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1.  Molecular Determinants of Epidermal Growth Factor Binding: A Molecular Dynamics Study 
PLoS ONE  2013;8(1):e54136.
The epidermal growth factor receptor (EGFR) is a member of the receptor tyrosine kinase family that plays a role in multiple cellular processes. Activation of EGFR requires binding of a ligand on the extracellular domain to promote conformational changes leading to dimerization and transphosphorylation of intracellular kinase domains. Seven ligands are known to bind EGFR with affinities ranging from sub-nanomolar to near micromolar dissociation constants. In the case of EGFR, distinct conformational states assumed upon binding a ligand is thought to be a determining factor in activation of a downstream signaling network. Previous biochemical studies suggest the existence of both low affinity and high affinity EGFR ligands. While these studies have identified functional effects of ligand binding, high-resolution structural data are lacking. To gain a better understanding of the molecular basis of EGFR binding affinities, we docked each EGFR ligand to the putative active state extracellular domain dimer and 25.0 ns molecular dynamics simulations were performed. MM-PBSA/GBSA are efficient computational approaches to approximate free energies of protein-protein interactions and decompose the free energy at the amino acid level. We applied these methods to the last 6.0 ns of each ligand-receptor simulation. MM-PBSA calculations were able to successfully rank all seven of the EGFR ligands based on the two affinity classes: EGF>HB-EGF>TGF-α>BTC>EPR>EPG>AR. Results from energy decomposition identified several interactions that are common among binding ligands. These findings reveal that while several residues are conserved among the EGFR ligand family, no single set of residues determines the affinity class. Instead we found heterogeneous sets of interactions that were driven primarily by electrostatic and Van der Waals forces. These results not only illustrate the complexity of EGFR dynamics but also pave the way for structure-based design of therapeutics targeting EGF ligands or the receptor itself.
PMCID: PMC3554757  PMID: 23382875
2.  A TRAF2 binding independent region of TNFR2 is responsibl for TRAF2 depletion and enhancement of cytotoxicity driven b TNFR1 
Oncotarget  2013;5(1):224-236.
Tumor Necrosis Factor (TNF) interacts with two receptors known as TNFR1 and TNFR2. TNFR1 activation may result in either cell proliferation or cell death. TNFR2 activates Nuclear Factor-kappaB (NF-kB) and c-Jun N-terminal kinase (JNK) which lead to transcriptional activation of genes related to cell proliferation and survival. This depends on the binding of TNF Receptor Associated Factor 2 (TRAF2) to the receptor. TNFR2 also induces TRAF2 degradation. In this work we have investigated the structural features of TNFR2 responsible for inducing TRAF2 degradation and have studied the biological consequences of this activity. We show that when TNFR1 and TNFR2 are co-expressed, TRAF2 depletion leads to an enhanced TNFR1 cytotoxicity which correlates with the inhibition of NF-kB. NF-kB activation and TRAF2 degradation depend of different regions of the receptor since TNFR2 mutants at amino acids 343-349 fail to induce TRAF2 degradation and have lost their ability to enhance TNFR1-mediated cell death but are still able to activate NF-kB. Moreover, whereas NF-kB activation requires TRAF2 binding to the receptor, TRAF2 degradation appears independent of TRAF2 binding. Thus, TNFR2 mutants unable to bind TRAF2 are still able to induce its degradation and to enhance TNFR1-mediated cytotoxicity. To test further this receptor crosstalk we have developed a system stably expressing in cells carrying only endogenous TNFR1 the chimeric receptor RANK-TNFR2, formed by the extracellular region of RANK (Receptor activator of NF-kB) and the intracellular region of TNFR2.This has made possible to study independently the signals triggered by TNFR1 and TNFR2. In these cells TNFR1 is selectively activated by soluble TNF (sTNF) while RANK-TNFR2 is selectively activated by RANKL. Treatment of these cells with sTNF and RANKL leads to an enhanced cytotoxicity.
PMCID: PMC3960203  PMID: 24318359
TNF receptors; Death Receptors; TRAF2; Apoptosis; NF-kappaB
3.  Characterization of ligand type of estrogen receptor by MD simulation and mm-PBSA free energy analysis 
Estrogen receptor is a transcription regulator and can bind structurally distinct ligands with full agonistic, SERMs, or full antagonistic properties. Crystal structures of the ER ligand binding domain (LBD)-complexed with full agonists or SERMs show that these ligands induce two different orientations of Helix12 in LBD and generate two different conformations, agonist conformation (A conformation) and AF2 antagonist conformation (B conformation). To understand how ER ligands interact with LBD structurally and energetically, we docked 3 full agonists, 9 SERMs and 2 full antagonists in both the A and B conformation of ERα LBD and performed a 4-step molecular dynamics (MD) simulation on all 28 complexes followed by mm-PBSA binding free energy calculation. We found that all full agonists prefer the A conformation while all SERMs prefer the B conformation. Analysis of the mm-PBSA energies revealed that calculated total binding free energies (delta PBTOT) and the difference of VDW between complex and the sum of receptor of ligands and ligand (delta VDW) have the order of full agonists>SERMs>full antagonists. However, the PB surface term has the order of full antagonists>SERMs>full agonists. We also found that the sum of the RMSD of mainchain atoms of Helix12 and all atoms of ligands in the A conformation is significantly lower for full agonists than that of the other ligands. Together, we conclude that the three types of ER ligands interact with the A and B conformations of ERα LBD differently and same type of ligands interact similarly. These findings will be useful in understanding the mechanism of ER antagonism and can be used in ligand type prediction.
