Leucine Zipper-mediated Homo-oligomerization Regulates the Rho-GEF Activity of AKAP-Lbc

Molecular Modeling of Single- and Double-Hydrocarbon-Stapled Coiled-Coil Inhibitors against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias and a subset of acute lymphoblastic leukemias. As a result of the so-called Philadelphia chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase, which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown that conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation relative to traditional small-molecule therapeutics. Here, we iterate a new generation of CCmut3 inhibitors against Bcr-CC-mediated Bcr-Abl assembly designed to address these constraints through incorporation of all-hydrocarbon staples spanning i and i + 7 positions in α-helix 2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to evaluate single- and double-stapled CCmut3 candidates in silico for dynamics and binding energetics. We further model a truncated system characterized by the deletion of α-helix 1 and the flexible loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems devoid of the CPP, with a cyclized CPP, and with an open-configuration CPP, for a total of six systems that comprise our library. From this library, we present lead-stapled peptide candidates to be synthesized and evaluated experimentally as our next iteration of inhibitors against Bcr-Abl.

Evidence of direct interaction between cisplatin and the caspase-cleaved prostate apoptosis response-4 tumor suppressor

Prostate apoptosis response-4 (Par-4) tumor suppressor protein has gained attention as a potential therapeutic target owing to its unique ability to selectively induce apoptosis in cancer cells, sensitize them to chemotherapy and radiotherapy, and mitigate drug resistance. It has recently been reported that Par-4 interacts synergistically with cisplatin, a widely used anticancer drug. However, the mechanistic details underlying this relationship remain elusive. In this investigation, we employed an array of biophysical techniques, including circular dichroism spectroscopy, dynamic light scattering, and UV-vis absorption spectroscopy, to characterize the interaction between the active caspase-cleaved Par-4 (cl-Par-4) fragment and cisplatin. Additionally, elemental analysis was conducted to quantitatively assess the binding of cisplatin to the protein, utilizing inductively coupled plasma-optical emission spectroscopy and atomic absorption spectroscopy. Our findings provide evidence of direct interaction between cl-Par-4 and cisplatin, and reveal a binding stoichiometry of 1:1. This result provides insights that could be useful in enhancing the efficacy of cisplatin-based and tumor suppressor-based cancer therapies.

Computational Modeling of Stapled Coiled-Coil Inhibitors Against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias (CML) and a subset of acute lymphoblastic leukemias (ALL). As a result of the so-called Philadelphia Chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation, relative to traditional small molecule therapeutics. Here, we iterate a new generation of our inhibitor against Bcr-CC mediated Bcr-Abl assembly that is designed to address these constraints through incorporation of all-hydrocarbon staples spanning i, i + 7 positions in helix α2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to design and characterize single and double staple candidates in silico, evaluating binding energetics and building upon our seminal work modeling single hydrocarbon staples when applied to a truncated Bcr-CC sequence. This strategy enables us to efficiently build, characterize, and screen lead single/double stapled CPP-CCmut3-st candidates for experimental studies and validation in vitro and in vivo. In addition to full-length CPP-CCmut, we model a truncated system characterized by deletion of helix α1 and the flexible-loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems without CPP, with cyclized CPP, and with linear CPP, for a total of six systems which comprise our library. From this library, we present lead stapled peptide candidates to be synthesized and evaluated experimentally as our next-generation inhibitors against Bcr-Abl.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

BACKGROUND

Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information.

RESULTS

In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins.

CONCLUSIONS

Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

The tetrameric structure of Plasmodium falciparum phosphoglycerate mutase is critical for optimal enzymatic activity

The glycolytic enzyme phosphoglycerate mutase (PGM) is of utmost importance for overall cellular metabolism and has emerged as a novel therapeutic target in cancer cells. This enzyme is also conserved in the rapidly proliferating malarial parasite Plasmodium falciparum, which have a similar metabolic framework as cancer cells and rely on glycolysis as the sole energy-yielding process during intraerythrocytic development. There is no redundancy among the annotated PGM enzymes in Plasmodium, and PfPGM1 is absolutely required for the parasite survival as evidenced by conditional knockdown in our study. A detailed comparison of PfPGM1 with its counterparts followed by in-depth structure-function analysis revealed unique attributes of this parasitic protein. Here, we report for the first time the importance of oligomerization for the optimal functioning of the enzyme in vivo, as earlier studies in eukaryotes only focused on the effects in vitro. We show that single point mutation of the amino acid residue W68 led to complete loss of tetramerization and diminished catalytic activity in vitro. Additionally, ectopic expression of the WT PfPGM1 protein enhanced parasite growth, whereas the monomeric form of PfPGM1 failed to provide growth advantage. Furthermore, mutation of the evolutionarily conserved residue K100 led to a drastic reduction in enzymatic activity. The indispensable nature of this parasite enzyme highlights the potential of PfPGM1 as a therapeutic target against malaria, and targeting the interfacial residues critical for oligomerization can serve as a focal point for promising drug development strategies that may not be restricted to malaria only.

Oligomerization of THAP9 Transposase via Amino-Terminal Domains

Active DNA transposases like the Drosophila P element transposase (DmTNP) undergo oligomerization as a prerequisite for transposition. Human THAP9 (hTHAP9) is a catalytically active but functionally uncharacterized homologue of DmTNP. Here we report (using co-immunoprecipitation, pull down, colocalization, and proximity ligation assays) that both full length and truncated hTHAP9 (corresponding to amino-terminal DNA binding and predicted coiled coil domains) undergo homo-oligomerization, predominantly in the nuclei of HEK293T cells. Interestingly, the oligomerization is shown to be partially mediated by DNA. However, mutating the leucines (either individually or together) or deleting the predicted coiled coil region did not significantly affect oligomerization. Thus, we highlight the importance of DNA and the amino-terminal regions of hTHAP9 for their ability to form higher-order oligomeric states. We also report that Hcf-1, THAP1, THAP10, and THAP11 are possible protein interaction partners of hTHAP9. Elucidating the functional relevance of the different putative oligomeric state(s) of hTHAP9 would help answer questions about its interaction partners as well as its unknown physiological roles.

Quantifying the distribution of protein oligomerization degree reflects cellular information capacity

The generation of information, energy and biomass in living cells involves integrated processes that optimally evolve into complex and robust cellular networks. Protein homo-oligomerization, which is correlated with cooperativity in biology, is one means of scaling the complexity of protein networks. It can play critical roles in determining the sensitivity of genetic regulatory circuits and metabolic pathways. Therefore, understanding the roles of oligomerization may lead to new approaches of probing biological functions. Here, we analyzed the frequency of protein oligomerization degree in the cell proteome of nine different organisms, and then, we asked whether there are design trade-offs between protein oligomerization, information precision and energy costs of protein synthesis. Our results indicate that there is an upper limit for the degree of protein oligomerization, possibly because of the trade-off between cellular resource limitations and the information precision involved in biochemical reaction networks. These findings can explain the principles of cellular architecture design and provide a quantitative tool to scale synthetic biological systems.

