexes. These 3D pattern pairs are not necessarily sequence-consecutive, but each residue in two groups of amino acids from two proteins in a complex is within certain °A threshold to most residues in the other group. We develop an algorithm called AA-pairs by which every pair of interacting proteins is represented as a bipartite graph, and it discovers all maximal quasi-bicliques from every bipartite graph to form our 3D pattern pairs. From 112 and 2533 highly conserved 3D pattern pairs discovered in the transient and obligate complexes respectively, we observe that Ala and Leu is the highest occuring amino acid in interacting 3D patterns of transient (20.91%) and obligate (33.82%) complexes respectively. From the study on the dipeptide composition on each side of interacting 3D pattern pairs, dipeptides Ala-Ala and Ala-Leu are popular in 3D patterns of both transient and obligate complexes. The interactions between amino acids with large hydrophobicity difference are present more in the transient than in the obligate complexes. On contrary, in obligate complexes, interactions between hydrophobic residues account for the top 5 most occuring amino acid pairings.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

A single PDZ domain protein interacts with the menkes copper ATPase, ATP7A : a new protein implicated in copper homeostasis

The homeostatic regulation of essential elements such as copper requires many proteins whose activities are often mediated and tightly coordinated through protein-protein interactions. This regulation ensures that cells receive enough copper without intracellular concentrations reaching toxic levels. To date, only a small number of proteins implicated in copper homeostasis have been identified, and little is known of the protein-protein interactions required for this process. To identify other proteins important for copper homeostasis, while also elucidating the protein-protein interactions that are integral to the process, we have utilized a known copper protein, the copper ATPase ATP7A, as a bait in a yeast two-hybrid screen of a human cDNA library to search for interacting partners. One of the ATP7A-interacting proteins identified is a novel protein with a single PDZ domain. This protein was recently identified to interact with the plasma membrane calcium ATPase b-splice variants. We propose a change in name for this protein from PISP (plasma membrane calcium ATPase-interacting single-PDZ protein) to AIPP1 (ATPase-interacting PDZ protein) and suggest that it represents the protein that interacts with the class I PDZ binding motif identified at the ATP7A C terminus. The interaction in mammalian cells was confirmed and an additional splice variant of AIPP1 was identified. This study represents an essential step forward in identifying the proteins and elucidating the network of protein-protein interactions involved in maintaining copper homeostasis and validates the use of the yeast two-hybrid approach for this purpose.

An iterative approach of protein function prediction

Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.

Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.

Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value.

Dynamically predicting protein functions from semantic associations of proteins

Predicting functions of un-annotated proteins is a significant challenge in the post-genomics era. Among existing computational approaches, exploiting interactions between proteins to predict functions of un-annotated proteins is widely used. However, it remains difficult to extract semantic associations between proteins (i.e. protein associations in terms of protein functionality) from protein interactions and incorporate extracted semantic associations to more effectively predict protein functions. Furthermore, existing approaches and algorithms regard the function prediction as a one-off procedure, ignoring dynamic and mutual associations between proteins. Therefore, deriving and exploiting semantic associations between proteins to dynamically predict functions are a promising and challenging approach for achieving better prediction results. In this paper, we propose an innovative algorithm to incorporate semantic associations between proteins into a dynamic procedure of protein function prediction. The semantic association between two proteins is measured by the semantic similarity of two proteins which is defined by the similarities of functions two proteins possess. To achieve better prediction results, function similarities are also incorporated into the prediction procedure. The algorithm dynamically predicts functions by iteratively selecting functions for the un-annotated protein and updating the similarities between the un-annotated protein and its neighbour annotated proteins until such suitable functions are selected that the similarities no longer change. The experimental results on real protein interaction datasets demonstrated that our method outperformed the similar and non-dynamic function prediction methods. Incorporating semantic associations between proteins into a dynamic procedure of function prediction reflects intrinsic relationships among proteins as well as dynamic features of protein interactions, and therefore, can significantly improve prediction results.