PMCID: PMC3180098  PMID: 21969034
Estrogen receptor; antagonism; full agonist; full antagonist; SERM; agonist conformation; AF2 antagonist conformation
4.  Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations 
The Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2 or 4) to the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1). MD simulation lengths have obvious impact on the predictions, and longer MD simulations are not always necessary to achieve better predictions; (2). The predictions are quite sensitive to solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface; (3). Conformational entropy showed large fluctuations in MD trajectories and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.
PMCID: PMC3029230  PMID: 21117705
MM/GBSA; MM/PBSA; Generalized Born; Poisson Boltzmann; Binding free energy
5.  Colocalization of endogenous TNF with a functional intracellular splice form of human TNF receptor type 2 
Tumor necrosis factor (TNF) is a pleiotropic cytokine involved in a broad spectrum of inflammatory and immune responses including proliferation, differentiation, and cell death. The biological effects of TNF are mediated via two cell surface TNF receptors: p55TNFR (TNFR1; CD120a) and p75TNFR (TNFR2; CD120b). Soluble forms of these two receptors consisting of the extracellular domains are proteolytically cleaved from the membrane and act as inhibitors. A novel p75TNFR isoform generated by the use of an additional transcriptional start site has been described and was termed hicp75TNFR. We focused on the characterization of this new isoform as this protein may be involved in chronic inflammatory processes.
Cell lines were retroviraly transduced with hp75TNFR isoforms. Subcellular localization and colocalization studies with TNF were performed using fluorescence microscopy including exhaustive photon reassignment software, flow cytometry, and receptosome isolation by magnetic means. Biochemical properties of the hicp75TNFR were determined by affinity chromatography, ELISA, and western blot techniques.
We describe the localization and activation of a differentially spliced and mainly intracellularly expressed isoform of human p75TNFR, termed hicp75TNFR. Expression studies with hicp75TNFR cDNA in different cell types showed the resulting protein mostly retained in the trans-Golgi network and in endosomes and colocalizes with endogenous TNF. Surface expressed hicp75TNFR behaves like hp75TNFR demonstrating susceptibility for TACE-induced shedding and NFκB activation after TNF binding.
Our data demonstrate that intracellular hicp75TNFR is not accessible for exogenously provided TNF but colocalizes with endogenously produced TNF. These findings suggest a possible intracellular activation mechanism of hicp75TNFR by endogenous TNF. Subsequent NFκB activation might induce anti-apoptotic mechanisms to protect TNF-producing cells from cytotoxic effects of TNF. In addition, the intracellular and not TACE-accessible splice form of the hp75TNFR could serve as a pool of preformed, functional hp75TNFR.
PMCID: PMC1183239  PMID: 15996269
6.  Binding Efficiency of Protein-Protein Complexes 
Biochemistry  2012;51(45):9124-9136.
We examine the relationship between binding affinity and interface size for reversible protein-protein interactions (PPI), using cytokines from the tumor necrosis factor (TNF) superfamily and their receptors as a test case. Using surface plasmon resonance, we measured single-site binding affinities for the large receptor TNFR1 binding to its ligands TNFα (KD = 1.4 ± 0.4 nM) and lymphotoxin-α (KD = 50 ± 10 nM), and also for the small receptor Fn14 binding to TWEAK (KD = 70 ± 10 nM). We additionally assembled data for all other TNF/TNFR family complexes for which reliable single site binding affinities have been reported. We used these values to calculate the binding efficiency – defined as binding energy per Å2 of surface area buried at the contact interface – for the nine of these complexes for which co-crystal structures are available, and compared the results to those for a set of 144 protein-protein complexes with published affinity values. The results show that the most efficient PPI complexes generate ~20 cal.mol−1/Å2 of binding energy. A minimum contact area of ~500 Å2 is required for a stable complex, required to generate sufficient interaction energy to pay the entropic cost of co-localizing two proteins from 1 M solution. The most compact and efficient TNF/TNFR complex was BAFF/BR3, which achieved ~80% of the maximum achievable binding efficiency. Other small receptors also gave high binding efficiencies, while the larger receptors generated only 44-49% of this limit despite interacting primarily through just a single small domain. The results provide new insight into how much binding energy can be generated by a PPI interface of a given size, and establish a quantitative method to predict how large a natural or engineered contact interface must be to achieve a given level of binding affinity.