Novel functional characterization of the insecticidal protein Vip3Aa on DNA binding activity

The toxicity of Vip3Aa protein on insect pests is known, however, it remains unclear underlying the structure-dependent molecular function of the Vip3Aa protein. To investigate the novel function of the Vip3Aa protein, we isolated recombinant Vip3Aa protein. The recombinant Vip3Aa protein was mostly present as oligomeric form depending on the hydrophobic amino acid residue. We found that the oligomeric Vip3Aa protein specifically binds to nucleic acids, including single-stranded (ssDNA) and double-stranded DNA (dsDNA). The conformational and functional domains of the Vip3Aa protein were confirmed by separating the Vip3Aa full and Vip3Aa active (actVip3Aa) forms using size exclusion chromatography and nucleic acid binding activity. Interestingly, actVip3Aa protein had a conformational change and decreased DNA binding activity compared to that of the Vip3Aa full, suggesting that N-terminal part of the Vip3Aa play an important role in maintaining the conformation and nucleic acid binding activity. These studies highlight novel functional characterization of the insecticidal protein Vip3Aa on DNA binding activity and may be attributed to the protection of DNA from the damage caused by oxidative stress.

Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information

Self-interacting proteins (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the article, we present a novel computational method called RRN-SIFT, which combines the recurrent neural network (RNN) with scale invariant feature transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it uses SIFT for extracting key feature by exploring the evolutionary information embedded in Position-Specific Iterated BLAST-constructed position-specific scoring matrix and employs an RNN classifier to perform classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on the yeast and human dataset, respectively. We also compared our performance with the back propagation neural network (BPNN), the state-of-the-art support vector machine (SVM), and other existing methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than that of the BPNN, SVM, and other previous methods in the domain. Therefore, we conclude that the proposed RNN-SIFT model is a useful tool for predicting SIPs, as well to solve other bioinformatics tasks. To facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed at http://219.219.62.123:8888/RNNSIFT/ including the source code and the SIP datasets.

Assessing Self-interaction of Mammalian Nuclear Proteins by Co-immunoprecipitation

Protein-protein interactions constitute the molecular foundations of virtually all biological processes. Co-immunoprecipitation (CoIP) experiments are probably the most widely used method to probe both heterotypic and homotypic protein-protein interactions. Recent advances in super-resolution microscopy have revealed that several nuclear proteins such as transcription factors are spatially distributed into local high-concentration clusters in mammalian cells, suggesting that many nuclear proteins self-interact. These observations have further underscored the need for orthogonal biochemical approaches for testing if self-association occurs, and if so, what the mechanisms are. Here, we describe a CoIP protocol specifically optimized to test self-association of endogenously tagged nuclear proteins (self-CoIP), and to evaluate the role of nucleic acids in such self-interaction. This protocol has proven reliable and robust in our hands, and it can be used to test both homotypic and heterotypic (CoIP) protein-protein interactions.

Stability Analysis of a Bulk-Surface Reaction Model for Membrane Protein Clustering

Protein aggregation on the plasma membrane (PM) is of critical importance to many cellular processes such as cell adhesion, endocytosis, fibrillar conformation, and vesicle transport. Lateral diffusion of protein aggregates or clusters on the surface of the PM plays an important role in governing their heterogeneous surface distribution. However, the stability behavior of the surface distribution of protein aggregates remains poorly understood. Therefore, understanding the spatial patterns that can emerge on the PM solely through protein-protein interaction, lateral diffusion, and feedback is an important step toward a complete description of the mechanisms behind protein clustering on the cell surface. In this work, we investigate the pattern formation of a reaction-diffusion model that describes the dynamics of a system of ligand-receptor complexes. The purely diffusive ligand in the cytosol can bind receptors in the PM and the resultant ligand-receptor complexes not only diffuse laterally but can also form clusters resulting in different oligomers. Finally, the largest oligomers recruit ligands from the cytosol using positive feedback. From a methodological viewpoint, we provide theoretical estimates for diffusion-driven instabilities of the protein aggregates based on the Turing mechanism. Our main result is a threshold phenomenon, in which a sufficiently high recruitment of ligands promotes the input of new monomeric components and consequently drives the formation of a single-patch spatially heterogeneous steady state.

Computational Models for Self-Interacting Proteins Prediction

Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to understand its functions. Previous studies on SIPs have focused on their structures and functions, while their whole properties are less emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical research, which will help to understanding the function and mechanism of proteins. It is worth noting that high throughput methods can be used for SIPs prediction, but can be costly, time consuming and challenging. Therefore, it is urgent to design computational models for the identification of SIPs. In this review, the concept and function of SIPs were introduced in detail. We further introduced SIPs data and some excellent computational models that have been designed for SIPs prediction. Specially, the most existing approaches were developed based on machine learning through carrying out different extract feature methods. Finally, we discussed several difficult problems in developing computational models for SIPs prediction.

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.

Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

Background:

Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell.

Objective:

In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation.

Method:

The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. </P><P> Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods.

Conclusion:

It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

Prediction of protein self-interactions using stacked long short-term memory from protein sequences information

BACKGROUND

Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental methods are labor-intensive, time-consuming and costly and can only yield limited results in real-world needs. Hence,it's urgent to develop an efficient computational SIPs prediction method to fill the gap. Deep learning technologies have proven to produce subversive performance improvements in many areas, but the effectiveness of deep learning methods for SIPs prediction has not been verified.

RESULTS

We developed a deep learning model for predicting SIPs by constructing a Stacked Long Short-Term Memory (SLSTM) neural network that contains "dropout". We extracted features from protein sequences using a novel feature extraction scheme that combined Zernike Moments (ZMs) with Position Specific Weight Matrix (PSWM). The capability of the proposed approach was assessed on S.erevisiae and Human SIPs datasets. The result indicates that the approach based on deep learning can effectively resist data skew and achieve good accuracies of 95.69 and 97.88%, respectively. To demonstrate the progressiveness of deep learning, we compared the results of the SLSTM-based method and the celebrated Support Vector Machine (SVM) method and several other well-known methods on the same datasets.

CONCLUSION

The results show that our method is overall superior to any of the other existing state-of-the-art techniques. As far as we know, this study first applies deep learning method to predict SIPs, and practical experimental results reveal its potential in SIPs identification.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB.

Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information

Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach for SIPs prediction. In this study, we propose a novel computational method for predicting SIPs from protein amino acids sequence. Firstly, a novel feature representation scheme based on Local Binary Pattern (LBP) is developed, in which the evolutionary information, in the form of multiple sequence alignments, is taken into account. Then, by employing the Relevance Vector Machine (RVM) classifier, the performance of our proposed method is evaluated on yeast and human datasets using a five-fold cross-validation test. The experimental results show that the proposed method can achieve high accuracies of 94.82% and 97.28% on yeast and human datasets, respectively. For further assessing the performance of our method, we compared it with the state-of-the-art Support Vector Machine (SVM) classifier, and other existing methods, on the same datasets. Comparison results demonstrate that the proposed method is very promising and could provide a cost-effective alternative for predicting SIPs. In addition, to facilitate extensive studies for future proteomics research, a web server is freely available for academic use at .

Characterization of Multi-subunit Protein Complexes of Human MxA Using Non-denaturing Polyacrylamide Gel-electrophoresis

The formation of oligomeric complexes is a crucial prerequisite for the proper structure and function of many proteins. The interferon-induced antiviral effector protein MxA exerts a broad antiviral activity against many viruses. MxA is a dynamin-like GTPase and has the capacity to form oligomeric structures of higher order. However, whether oligomerization of MxA is required for its antiviral activity is an issue of debate. We describe here a simple protocol to assess the oligomeric state of endogenously or ectopically expressed MxA in the cytoplasmic fraction of human cell lines by non-denaturing polyacrylamide gel electrophoresis (PAGE) in combination with Western blot analysis. A critical step of the protocol is the choice of detergents to prevent aggregation and/or precipitation of proteins particularly associated with cellular membranes such as MxA, without interfering with its enzymatic activity. Another crucial aspect of the protocol is the irreversible protection of the free thiol groups of cysteine residues by iodoacetamide to prevent artificial interactions of the protein. This protocol is suitable for a simple assessment of the oligomeric state of MxA and furthermore allows a direct correlation of the antiviral activity of MxA interface mutants with their respective oligomeric states.

Small-Molecule Protein-Protein Interaction Inhibitor of Oncogenic Rho Signaling

Uncontrolled activation of Rho signaling by RhoGEFs, in particular AKAP13 (Lbc) and its close homologs, is implicated in a number of human tumors with poor prognosis and resistance to therapy. Structure predictions and alanine scanning mutagenesis of Lbc identified a circumscribed hot region for RhoA recognition and activation. Virtual screening targeting that region led to the discovery of an inhibitor of Lbc-RhoA interaction inside cells. By interacting with the DH domain, the compound inhibits the catalytic activity of Lbc, halts cellular responses to activation of oncogenic Lbc pathways, and reverses a number of prostate cancer cell phenotypes such as proliferation, migration, and invasiveness. This study provides insights into the structural determinants of Lbc-RhoA recognition. This is a successful example of structure-based discovery of a small protein-protein interaction inhibitor able to halt oncogenic Rho signaling in cancer cells with therapeutic implications.

Regulating Rho GTPases and their regulators

Rho GTPases regulate cytoskeletal and cell adhesion dynamics and thereby coordinate a wide range of cellular processes, including cell migration, cell polarity and cell cycle progression. Most Rho GTPases cycle between a GTP-bound active conformation and a GDP-bound inactive conformation to regulate their ability to activate effector proteins and to elicit cellular responses. However, it has become apparent that Rho GTPases are regulated by post-translational modifications and the formation of specific protein complexes, in addition to GTP-GDP cycling. The canonical regulators of Rho GTPases - guanine nucleotide exchange factors, GTPase-activating proteins and guanine nucleotide dissociation inhibitors - are regulated similarly, creating a complex network of interactions to determine the precise spatiotemporal activation of Rho GTPases.

SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information

Protein self-interaction, i.e. the interaction between two or more identical proteins expressed by one gene, plays an important role in the regulation of cellular functions. Considering the limitations of experimental self-interaction identification, it is necessary to design specific bioinformatics tools for self-interacting protein (SIP) prediction from protein sequence information. In this study, we proposed an improved computational approach for SIP prediction, termed SPAR (Self-interacting Protein Analysis serveR). Firstly, we developed an improved encoding scheme named critical residues substitution (CRS), in which the fine-grained domain-domain interaction information was taken into account. Then, by employing the Random Forest algorithm, the performance of CRS was evaluated and compared with several other encoding schemes commonly used for sequence-based protein-protein interaction prediction. Through the tenfold cross-validation tests on a balanced training dataset, CRS performed the best, with the average accuracy up to 72.01 %. We further integrated CRS with other encoding schemes and identified the most important features using the mRMR (the minimum redundancy maximum relevance) feature selection method. Our SPAR model with selected features achieved an average accuracy of 92.09 % on the human-independent test set (the ratio of positives to negatives was about 1:11). Besides, we also evaluated the performance of SPAR on an independent yeast test set (the ratio of positives to negatives was about 1:8) and obtained an average accuracy of 76.96 %. The results demonstrate that SPAR is capable of achieving a reasonable performance in cross-species application. The SPAR server is freely available for academic use at http://systbio.cau.edu.cn/zzdlab/spar/ .

Analysis of AKAP7γ Dimerization

A-kinase anchoring proteins (AKAPs) constitute a family of scaffolding proteins that contribute to spatiotemporal regulation of PKA-mediated phosphorylation events. In particular, AKAP7 is a family of alternatively spliced proteins that participates in cardiac calcium dynamics. Here, we demonstrate via pull-down from transfected cells and by direct protein-protein association that AKAP7γ self-associates. Self-association appears to be an isoform specific phenomenon, as AKAP7α did not associate with itself or with AKAP7γ. However, AKAP7γ did associate with AKAP7δ, suggesting the long isoforms of the AKAP can form heterodimers. Surface plasmon resonance found that the AKAP7γ self-association occurs via two high affinity binding sites with K D values in the low nanomolar range. Mapping of the binding sites by peptide array reveals that AKAP7γ interacts with itself through multiple regions. Photon counting histogram analysis (PCH) of AKAP7γ-EGFP expressed in HEK-293 cells confirmed that AKAP7γ-EGFP self-associates in a cellular context. Lastly, computational modeling of PKA dynamics within AKAP7γ complexes suggests that oligomerization may augment phosphorylation of scaffolded PKA substrates. In conclusion, our study reveals that AKAP7γ forms both homo- and heterodimers with the long isoforms of the AKAP and that this phenomenon could be an important step in mediating effective substrate phosphorylation in cellular microdomains.