Beacon interacts with cdc2/cdc28-like kinases

Previously we found elevated beacon gene expression in the hypothalamus of obese Psammomys obesus. Beacon administration into the lateral ventricle of P. obesus stimulated food intake and body weight gain. In the current study we used yeast two-hybrid technology to screen for proteins in the human brain that interact with beacon. CLK4, an isoform of cdc2/cdc28-like kinase family of proteins, was identified as a strong interacting partner for beacon. Using active recombinant proteins and a surface plasmon resonance based detection technique, we demonstrated that the three members of this subfamily of kinases (CLK1, 2, and 4) all interact with beacon. Based on the known sequence and functional properties of beacon and CLKs, we speculate that beacon could either modulate the function of key regulatory molecules such as PTP1B or control the expression patterns of specific genes involved in the central regulation of energy metabolism.

Recruitment of class I hydrophobins to the air : water interface initiates a multi-step process of functional amyloid formation

Class I fungal hydrophobins form amphipathic monolayers composed of amyloid rodlets. This is a remarkable case of functional amyloid formation in that a hydrophobic:hydrophilic interface is required to trigger the self-assembly of the proteins. The mechanism of rodlet formation and the role of the interface in this process have not been well understood. Here, we have studied the effect of a range of additives, including ionic liquids, alcohols, and detergents, on rodlet formation by two class I hydrophobins, EAS and DewA. Although the conformation of the hydrophobins in these different solutions is not altered, we observe that the rate of rodlet formation is slowed as the surface tension of the solution is decreased, regardless of the nature of the additive. These results suggest that interface properties are of critical importance for the recruitment, alignment, and structural rearrangement of the amphipathic hydrophobin monomers. This work gives insight into the forces that drive macromolecular assembly of this unique family of proteins and allows us to propose a three-stage model for the interface-driven formation of rodlets.

Identification of a novel polymorphism in the 3'UTR of the L-arginine transporter gene SLC7A1 : contribution to hypertension and endothelial dysfunction

Background— Endothelial dysfunction because of reduced nitric oxide bioavailability is a key feature of essential hypertension. We have found that normotensive siblings of subjects with essential hypertension have impaired endothelial function accompanied by altered arginine metabolism.

Methods and Results— We have identified a novel C/T polymorphism in the 3′UTR of the principal arginine transporter, solute carrier family 7 (cationic amino acid transporter, y+ system), member 1 gene (SLC7A1). The minor T allele significantly attenuates reporter gene expression (P<0.01) and is impaired in its capacity to form DNA-protein complexes (P<0.05). In 278 hypertensive subjects the frequency of the T allele was 13.3% compared with 7.6% in 498 normotensive subjects (P<0.001). Moreover, the overall genotype distribution observed in hypertensives differed significantly from that in normotensives (P<0.001). To complement these studies, we generated an endothelial-specific transgenic mouse overexpressing l-arginine transporter SLC7A1. The Slc7A1 transgenic mice exhibited significantly enhanced responses to the endothelium-dependent vasodilator acetylcholine (−log EC50 for wild-type versus Slc7A1 transgenic: 6.87±0.10 versus 7.56±0.13; P<0.001). This was accompanied by elevated production of nitric oxide by isolated aortic endothelial cells.

Conclusions— The present study identifies a key, functionally active polymorphism in the 3′UTR of SLC7A1. As such, this polymorphism may account for the apparent link between altered endothelial function, l-arginine, and nitric oxide metabolism and predisposition to essential hypertension.

Cotranslational assembly of the yeast SET1C histone methyltransferase complex

 While probing the role of RNA for the function of SET1C/COMPASS histone methyltransferase, we identified SET1RC (SET1 mRNA-associated complex), a complex that contains SET1 mRNA and Set1, Swd1, Spp1 and Shg1, four of the eight polypeptides that constitute SET1C. Characterization of SET1RC showed that SET1 mRNA binding did not require associated Swd1, Spp1 and Shg1 proteins or RNA recognition motifs present in Set1. RNA binding was not observed when Set1 protein and SET1 mRNA were derived from independent genes or when SET1 transcripts were restricted to the nucleus. Importantly, the protein-RNA interaction was sensitive to EDTA, to the translation elongation inhibitor puromycin and to the inhibition of translation initiation in prt1-1 mutants. Taken together, our results support the idea that SET1 mRNA binding was dependent on translation and that SET1RC assembled on nascent Set1 in a cotranslational manner. Moreover, we show that cellular accumulation of Set1 is limited by the availability of certain SET1C components, such as Swd1 and Swd3, and suggest that cotranslational protein interactions may exert an effect in the protection of nascent Set1 from degradation.