PMCID: PMC3567247  PMID: 23088250
binding energy; binding affinity; protein-protein binding; ligand efficiency; TNF superfamily; TNF receptor; TNFR1; TNFRp55; TNFα; LTα; Fn14; TWEAK; cysteine rich domain; protein evolution; protein engineering; affinity maturation
7.  Molecular Recognition in a Diverse Set of Protein-Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations 
End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal•mol−1 highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10). But larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the non-polar and entropy components, rather than a better representation of the electrostatics. SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by pre-scoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the non-polar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.
PMCID: PMC4058328  PMID: 24032517
8.  Exploring the Molecular Design of Protein Interaction Sites with Molecular Dynamics Simulations and Free Energy Calculations† 
Biochemistry  2009;48(2):399-414.
The significant work that has been invested toward understanding protein–protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein–protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein–protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes.
PMCID: PMC2754190  PMID: 19113835
9.  Assessing the performance of the MM/PBSA and MM/GBSA methods: II. The accuracy of ranking poses generated from docking 
In molecular docking, it is challenging to develop a scoring function which is accurate to conduct high throughput screenings (HTS). Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) in molecular docking studies. Here we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein/ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics (MD) simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric 2.0, produced a Spearman correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free energy estimations and is efficient to re-score the top-hit poses produced by other less accurate scoring functions.
PMCID: PMC3043139  PMID: 20949517
Molecular Docking; MM/GBSA; MM/PBSA; Generalized Born; Poisson Boltzmann; Binding free energy
10.  ATROSAB, a humanized antagonistic anti-tumor necrosis factor receptor one-specific antibody 
mAbs  2010;2(6):639-647.
Tumor necrosis factor (TNF) signals through TNFR1 and TNFR2, two membrane receptors, and TNFR1 is known to be the major pathogenic mediator of chronic and acute inflammatory diseases. Present clinical intervention is based on neutralization of the ligand TNF. Selective inhibition of TNF receptor 1 (TNFR1) provides an alternative opportunity to neutralize the pro-inflammatory activity of TNF while maintaining the advantageous immunological responses mediated by TNFR2, including immune regulation, tissue homeostasis and neuroprotection. We recently humanized a mouse anti-human TNFR1 monoclonal antibody exhibiting TNFR1-neutralizing activity. This humanized antibody has been converted into an IgG1 molecule (ATROSAB) containing a modified Fc region previously demonstrated to have greatly reduced effector functions. Purified ATROSAB produced in CHO cells showed strong binding to human and rhesus TNFR1-Fc fusion protein and mouse embryonic fibroblasts transfected with a recombinant TNFR1 fusion protein with an affinity identical to the parental mouse antibody H398. Using chimeric human/mouse TNFR1 molecules, the epitope of ATROSAB was mapped to the N-terminal region (amino acid residues 1–70) comprising the first cysteine-rich domain (CRD1) and the A1 sub-domain of CRD2. In vitro, ATROSAB inhibited typical TNF-mediated responses like apoptosis induction and activation of NFκB-dependent gene expression such as IL-6 and IL-8 production. These findings open the way to further analyze the therapeutic activity of ATROSAB in relevant disease models in non-human primates.
PMCID: PMC3011218  PMID: 20935477
humanized IgG; antagonistic antibody; tumor necrosis factor receptor 1; epitope mapping
11.  CrmE, a Novel Soluble Tumor Necrosis Factor Receptor Encoded by Poxviruses 
Journal of Virology  2001;75(1):226-233.
Cytokines and chemokines play a critical role in both the innate and acquired immune responses and constitute prime targets for pathogen sabotage. Molecular mimicry of cytokines and cytokine receptors is a mechanism encoded by large DNA viruses to modulate the host immune response. Three tumor necrosis factor receptors (TNFRs) have been identified in the poxvirus cowpox virus. Here we report the identification and characterization of a fourth distinct soluble TNFR, named cytokine response modifier E (CrmE), encoded by cowpox virus. The crmE gene has been sequenced in strains of the orthopoxviruses cowpox virus, ectromelia virus, and camelpox virus, and was found to be active in cowpox virus. crmE is expressed as a secreted 18-kDa protein with TNF binding activity. CrmE was produced in the baculovirus and vaccinia virus expression systems and was shown to bind human, mouse, and rat TNF, but not human lymphotoxin α, conjugates of lymphotoxins α and β, or seven other ligands of the TNF superfamily. However, CrmE protects cells only from the cytolytic activity of human TNF. CrmE is a new member of the TNFR superfamily which is expressed as a soluble molecule that blocks the binding of TNF to high-affinity TNFRs on the cell surface. The remarkable finding of a fourth poxvirus-encoded TNFR suggests that modulation of TNF activity is complex and represents a novel viral immune evasion mechanism.
PMCID: PMC113916  PMID: 11119592
12.  Antagonistic TNF Receptor One-Specific Antibody (ATROSAB): Receptor Binding and In Vitro Bioactivity 
PLoS ONE  2013;8(8):e72156.
Selective inhibition of TNFR1 signaling holds the potential to greatly reduce the pro-inflammatory activity of TNF, while leaving TNFR2 untouched, thus allowing for cell survival and tissue homeostasis. ATROSAB is a humanized antagonistic anti-TNFR1 antibody developed for the treatment of inflammatory diseases.