Gastrin-stimulated Gα13 Activation of Rgnef Protein (ArhGEF28) in DLD-1 Colon Carcinoma Cells

The guanine nucleotide exchange factor Rgnef (also known as ArhGEF28 or p190RhoGEF) promotes colon carcinoma cell motility and tumor progression via interaction with focal adhesion kinase (FAK). Mechanisms of Rgnef activation downstream of integrin or G protein-coupled receptors remain undefined. In the absence of a recognized G protein signaling homology domain in Rgnef, no proximal linkage to G proteins was known. Utilizing multiple methods, we have identified Rgnef as a new effector for Gα13 downstream of gastrin and the type 2 cholecystokinin receptor. In DLD-1 colon carcinoma cells depleted of Gα13, gastrin-induced FAK Tyr(P)-397 and paxillin Tyr(P)-31 phosphorylation were reduced. RhoA GTP binding and promoter activity were increased by Rgnef in combination with active Gα13. Rgnef co-immunoprecipitated with activated Gα13Q226L but not Gα12Q229L. The Rgnef C-terminal (CT, 1279-1582) region was sufficient for co-immunoprecipitation, and Rgnef-CT exogenous expression prevented Gα13-stimulated SRE activity. A domain at the C terminus of the protein close to the FAK binding domain is necessary to bind to Gα13. Point mutations of Rgnef-CT residues disrupt association with active Gα13 but not Gαq. These results show that Rgnef functions as an effector of Gα13 signaling and that this linkage may mediate FAK activation in DLD-1 colon carcinoma cells.

A-kinase anchoring proteins: cAMP compartmentalization in neurodegenerative and obstructive pulmonary diseases

The universal second messenger cAMP is generated upon stimulation of Gs protein-coupled receptors, such as the β2 -adreneoceptor, and leads to the activation of PKA, the major cAMP effector protein. PKA oscillates between an on and off state and thereby regulates a plethora of distinct biological responses. The broad activation pattern of PKA and its contribution to several distinct cellular functions lead to the introduction of the concept of compartmentalization of cAMP. A-kinase anchoring proteins (AKAPs) are of central importance due to their unique ability to directly and/or indirectly interact with proteins that either determine the cellular content of cAMP, such as β2 -adrenoceptors, ACs and PDEs, or are regulated by cAMP such as the exchange protein directly activated by cAMP. We report on lessons learned from neurons indicating that maintenance of cAMP compartmentalization by AKAP5 is linked to neurotransmission, learning and memory. Disturbance of cAMP compartments seem to be linked to neurodegenerative disease including Alzheimer's disease. We translate this knowledge to compartmentalized cAMP signalling in the lung. Next to AKAP5, we focus here on AKAP12 and Ezrin (AKAP78). These topics will be highlighted in the context of the development of novel pharmacological interventions to tackle AKAP-dependent compartmentalization.

Correlation of transducin photoaffinity labeling with the specific formation of intermolecular disulfide linkages in its α-subunit

Transducin (T) is a heterotrimer of Tα, Tβ, and Tγ subunits. In the presence of light-activated rhodopsin, 8-azidoguanosine triphosphate (8-N3GTP) was covalently incorporated into T in a UV-light photodependent manner, with a low stoichiometry of 0.02 mol of 8-N3GTP per mol of T. Although Tα was preferentially labeled by 8-N3GTP, Tβ and Tγ were also modified. Photolabeling of T was specifically inhibited by GDP and GTP, but not by β,γ-imido-guanosine 5'-triphosphate (GMP-PNP), indicating that 8-N3GTP was modifying the GDP binding site of the holoenzyme. This was consistent with the observation that the photoaffinity probe was completely hydrolyzed to 8-N3GDP by T activated by illuminated rhodopsin. The formation of intermolecular disulfide associations in T was also determined because photolabeling of T was performed under non-reducing conditions. We established that Cys-347 of Tα was the major residue involved in the formation of disulfide-linked T oligomers. Other cysteines of Tα, such as Cys-321, also participated in the formation of disulfide bonds, revealing a complex pattern of intermolecular disulfide cross-links that led to the polymerization of T. The spontaneous generation of these cystines in Tα inhibited the light-dependent GTPase and GMP-PNP binding activities of T. A model was constructed illustrating that when two heterotrimers dimerize through the formation of disulfide bridges between the Cys-347 of their Tα subunits, the guanine ring of the 8-N3GDP bound to one T molecule might approach to the Tβγ-complex of the other heterotrimer. This model provides an explanation for the additional photolabeling of Tβ and Tγ by 8-N3GTP.

Post-translational regulation of rice MADS29 function: homodimerization or binary interactions with other seed-expressed MADS proteins modulate its translocation into the nucleus

OsMADS29 is a seed-specific MADS-box transcription factor that affects embryo development and grain filling by maintaining hormone homeostasis and degradation of cells in the nucellus and nucellar projection. Although it has a bipartite nuclear localization signal (NLS) sequence, the transiently expressed OsMADS29 monomer does not localize specifically in the nucleus. Dimerization of the monomers alters the intracellular localization fate of the resulting OsMADS29 homodimer, which then translocates into the nucleus. By generating domain-specific deletions/mutations, we show that two conserved amino acids (lysine(23) and arginine(24)) in the NLS are important for nuclear localization of the OsMADS29 homodimer. Furthermore, the analyses involving interaction of OsMADS29 with 30 seed-expressed rice MADS proteins revealed 19 more MADS-box proteins, including five E-class proteins, which interacted with OsMADS29. Eleven of these complexes were observed to be localized in the nucleus. Deletion analysis revealed that the KC region (K-box and C-terminal domain) plays a pivotal role in homodimerization. These data suggest that the biological function of OsMADS29 may not only be regulated at the level of transcription and translation as reported earlier, but also at the post-translational level by way of the interaction between OsMADS29 monomers, and between OsMADS29 and other MADS-box proteins.

Structural insights into the activation of the RhoA GTPase by the lymphoid blast crisis (Lbc) oncoprotein

The small GTPase RhoA promotes deregulated signaling upon interaction with lymphoid blast crisis (Lbc), the oncogenic form of A-kinase anchoring protein 13 (AKAP13). The onco-Lbc protein is a hyperactive Rho-specific guanine nucleotide exchange factor (GEF), but its structural mechanism has not been reported despite its involvement in cardiac hypertrophy and cancer causation. The pleckstrin homology (PH) domain of Lbc is located at the C-terminal end of the protein and is shown here to specifically recognize activated RhoA rather than lipids. The isolated dbl homology (DH) domain can function as an independent activator with an enhanced activity. However, the DH domain normally does not act as a solitary Lbc interface with RhoA-GDP. Instead it is negatively controlled by the PH domain. In particular, the DH helical bundle is coupled to the structurally dependent PH domain through a helical linker, which reduces its activity. Together the two domains form a rigid scaffold in solution as evidenced by small angle x-ray scattering and ¹H,¹³C,¹⁵N-based NMR spectroscopy. The two domains assume a “chair” shape with its back possessing independent GEF activity and the PH domain providing a broad seat for RhoA-GTP docking rather than membrane recognition. This provides structural and dynamical insights into how DH and PH domains work together in solution to support regulated RhoA activity. Mutational analysis supports the bifunctional PH domain mediation of DH-RhoA interactions and explains why the tandem domain is required for controlled GEF signaling.

