Molecular recognition in helix-loop-helix and helix-loop-helix-leucine zipper domains: Design of repertoires and selection of high affinity ligands for natural proteins

Helix-loop-helix (HLH) and helix-loop-helix-leucine zipper (HLHZip) are dimerization domains that mediate selective pairing among members of a large transcription factor family involved in cell fate determination. To investigate the molecular rules underlying recognition specificity and to isolate molecules interfering with cell proliferation and differentiation control, we assembled two molecular repertoires obtained by directed randomization of the binding surface in these two domains. For this strategy we selected the Heb HLH and Max Zip regions as molecular scaffolds for the randomization process and displayed the two resulting molecular repertoires on lambda phage capsids. By affinity selection, many domains were isolated that bound to the proteins Mad, Rox, MyoD, and Id2 with different levels of affinity. Although several residues along an extended surface within each domain appeared to contribute to dimerization, some key residues critically involved in molecular recognition could be identified. Furthermore, a number of charged residues appeared to act as switch points facilitating partner exchange. By successfully selecting ligands for four of four HLH or HLHZip proteins, we have shown that the repertoires assembled are rather general and possibly contain elements that bind with sufficient affinity to any natural HLH or HLHZip molecule. Thus they represent a valuable source of ligands that could be used as reagents for molecular dissection of functional regulatory pathways

Binding to DPF-motif by the POB1 EH domain is responsible for POB1-Eps15 interaction

<p>Abstract</p> <p>Background</p> <p>Eps15 homology (EH) domains are protein interaction modules binding to peptides containing Asn-Pro-Phe (NPF) motifs and mediating critical events during endocytosis and signal transduction. The EH domain of POB1 associates with Eps15, a protein characterized by a striking string of DPF triplets, 15 in human and 13 in mouse Eps15, at the C-terminus and lacking the typical EH-binding NPF motif.</p> <p>Results</p> <p>By screening a multivalent nonapeptide phage display library we have demonstrated that the EH domain of POB1 has a different recognition specificity since it binds to both NPF and DPF motifs. The region of mouse Eps15 responsible for the interaction with the EH domain of POB1 maps within a 18 amino acid peptide (residues 623–640) that includes three DPF repeats. Finally, mutational analysis in the EH domain of POB1, revealed that several solvent exposed residues, while distal to the binding pocket, mediate specific recognition of binding partners through both hydrophobic and electrostatic contacts.</p> <p>Conclusion</p> <p>In the present study we have analysed the binding specificity of the POB1 EH domain. We show that it differs from other EH domains since it interacts with both NPF- and DPF-containing sequences. These unusual binding properties could be attributed to a different conformation of the binding pocket that allows to accommodate negative charges; moreover, we identified a cluster of solvent exposed Lys residues, which are only found in the EH domain of POB1, and influence binding to both NPF and DPF motifs. The characterization of structures of the DPF ligands described in this study and the POB1 EH domain will clearly determine the involvement of the positive patch and the rationalization of our findings.</p

Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype

The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells

BACKGROUND: Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization. METHODS: We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks. RESULTS: By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations. CONCLUSIONS: The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks

Oxidative Stress, DNA Damage, and c-Abl Signaling: At the Crossroad in Neurodegenerative Diseases?

The c-Abl tyrosine kinase is implicated in diverse cellular activities including growth factor signaling, cell adhesion, oxidative stress, and DNA damage response. Studies in mouse models have shown that the kinases of the c-Abl family play a role in the development of the central nervous system. Recent reports show that aberrant c-Abl activation causes neuroinflammation and neuronal loss in the forebrain of transgenic adult mice. In line with these observations, an increased c-Abl activation is reported in human neurodegenerative pathologies, such as Parkinson's, and Alzheimer's diseases. This suggests that aberrant nonspecific posttranslational modifications induced by c-Abl may contribute to fuel the recurrent phenotypes/features linked to neurodegenerative disorders, such as an impaired mitochondrial function, oxidative stress, and accumulation of protein aggregates. Herein, we review some reports on c-Abl function in neuronal cells and we propose that modulation of different aspects of c-Abl signaling may contribute to mediate the molecular events at the interface between stress signaling, metabolic regulation, and DNA damage. Finally, we propose that this may have an impact in the development of new therapeutic strategies

CUBAN, a Case Study of Selective Binding:Structural Details of the Discrimination between Ubiquitin and NEDD8

The newly identified CUBAN (Cullin binding domain associating with NEDD8) domain recognizes both ubiquitin and the ubiquitin-like NEDD8. Despite the high similarity between the two molecules, CUBAN shows a clear preference for NEDD8, free and conjugated to cullins. We previously characterized the domain structure, both alone and in complex with NEDD8. The results here reported are addressed to investigate the determinants that drive the selective binding of CUBAN towards NEDD8 and ubiquitin. The 15N HSQC NMR perturbation pattern of the labeled CUBAN domain, when combined with either NEDD8 or ubiquitin, shows a clear involvement of hydrophobic residues that characterize the early stages of these interactions. After a slow conformational selection step, hydrophobic and then neutral and polar interactions take place, which drive the correct orientation of the CUBAN domain, leading to differences in the recognition scheme of NEDD8 and ubiquitin. As a result, a cascade of induced fit steps seems to determine the structural preference shown for NEDD8 and therefore the basis of the selectivity of the CUBAN domain. Finally, molecular dynamics analysis was performed to determine by fluctuations the internal flexibility of the CUBAN/NEDD8 complex. We consider that our results, based on a structural investigation mainly focused on the early stages of the recognition, provide a fruitful opportunity to report the different behavior of the same protein with two highly similar binding partners