Methodology/Principal Findings
The epitope of ATROSAB resides in the N-terminal region of TNFR1 covering parts of CRD1 and CRD2. By site-directed mutagenesis, we identified Arg68 and His69 of TNFR1 as important residues for ATROSAB binding. ATROSAB inhibited binding of 125I-labeled TNF to HT1080 in the subnanomolar range. Furthermore, ATROSAB inhibited release of IL-6 and IL-8 from HeLa and HT1080 cells, respectively, induced by TNF or lymphotoxin alpha (LTα). Different from an agonistic antibody (Htr-9), which binds to a region close to the ATROSAB epitope but elicits strong TNFR1 activation, ATROSAB showed a negligible induction of IL-6 and IL-8 production over a broad concentration range. We further verified that ATROSAB, comprising mutations within the Fc region known to abrogate complement fixation and antibody-mediated cellular effector functions, indeed lacks binding activity for C1q, FcγRI (CD64), FcγRIIB (CD32b), and FcγRIII (CD16) disabling ADCC and CDC.
The data corroborate ATROSAB’s unique function as a TNFR1-selective antagonist efficiently blocking both TNF and LTα action. In agreement with recent studies of TNFR1 complex formation and activation, we suggest a model of the underlying mechanism of TNFR1 inhibition by ATROSAB.
PMCID: PMC3747052  PMID: 23977237
13.  Both internalization and AIP1 association are required for TNFR2-mediated JNK Signaling 
The proinflammtory cytokine tumor necrosis factor (TNF), primarily via TNF receptor 1 (TNFR1), induces NF-κB-dependent cell survival, and JNK and caspase-dependent cell death, regulating vascular endothelial cell (EC) activation and apoptosis. However, signaling by the second receptor, TNFR2, is poorly understood. The goal of this study is to dissect how TNFR2 mediates NF-κB and JNK signaling in vascular endothelial cells (EC), and its relevance with in vivo EC function.
Methods and Results
We show that TNFR2 contributes to TNF-induced NF-κB and JNK signaling in EC as TNFR2 deletion or knockdown reduces the TNF responses. To dissect out the critical domains of TNFR2 that mediate the TNF responses, we examine the activity of TNFR2 mutant with a specific deletion of the TNFR2 intracellular region, which contains conserved domain I, domain II, domain III, and two TRAF2-binding sites. Deletion analyses indicate that different sequences on TNFR2 have distinct roles in NF-κB and JNK activation. Specifically, deletion of the TRAF2-binding sites (TNFR2-59) diminishes the TNFR2-mediated NF-κB, but not JNK activation; whereas, deletion of domain II or domain III blunts TNFR2-mediated JNK but not NF-κB activation. Interestingly, we find that the TRAF2-binding sites ensure TNFR2 on the plasma membrane, but the di-leucine LL motif within the domain II and aa338-355 within the domain III are required for TNFR2 internalization as well as TNFR2-dependent JNK signaling. Moreover, domain III of TNFR2 is responsible for association with AIP1, a signaling adaptor critical for TNF-induced JNK signaling. While TNFR2 containing the TRAF2-binding sites prevents EC cell death, a specific activation of JNK without NF-κB activation by TNFR2-59 strongly induces caspase activation and EC apoptosis.
Our data reveal that both internalization and AIP1 association are required for TNFR2-dependent JNK and apoptotic signaling. Controlling TNFR2-mediated JNK and apoptotic signaling in EC may provide a novel strategy for the treatment of vascular diseases.
PMCID: PMC3421067  PMID: 22743059
TNF; TNFR2; JNK; endothelial cell; apoptosis
14.  An In Silico Analysis of the Binding Modes and Binding Affinities of Small Molecule Modulators of PDZ-Peptide Interactions 
PLoS ONE  2013;8(8):e71340.
Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson’s disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of the binding modes and binding affinities of a set of 38 small molecules with known Ki or Kd values for PDZ2 and PDZ3 domains of PSD-95 protein. These two PDZ domains show differential selectivity for these compounds despite having a high degree of sequence similarity and almost identical peptide binding pockets. Optimum binding modes for these ligands for PDZ2 and PDZ3 domains were identified by using a novel combination of semi-flexible docking and explicit solvent molecular dynamics (MD) simulations. Analysis of the binding modes revealed most of the peptidomimectic ligands which had high Ki or Kd moved away from the peptide binding pocket, while ligands with high binding affinities remained in the peptide binding pocket. The differential specificities of the PDZ2 and PDZ3 domains primarily arise from differences in the conformation of the loop connecting βB and βC strands, because this loop interacts with the N-terminal chemical moieties of the ligands. We have also computed the MM/PBSA binding free energy values for these 38 compounds with both the PDZ domains from multiple 5 ns MD trajectories on each complex i.e. a total of 228 MD trajectories of 5 ns length each. Interestingly, computational binding free energies show good agreement with experimental binding free energies with a correlation coefficient of approximately 0.6. Thus our study demonstrates that combined use of docking and MD simulations can help in identification of potent inhibitors of PDZ-peptide complexes.