The C-terminus of the long AKAP13 isoform (AKAP-Lbc) is critical for development of compensatory cardiac hypertrophy

The objective of this study was to determine the role of A-Kinase Anchoring Protein (AKAP)-Lbc in the development of heart failure, by investigating AKAP-Lbc-protein kinase D1 (PKD1) signaling in vivo in cardiac hypertrophy. Using a gene-trap mouse expressing a truncated version of AKAP-Lbc (due to disruption of the endogenous AKAP-Lbc gene), that abolishes PKD1 interaction with AKAP-Lbc (AKAP-Lbc-ΔPKD), we studied two mouse models of pathological hypertrophy: i) angiotensin (AT-II) and phenylephrine (PE) infusion and ii) transverse aortic constriction (TAC)-induced pressure overload. Our results indicate that AKAP-Lbc-ΔPKD mice exhibit an accelerated progression to cardiac dysfunction in response to AT-II/PE treatment and TAC. AKAP-Lbc-ΔPKD mice display attenuated compensatory cardiac hypertrophy, increased collagen deposition and apoptosis, compared to wild-type (WT) control littermates. Mechanistically, reduced levels of PKD1 activation are observed in AKAP-Lbc-ΔPKD mice compared to WT mice, resulting in diminished phosphorylation of histone deacetylase 5 (HDAC5) and decreased hypertrophic gene expression. This is consistent with a reduced compensatory hypertrophy phenotype leading to progression of heart failure in AKAP-Lbc-ΔPKD mice. Overall, our data demonstrates a critical in vivo role for AKAP-Lbc-PKD1 signaling in the development of compensatory hypertrophy to enhance cardiac performance in response to TAC-induced pressure overload and neurohumoral stimulation by AT-II/PE treatment.

A-kinase anchoring protein-Lbc promotes pro-fibrotic signaling in cardiac fibroblasts

In response to stress or injury the heart undergoes an adverse remodeling process associated with cardiomyocyte hypertrophy and fibrosis. Transformation of cardiac fibroblasts to myofibroblasts is a crucial event initiating the fibrotic process. Cardiac myofibroblasts invade the myocardium and secrete excess amounts of extracellular matrix proteins, which cause myocardial stiffening, cardiac dysfunctions and progression to heart failure. While several studies indicate that the small GTPase RhoA can promote profibrotic responses, the exchange factors that modulate its activity in cardiac fibroblasts are yet to be identified. In the present study, we show that AKAP-Lbc, an A-kinase anchoring protein (AKAP) with an intrinsic Rho-specific guanine nucleotide exchange factor (GEF) activity, is critical for activating RhoA and transducing profibrotic signals downstream of type I angiotensin II receptors (AT1Rs) in cardiac fibroblasts. In particular, our results indicate that suppression of AKAP-Lbc expression by infecting adult rat ventricular fibroblasts with lentiviruses encoding AKAP-Lbc specific short hairpin (sh) RNAs strongly reduces the ability of angiotensin II to promote RhoA activation, differentiation of cardiac fibroblasts to myofibroblasts, collagen deposition as well as myofibroblast migration. Interestingly, AT1Rs promote AKAP-Lbc activation via a pathway that requires the α subunit of the heterotrimeric G protein G12. These findings identify AKAP-Lbc as a key Rho-guanine nucleotide exchange factor modulating profibrotic responses in cardiac fibroblasts.

G12/13-dependent signaling of G-protein-coupled receptors: disease context and impact on drug discovery

G-protein-coupled receptors (GPCRs) transmit extracellular signals across the plasma membrane via intracellular activation of heterotrimeric G proteins. The signal transduction pathways of Gs, Gi and Gq protein families are widely studied, whereas signaling properties of G12 proteins are only emerging. Many GPCRs were found to couple to G12/13 proteins in addition to coupling to one or more other types of G proteins. G12/13 proteins couple GPCRs to activation of the small monomeric GTPase RhoA. Activation of RhoA modulates various downstream effector systems relevant to diseases such as hypertension, artherosclerosis, asthma and cancer. GPCR screening assays exist for Gs-, Gi- and Gq-linked pathways, whereas a drug-screening assay for the G12-Rho pathway was developed only recently. The review gives an overview of the present understanding of the G12/13-related biology of GPCRs.

CaV1.2 signaling complexes in the heart

L-type Ca(2+) channels (LTCCs) are essential for generation of the electrical and mechanical properties of cardiac muscle. Furthermore, regulation of LTCC activity plays a central role in mediating the effects of sympathetic stimulation on the heart. The primary mechanism responsible for this regulation involves β-adrenergic receptor (βAR) stimulation of cAMP production and subsequent activation of protein kinase A (PKA). Although it is well established that PKA-dependent phosphorylation regulates LTCC function, there is still much we do not understand. However, it has recently become clear that the interaction of the various signaling proteins involved is not left to completely stochastic events due to random diffusion. The primary LTCC expressed in cardiac muscle, CaV1.2, forms a supramolecular signaling complex that includes the β2AR, G proteins, adenylyl cyclases, phosphodiesterases, PKA, and protein phosphatases. In some cases, the protein interactions with CaV1.2 appear to be direct, in other cases they involve scaffolding proteins such as A kinase anchoring proteins and caveolin-3. Functional evidence also suggests that the targeting of these signaling proteins to specific membrane domains plays a critical role in maintaining the fidelity of receptor mediated LTCC regulation. This information helps explain the phenomenon of compartmentation, whereby different receptors, all linked to the production of a common diffusible second messenger, can vary in their ability to regulate LTCC activity. The purpose of this review is to examine our current understanding of the signaling complexes involved in cardiac LTCC regulation.