PMCID: PMC3738590  PMID: 23951139
15.  Etk/Bmx as a Tumor Necrosis Factor Receptor Type 2-Specific Kinase: Role in Endothelial Cell Migration and Angiogenesis 
Molecular and Cellular Biology  2002;22(21):7512-7523.
Tumor necrosis factor (TNF) is a cytokine that mediates many pathophysiologial processes, including angiogenesis. However, the molecular signaling involved in TNF-induced angiogenesis has not been determined. In this study, we examined the role of Etk/Bmx, an endothelial/epithelial tyrosine kinase involved in cell adhesion, migration, and survival in TNF-induced angiogenesis. We show that TNF activates Etk specifically through TNF receptor type 2 (TNFR2) as demonstrated by studies using a specific agonist to TNFR2 and TNFR2-deficient cells. Etk forms a preexisting complex with TNFR2 in a ligand-independent manner, and the association is through multiple domains (pleckstrin homology domain, TEC homology domain, and SH2 domain) of Etk and the C-terminal domain of TNFR2. The C-terminal 16-amino-acid residues of TNFR2 are critical for Etk association and activation, and this Etk-binding and activating motif in TNFR2 is not overlapped with the TNFR-associated factor type 2 (TRAF2)-binding sequence. Thus, TRAF2 is not involved in TNF-induced Etk activation, suggesting a novel mechanism for Etk activation by cytokine receptors. Moreover, a constitutively active form of Etk enhanced, whereas a dominant-negative Etk blocked, TNF-induced endothelial cell migration and tube formation. While most TNF actions have been attributed to TNFR1, our studies demonstrate that Etk is a TNFR2-specific kinase involved in TNF-induced angiogenic events.
PMCID: PMC135657  PMID: 12370298
16.  The Tumor Necrosis Factor Receptor Stalk Regions Define Responsiveness to Soluble versus Membrane-Bound Ligand 
Molecular and Cellular Biology  2012;32(13):2515-2529.
The family of tumor necrosis factor receptors (TNFRs) and their ligands form a regulatory signaling network that controls immune responses. Various members of this receptor family respond differently to the soluble and membrane-bound forms of their respective ligands. However, the determining factors and underlying molecular mechanisms of this diversity are not yet understood. Using an established system of chimeric TNFRs and novel ligand variants mimicking the bioactivity of membrane-bound TNF (mTNF), we demonstrate that the membrane-proximal extracellular stalk regions of TNFR1 and TNFR2 are crucial in controlling responsiveness to soluble TNF (sTNF). We show that the stalk region of TNFR2, in contrast to the corresponding part of TNFR1, efficiently inhibits both the receptor's enrichment/clustering in particular cell membrane regions and ligand-independent homotypic receptor preassembly, thereby preventing sTNF-induced, but not mTNF-induced, signaling. Thus, the stalk regions of the two TNFRs not only have implications for additional TNFR family members, but also provide potential targets for therapeutic intervention.
PMCID: PMC3434479  PMID: 22547679
17.  The pre-ligand binding assembly domain: a potential target of inhibition of tumour necrosis factor receptor function 
Annals of the Rheumatic Diseases  2000;59(Suppl 1):i50-i53.
Signalling by the tumour necrosis factor receptors (TNFR) is thought to be mediated by the binding of the trimeric ligand TNF to three monomeric subunits of the receptor. This ligand induced trimerisation model of TNFR signalling is mainly supported by crystallographic data of the p60 TNFR-1 and TNFβ complex in which the trimeric ligand interdigitates between the individual receptor chains and prevents the receptor subunits from interacting with each other. Recently, a domain NH2-terminal to the ligand binding domain in the extracellular region of p60 TNFR-1, p80 TNFR-2 and Fas was identified that mediates receptor self association before ligand binding. This pre-ligand binding assembly domain or PLAD is critical for assembly of functional receptor complexes on the cell surface and may provide a potential target in the design of future novel therapeutics against diseases mediated by members of the TNFR family of receptors.

PMCID: PMC1766631  PMID: 11053089
18.  Develop and Test a Solvent Accessible Surface Area-Based Model in Conformational Entropy Calculations 
It is of great interest in modern drug design to accurately calculate the free energies of protein-ligand or nucleic acid-ligand binding. MM-PBSA (Molecular Mechanics-Poisson Boltzmann Surface Area) and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) have gained popularity in this field. For both methods, the conformational entropy, which is usually calculated through normal mode analysis (NMA), is needed to calculate the absolute binding free energies. Unfortunately, NMA is computationally demanding and becomes a bottleneck of the MM-PB/GBSA-NMA methods. In this work, we have developed a fast approach to estimate the conformational entropy based upon solvent accessible surface area calculations. In our approach, the conformational entropy of a molecule, S, can be obtained by summing up the contributions of all atoms, no matter they are buried or exposed. Each atom has two types of surface areas, solvent accessible surface area (SAS) and buried SAS (BSAS). The two types of surface areas are weighted to estimate the contribution of an atom to S. Atoms having the same atom type share the same weight and a general parameter k is applied to balance the contributions of the two types of surface areas.
This entropy model was parameterized using a large set of small molecules for which their conformational entropies were calculated at the B3LYP/6-31G* level taking the solvent effect into account. The weighted solvent accessible surface area (WSAS) model was extensively evaluated in three tests. For the convenience, TS, the product of temperature T and conformational entropy S, were calculated in those tests. T was always set to 298.15 K through the text. First of all, good correlations were achieved between WSAS TS and NMA TS for 44 protein or nucleic acid systems sampled with molecular dynamics simulations (10 snapshots were collected for post-entropy calculations): the mean correlation coefficient squares (R2) was 0.56. As to the 20 complexes, the TS changes upon binding, TΔS, were also calculated and the mean R2 was 0.67 between NMA and WSAS. In the second test, TS were calculated for 12 proteins decoy sets (each set has 31 conformations) generated by the Rosetta software package. Again, good correlations were achieved for all decoy sets: the mean, maximum, minimum of R2 were 0.73, 0.89 and 0.55, respectively. Finally, binding free energies were calculated for 6 protein systems (the numbers of inhibitors range from 4 to 18) using four scoring functions. Compared to the measured binding free energies, the mean R2 of the six protein systems were 0.51, 0.47, 0.40 and 0.43 for MM-GBSA-WSAS, MM-GBSA-NMA, MM-PBSA-WSAS and MM-PBSA-NMA, respectively. The mean RMS errors of prediction were 1.19, 1.24, 1.41, 1.29 kcal/mol for the four scoring functions, correspondingly. Therefore, the two scoring functions employing WSAS achieved a comparable prediction performance to that of the scoring functions using NMA. It should be emphasized that no minimization was performed prior to the WSAS calculation in the last test.
Although WSAS is not as rigorous as physical models such as quasi-harmonic analysis and thermodynamic integration (TI), it is computationally very efficient as only surface area calculation is involved and no structural minimization is required. Moreover, WSAS has achieved a comparable performance to normal mode analysis. We expect that this model could find its applications in the fields like high throughput screening (HTS), molecular docking and rational protein design. In those fields, efficiency is crucial since there are a large number of compounds, docking poses or protein models to be evaluated. A list of acronyms and abbreviations used in this work is provided for quick reference.
PMCID: PMC3360827  PMID: 22497310
Conformational Entropy; Configurational Entropy; WSAS; Solvent Accessible Surface Area; MM-PBSA; MM-GBSA; Binding Free Energy Calculations; Protein Design; Drug Design
19.  The Free Energy Landscape of Small Molecule Unbinding 
PLoS Computational Biology  2011;7(2):e1002002.
The spontaneous dissociation of six small ligands from the active site of FKBP (the FK506 binding protein) is investigated by explicit water molecular dynamics simulations and network analysis. The ligands have between four (dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms, and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each ligand) are grouped according to a set of intermolecular distances into nodes of a network, and the direct transitions between them are the links. The network analysis reveals that the bound state consists of several subbasins, i.e., binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts. The dissociation kinetics show a simple (i.e., single-exponential) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state. The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site, which correspond to multiple pathways of dissociation. For the six small ligands of FKBP, the weaker the binding affinity the closer to the bound state (along the intermolecular distance) are the transition state structures, which is a new manifestation of Hammond behavior. Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution. Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape (both thermodynamics and kinetics) of ligand unbinding.
Author Summary
Most known drugs used to fight human diseases are small molecules that bind strongly to proteins, particularly to enzymes or receptors involved in essential biochemical or physiological processes. The binding process is very complex because of the many degrees of freedom and multiple interactions between pairs of atoms. Here we show that network analysis, a mathematical tool used to study a plethora of complex systems ranging from social interactions (e.g, friendship links in Facebook) to metabolic networks, provides a detailed description of the free energy landscape and pathways involved in the binding of small molecules to an enzyme. Using molecular dynamics simulations to sample the free energy landscape, we provide strong evidence at atomistic detail that small ligands can have multiple favorable positions and orientations in the active site. We also observe a broad heterogeneity of (un)binding pathways. Experimental approaches to the study of fragment binding to proteins have limitations in spatial and temporal resolution. Our network analysis of the molecular dynamics simulations does not suffer from these limitations. It provides a thorough description of the thermodynamics and kinetics of the binding process.
PMCID: PMC3033371  PMID: 21390201
20.  Crystal structure and molecular modeling study of N-carbamoylsarcosine amidase Ta0454 from Thermoplasma acidophilum 
Journal of structural biology  2009;169(3):304-311.