Cardiomyocytes from AKAP7 knockout mice respond normally to adrenergic stimulation

Protein kinase A (PKA) is activated during sympathetic stimulation of the heart and phosphorylates key proteins involved in cardiac Ca(2+) handling, including the L-type Ca(2+) channel (Ca(V)1.2) and phospholamban (PLN). This results in acceleration and amplification of the beat-to-beat changes in cytosolic Ca(2+) in cardiomyocytes and, in turn, an increased rate and force of contraction. PKA is held in proximity to its substrates by protein scaffolds called A kinase anchoring proteins (AKAPs). It has been suggested that the short and long isoforms of AKAP7 (also called AKAP15/18) localize PKA in complexes with Ca(V)1.2 and PLN, respectively. We generated an AKAP7 KO mouse in which all isoforms were deleted and tested whether Ca(2+) current, intracellular Ca(2+) concentration, or Ca(2+) reuptake were impaired in isolated adult ventricular cardiomyocytes following stimulation with the β-adrenergic agonist isoproterenol. KO cardiomyocytes responded normally to adrenergic stimulation, as measured by whole-cell patch clamp or a fluorescent intracellular Ca(2+) indicator. Phosphorylation of Ca(V)1.2 and PLN were also unaffected by genetic deletion of AKAP7. Immunoblot and RT-PCR revealed that only the long isoforms of AKAP7 were detectable in ventricular cardiomyocytes. The results indicate that AKAP7 is not required for regulation of Ca(2+) handling in mouse cardiomyocytes.

Caught in self-interaction: evolutionary and functional mechanisms of protein homooligomerization

Many soluble and membrane proteins form homooligomeric complexes in a cell which are responsible for the diversity and specificity of many pathways, may mediate and regulate gene expression, activity of enzymes, ion channels, receptors, and cell adhesion processes. The evolutionary and physical mechanisms of oligomerization are very diverse and its general principles have not yet been formulated. Homooligomeric states may be conserved within certain protein subfamilies and might be important in providing specificity to certain substrates while minimizing interactions with other unwanted partners. Moreover, recent studies have led to a greater awareness that transitions between different oligomeric states may regulate protein activity and provide the switch between different pathways. In this paper we summarize the biological importance of homooligomeric assemblies, physico-chemical properties of their interfaces, experimental and computational methods for their identification and prediction. We particularly focus on homooligomer evolution and describe the mechanisms to develop new specificities through the formation of different homooligomeric complexes. Finally, we discuss the possible role of oligomeric transitions in the regulation of protein activity and compile a set of experimental examples with such regulatory mechanisms.

AKAPs: the architectural underpinnings of local cAMP signaling

The cAMP-dependent protein kinase A (PKA) is targeted to specific compartments in the cardiac myocyte by A-kinase anchoring proteins (AKAPs), a diverse set of scaffold proteins that have been implicated in the regulation of excitation-contraction coupling and cardiac remodeling. AKAPs bind not only PKA, but also a large variety of structural and signaling molecules. In this review, we discuss the basic concepts underlying compartmentation of cAMP and PKA signaling, as well as a few of the individual AKAPs that have been shown to be functionally relevant in the heart. This article is part of a Special Issue entitled "Local Signaling in Myocytes".

Mechanisms of protein oligomerization, the critical role of insertions and deletions in maintaining different oligomeric states

The main principles of protein-protein recognition are elucidated by the studies of homooligomers which in turn mediate and regulate gene expression, activity of enzymes, ion channels, receptors, and cell-cell adhesion processes. Here we explore oligomeric states of homologous proteins in various organisms to better understand the functional roles and evolutionary mechanisms of homooligomerization. We observe a great diversity in mechanisms controlling oligomerization and focus in our study on insertions and deletions in homologous proteins and how they enable or disable complex formation. We show that insertions and deletions which differentiate monomers and dimers have a significant tendency to be located on the interaction interfaces and about a quarter of all proteins studied and forty percent of enzymes have regions which mediate or disrupt the formation of oligomers. We suggest that relatively small insertions or deletions may have a profound effect on complex stability and/or specificity. Indeed removal of complex enabling regions from protein structures in many cases resulted in the complete or partial loss of stability. Moreover, we find that insertions and deletions modulating oligomerization have a lower aggregation propensity and contain a larger fraction of polar, charged residues, glycine and proline compared to conventional interfaces and protein surface. Most likely, these regions may mediate specific interactions, prevent nonspecific dysfunctional aggregation and preclude undesired interactions between close paralogs therefore separating their functional pathways. Last, we show how the presence or absence of insertions and deletions on interfaces might be of practical value in annotating protein oligomeric states.

Organizing signal transduction through A-kinase anchoring proteins (AKAPs)

A fundamental role for protein-protein interactions in the organization of signal transduction pathways is evident. Anchoring, scaffolding and adapter proteins function to enhance the precision and directionality of these signaling events by bringing enzymes together. The cAMP signaling pathway is organized by A-kinase anchoring proteins. This family of proteins assembles enzyme complexes containing the cAMP-dependent protein kinase, phosphoprotein phosphatases, phosphodiesterases and other signaling effectors to optimize cellular responses to cAMP and other second messengers. Selected A-kinase anchoring protein signaling complexes are highlighted in this minireview.

The minimal autoinhibited unit of the guanine nucleotide exchange factor intersectin

Intersectin-1L is a member of the Dbl homology (DH) domain guanine nucleotide exchange factors (GEF) which control Rho-family GTPase signaling. Intersectin-1L is a GEF that is specific for Cdc42. It plays an important role in endocytosis, and is regulated by several partners including the actin regulator N-WASP. Intact intersectin-1L shows low Cdc42 exchange activity, although the isolated catalytic DH domain shows high activity. This finding suggests that the molecule is autoinhibited. To investigate the mechanism of autoinhibition we have constructed a series of domain deletions. We find that the five SH3 domains of intersectin are important for autoinhibition, with the fifth domain (SH3(E)) being sufficient for the bulk of the autoinhibitory effect. This SH3 domain appears to primarily interact with the DH domain. We have determined the crystal structure of the SH3(E)-DH domain construct, which shows a domain swapped arrangement in which the SH3 from one monomer interacts with the DH domain of the other monomer. Analytical ultracentrifugation and gel filtration, however, show that under biochemical concentrations, the construct is fully monomeric. Thus we propose that the actual autoinhibited structure contains the related intramolecular SH3(E)-DH interaction. We propose a model in which this intramolecular interaction may block or distort the GTPase binding region of the DH domain.

Networking with AKAPs: context-dependent regulation of anchored enzymes

A-Kinase Anchoring Proteins (AKAPs) orchestrate and synchronize cellular events by tethering the cAMP-dependent protein kinase (PKA) and other signaling enzymes to organelles and membranes. The control of kinases and phosphatases that are held in proximity to activators, effectors, and substrates favors the rapid dissemination of information from one cellular location to the next. This article charts the inception of the PKA-anchoring hypothesis, the characterization of AKAPs and their nomenclature, and the physiological roles of context-specific AKAP signaling complexes.

The Rho guanine nucleotide exchange factor AKAP13 (BRX) is essential for cardiac development in mice

A fundamental biologic principle is that diverse biologic signals are channeled through shared signaling cascades to regulate development. Large scaffold proteins that bind multiple proteins are capable of coordinating shared signaling pathways to provide specificity to activation of key developmental genes. Although much is known about transcription factors and target genes that regulate cardiomyocyte differentiation, less is known about scaffold proteins that couple signals at the cell surface to differentiation factors in developing heart cells. Here we show that AKAP13 (also known as Brx-1, AKAP-Lbc, and proto-Lbc), a unique protein kinase A-anchoring protein (AKAP) guanine nucleotide exchange region belonging to the Dbl family of oncogenes, is essential for cardiac development. Cardiomyocytes of Akap13-null mice had deficient sarcomere formation, and developing hearts were thin-walled and mice died at embryonic day 10.5-11.0. Disruption of Akap13 was accompanied by reduced expression of Mef2C. Consistent with a role of AKAP13 upstream of MEF2C, Akap13 siRNA led to a reduction in Mef2C mRNA, and overexpression of AKAP13 augmented MEF2C-dependent reporter activity. The results suggest that AKAP13 coordinates Galpha(12) and Rho signaling to an essential transcription program in developing cardiomyocytes.

Mechanisms of protein kinase A anchoring

The second messenger cyclic adenosine monophosphate (cAMP), which is produced by adenylyl cyclases following stimulation of G-protein-coupled receptors, exerts its effect mainly through the cAMP-dependent serine/threonine protein kinase A (PKA). Due to the ubiquitous nature of the cAMP/PKA system, PKA signaling pathways underlie strict spatial and temporal control to achieve specificity. A-kinase anchoring proteins (AKAPs) bind to the regulatory subunit dimer of the tetrameric PKA holoenzyme and thereby target PKA to defined cellular compartments in the vicinity of its substrates. AKAPs promote the termination of cAMP signals by recruiting phosphodiesterases and protein phosphatases, and the integration of signaling pathways by binding additional signaling proteins. AKAPs are a heterogeneous family of proteins that only display similarity within their PKA-binding domains, amphipathic helixes docking into a hydrophobic groove formed by the PKA regulatory subunit dimer. This review summarizes the current state of information on compartmentalized cAMP/PKA signaling with a major focus on structural aspects, evolution, diversity, and (patho)physiological functions of AKAPs and intends to outline newly emerging directions of the field, such as the elucidation of AKAP mutations and alterations of AKAP expression in human diseases, and the validation of AKAP-dependent protein-protein interactions as new drug targets. In addition, alternative PKA anchoring mechanisms employed by noncanonical AKAPs and PKA catalytic subunit-interacting proteins are illustrated.

Structure and function of heterotrimeric G protein-regulated Rho guanine nucleotide exchange factors

Activation of certain classes of G protein-coupled receptors (GPCRs) can lead to alterations in the actin cytoskeleton, gene transcription, cell transformation, and other processes that are known to be regulated by Rho family small-molecular-weight GTPases. Although these responses can occur indirectly via cross-talk from canonical heterotrimeric G protein cascades, it has recently been demonstrated that Dbl family Rho guanine nucleotide exchange factors (RhoGEFs) can serve as the direct downstream effectors of heterotrimeric G proteins. Heterotrimeric Galpha(12/13), Galpha(q), and Gbetagamma subunits are each now known to directly bind and regulate RhoGEFs. Atomic structures have recently been determined for several of these RhoGEFs and their G protein complexes, providing fresh insight into the molecular mechanisms of signal transduction between GPCRs and small molecular weight G proteins. This review covers what is currently known about the structure, function, and regulation of these recently recognized effectors of heterotrimeric G proteins.

The ubiquitin-like protein LC3 regulates the Rho-GEF activity of AKAP-Lbc

AKAP-Lbc is a member of the A-kinase anchoring protein (AKAP) family that has been recently associated with the development of pathologies, such as cardiac hypertrophy and cancer. We have previously demonstrated that, at the molecular level, AKAP-Lbc functions as a guanine nucleotide exchange factor (GEF) that promotes the specific activation of RhoA. In the present study, we identified the ubiquitin-like protein LC3 as a novel regulatory protein interacting with AKAP-Lbc. Mutagenesis studies revealed that LC3, through its NH(2)-terminal alpha-helical domain, interacts with two binding sites located within the NH(2)-terminal regulatory region of AKAP-Lbc. Interestingly, LC3 overexpression strongly reduced the ability of AKAP-Lbc to interact with RhoA, profoundly impairing the Rho-GEF activity of the anchoring protein and, as a consequence, its ability to promote cytoskeletal rearrangements associated with the formation of actin stress fibers. Moreover, AKAP-Lbc mutants that fail to interact with LC3 show a higher basal Rho-GEF activity as compared with the wild type protein and become refractory to the inhibitory effect of LC3. This suggests that LC3 binding maintains AKAP-Lbc in an inactive state that displays a reduced ability to promote downstream signaling. Collectively, these findings provide evidence for a previously uncharacterized role of LC3 in the regulation of Rho signaling and in the reorganization of the actin cytoskeleton.

Modulation of Rho guanine exchange factor Lfc activity by protein kinase A-mediated phosphorylation

Lfc is a guanine nucleotide exchange factor (GEF) for Rho that demonstrates an unusual ability to associate with microtubules. While several phosphorylated residues have been detected in the Lfc polypeptide, the mechanism(s) by which phosphorylation regulates the exchange activity of Lfc remains unclear. We confirm that Lfc is a phosphorylated protein and demonstrate that 14-3-3 interacts directly and in a phosphorylation-dependent manner with Lfc. We identify AKAP121 as an Lfc-binding protein and show that Lfc is phosphorylated in an AKAP-dependent manner by protein kinase A (PKA). Forskolin treatment induced 14-3-3 binding to Lfc and suppressed the exchange activity of wild-type Lfc on RhoA. Importantly, a mutant of Lfc that is unable to associate with 14-3-3 proteins was resistant to inhibition by forskolin. Tctex-1, a dynein motor light chain, binds to Lfc in a competitive manner with 14-3-3.