A crystal structure of the putative N-carbamoylsarcosine amidase (CSHase) Ta0454 from Thermoplasma acidophilum was solved by single-wavelength anomalous diffraction and refined at a resolution of 2.35 Å. CSHases are involved in the degradation of creatinine. Ta0454 shares a similar fold and a highly conserved C-D-K catalytic triad (Cys123, Asp9, and Lys90) with the structures of three cysteine hydrolases (PDB codes 1NBA, 1IM5, and 2H0R). Molecular dynamics (MD) simulations of Ta0454/N-carbamoylsarcosine and Ta0454/pyrazinamide complexes were performed to determine the structural basis of the substrate binding pattern for each ligand. Based on the MD simulated-trajectories, the MM/PBSA method predicts binding free energies of −24.5 and −17.1 kcal/mol for the two systems, respectively. The predicted binding free energies suggest that Ta0454 is selective for N-carbamoylsarcosine over pyrazinamide, and zinc ions play an important role in the favorable substrate bound states.
PMCID: PMC2830209  PMID: 19932181
N-carbamoylsarcosine amidase; C-D-K catalytic triad; creatinine degradation; crystal structure; MM/PBSA; molecular dynamics simulations
21.  Suppression of TNF receptor-1 signaling in an in vitro model of epileptic tolerance 
Tumor necrosis factor-α (TNFα) is a pleiotropic cytokine that can regulate cell survival, inflammation or, under certain circumstances, trigger cell death. Previous work in rat seizure models and analysis of temporal lobe samples from epilepsy patients has suggested seizures activate TNF receptor 1 (TNFR1). Here we explored the activation and functional significance of TNFR1 signaling in the mouse hippocampus using in vitro and in vivo models of seizure-induced neuronal injury. Focal-onset status epilepticus in mice upregulated TNFR1 levels and led to formation of TNFR1-TNFR-associated death domain (TRADD) and TRADD-Fas-associated death domain (FADD) binding. Seizure-like injury modeled in vitro by removal of chronic excitatory blockade in mouse hippocampal neurons also activated this TNFR1 signaling pathway. Prior exposure of hippocampal neurons to a non-harmful seizure episode, via NMDA receptor blockade, 24 h prior to injurious seizures significantly reduced cell death and modeled epileptic tolerance in vitro. TNFR1 complex formation with TRADD and TRADD-FADD binding were reduced in tolerant cells. Finally, TNFR1 signaling and cell death were reduced by PKF-242-484, a dual matrix metaloproteinase/TNFα converting enzyme inhibitor. The present study shows that TNFR1 signaling is activated in mouse seizure models and may contribute to neuropathology in vitro and in vivo while suppression of this pathway may underlie neuroprotection in epileptic tolerance.
PMCID: PMC3134006  PMID: 21760970
Seizure; epileptic tolerance; preconditioning; TACE; PKF242-484; cell death; neuroprotection; TNF
22.  Lyapunov exponents and phase diagrams reveal multi-factorial control over TRAIL-induced apoptosis 
Kinetic modeling, phase diagrams analysis, and quantitative single-cell experiments are combined to investigate how multiple factors, including the XIAP:caspase-3 ratio and ligand concentration, regulate receptor-mediated apoptosis.
Based on protein expression levels, Lyapunov-based phase diagrams predict which pathways are required for a cell to undergo receptor-mediated cell death.Multiple inter-dependent factors, including the XIAP:caspase-3 ratio and ligand concentration, regulate the requirement for mitochondrial outer membrane permeabilization during receptor-mediated apoptosis.The E3 ubiquitin ligase activity of XIAP is essential for maintaining the ‘snap-action' regulation of effector caspase activity.Cell-to-cell variability in protein expression gives rise to mixed phenotypes in cell lines that map close to boundaries (separatrices) identified by Lyapunov exponent analysis.
In mammalian cells, extrinsic (receptor-mediated) apoptosis is triggered by binding of extracellular death ligands such as tumor necrosis factor (TNF) and TRAIL (TNF-related apoptosis-inducing ligand) to cognate receptors. When death receptors are activated, death inducing signaling complexes (DISCs) assemble causing activation and cleavage of initiator pro-caspases-8 and -10, which then cleave effector pro-caspases-3 and -7 in a multi-enzyme cascade (Riedl and Shi, 2004). Active effector caspases digest essential cellular proteins and activate the CAD nucleases that cleave genomic DNA, thereby killing cells. This cascade of DISC assembly followed by initiator and then effector caspase activation is sufficient to kill so-called type I cells (e.g. B lymphocytes), but most cell types exhibit a type II behavior in which mitochondrial outer membrane permeabilization (MOMP) is an essential step in the march to death (Scaffidi et al, 1998; Barnhart et al, 2003; Letai, 2008). Identifying factors that determine whether cells are type I or II is of practical and theoretical interest. From a practical perspective, whether a cell requires MOMP for apoptosis determines the potency of Bcl2 and similar oncogenes, the efficacy of anti-Bcl2 drugs such as navitoclax (ABT-263), and the sensitivity of cells to TRAIL and anti-TRAIL receptor antibodies, which are also investigational anti-cancer drugs (Newsom-Davis et al, 2009). From a theoretical perspective, the type I versus II choice exemplifies a common situation in mammalian cells in which overlapping signaling pathways play a greater or lesser role in controlling cell fate depending on cell type: it is remarkable that a simple three-step (receptor→initiator caspase→effector caspase) process is sufficient to trigger apoptosis in some cell types but that a much more complex route involving MOMP is required in others.