Regulation of RhoGEF proteins by G12/13-coupled receptors

G protein-coupled receptors (GPCRs) represent a large family of seven transmembrane receptors, which communicate extracellular signals into the cellular lumen. The human genome contains 720-800 GPCRs, and their diverse signal characteristics are determined by their specific tissue and subcellular expression profiles, as well as their coupling profile to the various G protein families (G(s), G(i), G(q), G(12)). The G protein coupling pattern links GPCR activation to the specific downstream effector pathways. G(12/13) signalling of GPCRs has been studied only recently in more detail, and involves activation of RhoGTPase nucleotide exchange factors (RhoGEFs). Four mammalian RhoGEFs regulated by G(12/13) proteins are known: p115-RhoGEF, PSD-95/Disc-large/ZO-1 homology-RhoGEF, leukemia-associated RhoGEF and lymphoid blast crisis-RhoGEF. These link GPCRs to activation of the small monomeric GTPase RhoA, and other downstream effectors. Misregulated G(12/13) signalling is involved in multiple pathophysiological conditions such as cancer, cardiovascular diseases, arterial and pulmonary hypertension, and bronchial asthma. Specific targeting of G(12/13) signalling-related diseases of GPCRs hence provides novel therapeutic approaches. Assays to quantitatively measure GPCR-mediated activation of G(12/13) are only emerging, and are required to understand the G(12/13)-linked pharmacology. The review gives an overview of G(12/13) signalling of GPCRs with a focus on RhoGEF proteins as the immediate mediators of G(12/13) activation.

Cyclic AMP (cAMP)-mediated stimulation of adipocyte differentiation requires the synergistic action of Epac- and cAMP-dependent protein kinase-dependent processes

Cyclic AMP (cAMP)-dependent processes are pivotal during the early stages of adipocyte differentiation. We show that exchange protein directly activated by cAMP (Epac), which functions as a guanine nucleotide exchange factor for the Ras-like GTPases Rap1 and Rap2, was required for cAMP-dependent stimulation of adipocyte differentiation. Epac, working via Rap, acted synergistically with cAMP-dependent protein kinase (protein kinase A [PKA]) to promote adipogenesis. The major role of PKA was to down-regulate Rho and Rho-kinase activity, rather than to enhance CREB phosphorylation. Suppression of Rho-kinase impaired proadipogenic insulin/insulin-like growth factor 1 signaling, which was restored by activation of Epac. This interplay between PKA and Epac-mediated processes not only provides novel insight into the initiation and tuning of adipocyte differentiation, but also demonstrates a new mechanism of cAMP signaling whereby cAMP uses both PKA and Epac to achieve an appropriate cellular response.

Protein kinase A-dependent phosphorylation modulates beta1Pix guanine nucleotide exchange factor activity through 14-3-3beta binding

beta(1)Pix is a guanine nucleotide exchange factor (GEF) for the small GTPases Rac and Cdc42 which has been shown to mediate signaling pathways leading to cytoskeletal reorganization. In the present study, we show that the basal association between endogenous betaPix and endogenous 14-3-3beta was increased after forskolin stimulation and significantly inhibited by protein kinase A inhibitor. However, forskolin stimulation failed to increase the interaction between 14-3-3beta and a beta(1)Pix mutant that is insensitive to protein kinase A phosphorylation, beta(1)Pix(S516A, T526A). We present evidence indicating that forskolin-induced binding of 14-3-3beta to beta(1)Pix results in inhibition of Rac1 GTP loading in 293 cells and in vitro. Furthermore, we show that deletion of 10 amino acid residues within the leucine zipper domain is sufficient to block beta(1)Pix homodimerization and 14-3-3beta binding and modulates beta(1)Pix-GEF activity. These residues also play a crucial role in beta(1)Pix intracellular localization. These results indicate that 14-3-3beta negatively affects the GEF activity of dimeric beta(1)Pix only. Altogether, these results provide a mechanistic insight into the role of 14-3-3beta in modulating beta(1)Pix-GEF activity.

Lsc activity is controlled by oligomerization and regulates integrin adhesion

Lsc is a hematopoietic-restricted protein that functions as an effector of G alpha(12/13)-associated G-protein coupled receptors that activates RhoA. In the absence of Lsc leukocytes exhibit impaired migration and B lymphocytes inefficiently resolve integrin-mediated adhesion. Here, we demonstrate that Lsc exists physiologically in primary B lymphocytes as a large molecular weight complex resembling a homo-tetramer. Interfering with the assembly of this large molecular weight Lsc oligomer results in the activation of both Lsc functional activities and leads to cell rounding and inhibition of integrin-mediated adhesion. During cell migration on integrin ligands we find Lsc localizes predominantly toward the rear of migrating cells where we suggest it activates RhoA to resolve integin-mediated adhesion. Together these data demonstrate that Lsc regulates integrin-mediated adhesive events at the trailing edge of migrating cells.

Scaffolding proteins in G-protein signaling

Heterotrimeric G proteins are ubiquitous signaling partners of seven transmembrane-domain G-protein-coupled receptors (GPCRs), the largest (and most important pharmacologically) receptor family in mammals. A number of scaffolding proteins have been identified that regulate various facets of GPCR signaling. In this review, we summarize current knowledge concerning those scaffolding proteins that are known to directly bind heterotrimeric G proteins, and discuss the composition of the protein complexes they assemble and their effects on signal transduction. Emerging evidence about possible ways of regulation of activity of these scaffolding proteins is also discussed.

The A-kinase anchoring protein (AKAP)-Lbc-signaling complex mediates alpha1 adrenergic receptor-induced cardiomyocyte hypertrophy

In response to various pathological stresses, the heart undergoes a pathological remodeling process that is associated with cardiomyocyte hypertrophy. Because cardiac hypertrophy can progress to heart failure, a major cause of lethality worldwide, the intracellular signaling pathways that control cardiomyocyte growth have been the subject of intensive investigation. It has been known for more than a decade that the small molecular weight GTPase RhoA is involved in the signaling pathways leading to cardiomyocyte hypertrophy. Although some of the hypertrophic pathways activated by RhoA have now been identified, the identity of the exchange factors that modulate its activity in cardiomyocytes is currently unknown. In this study, we show that AKAP-Lbc, an A-kinase anchoring protein (AKAP) with an intrinsic Rho-specific guanine nucleotide exchange factor activity, is critical for activating RhoA and transducing hypertrophic signals downstream of alpha1-adrenergic receptors (ARs). In particular, our results indicate that suppression of AKAP-Lbc expression by infecting rat neonatal ventricular cardiomyocytes with lentiviruses encoding AKAP-Lbc-specific short hairpin RNAs strongly reduces both alpha1-AR-mediated RhoA activation and hypertrophic responses. Interestingly, alpha1-ARs promote AKAP-Lbc activation via a pathway that requires the alpha subunit of the heterotrimeric G protein G12. These findings identify AKAP-Lbc as the first Rho-guanine nucleotide exchange factor (GEF) involved in the signaling pathways leading to cardiomyocytes hypertrophy.