Attempts to understand why some cells require MOMP for cell death and others do not have identified differences in the oligomeric state of death ligand receptors and the efficiency of DISC formation as important variables. In cells in which DISCs form efficiently, initiator caspases are cleaved rapidly and sufficient effector pro-caspases are processed into their active forms to kill cells (type I cells; Scaffidi et al, 1999b). In type II cells, DISC formation seems to be less efficient, and it has been proposed that MOMP is required to amplify weak initiator caspase signals and thereby generate lethal effector caspase levels (Barnhart et al, 2003). However, it has recently become apparent that XIAP also plays a role in type I versus II choice: in XIAP knockout mice, liver cells switch from a type II to a type I phenotype (Jost et al, 2009) and XIAP is observed to be involved in the survival of type I cells treated with death ligands in culture (Maas et al, 2010).
In this paper, we attempt to place these observations in a quantitative context by analyzing a computational model of extrinsic cell death using a method drawn from dynamical system analysis, direct finite-time Lyapunov exponent (DLE) analysis. Our implementation of DLE analysis relates changes in the concentrations of protein in a model to an outcome several hours later. We computed DLEs for six regulators of apoptosis over a range of concentrations determined experimentally to represent a natural range of variation in parental or genetically modified tumor cell lines. This generated a phase space onto which individual cell lines could be mapped using quantitative immunoblotting data. Cell-to-cell variation was estimated by flow cytometry and also mapped onto the phase space. The most interesting regions of the space were those in which a small change in one or more initial protein concentration resulted in a dramatic change in phenotype. Such a boundary or separatrix was observed in slices of phase space corresponding XIAP versus pro-caspase-3 concentration (the [XIAP]:[caspase-3] ratio). In cells in which the ratio is low, a type I phenotype is predicted to occur; when the ratio is high, a type II phenotype is favored; and in cell lines that lie close to the separatrix, cell-to-cell variability is expected, with some cells exhibiting a type I phenotype and others a type II behavior. DLE analysis shows that the [XIAP]:[caspase-3] ratio is not the only controlling factor in type I versus II control: as receptor activity or ligand concentration increase, the position of the separatrix changes so as to expand the region in which the type I phenotype is favored.
We tested these predictions by manipulating XIAP and ligand levels in multiple cell lines and then followed cell death by imaging, flow cytometry, or clonogenic assays. We observed that when XIAP was knocked out (by homologous recombination) in the HCT116 colorectal cancer line, cells shifted from a pure type II to a type I phenotype, as predicted from the DLE phase diagram. SKW6.4 B-cell lymphoma cells were predicted to lie at a position in phase space that is insensitive to XIAP levels (within the range achievable by over-expression) and we confirmed this experimentally. Finally, T47D breast cancer cells were predicted—and observed—to straddle the separatrix and to exhibit cell-to-cell variability in fate, with some cells showing a type I and others a type II phenotype. As the concentration of TRAIL was increased, the ratio of type I to type II T47D cells increased, confirming the prediction that this ratio is controlled in a multi-factorial manner.
To extend our approach to mutations that change protein activity rather than protein level, we simulated the effects of changing rate constants that control ubiquitylation of caspase-3 following its binding to XIAP. We generated cells carrying a truncated form of XIAP that lacks the RING domain (XIAPΔRING) and cannot mediate the ubiquitylation of caspase-3 (this truncation leaves the affinity of XIAP for caspase-3 unchanged). We predicted and demonstrated experimentally that expression of XIAPΔRING disrupts normal snap-action control over caspase-3 activation. Our findings not only advance understanding of extrinsic apoptosis but also constitute a proof of principle for an approach to quantitative modeling of dynamic regulatory processes in diverse cell types.
Receptor-mediated apoptosis proceeds via two pathways: one requiring only a cascade of initiator and effector caspases (type I behavior) and the second requiring an initiator–effector caspase cascade and mitochondrial outer membrane permeabilization (type II behavior). Here, we investigate factors controlling type I versus II phenotypes by performing Lyapunov exponent analysis of an ODE-based model of cell death. The resulting phase diagrams predict that the ratio of XIAP to pro-caspase-3 concentrations plays a key regulatory role: type I behavior predominates when the ratio is low and type II behavior when the ratio is high. Cell-to-cell variability in phenotype is observed when the ratio is close to the type I versus II boundary. By positioning multiple tumor cell lines on the phase diagram we confirm these predictions. We also extend phase space analysis to mutations affecting the rate of caspase-3 ubiquitylation by XIAP, predicting and showing that such mutations abolish all-or-none control over activation of effector caspases. Thus, phase diagrams derived from Lyapunov exponent analysis represent a means to study multi-factorial control over a complex biochemical pathway.
PMCID: PMC3261706  PMID: 22108795
apoptosis; caspases; dynamical systems analysis; kinetic modeling; XIAP
23.  Genetic Analysis of the Role of Tumor Necrosis Factor Receptors in Functional Outcome after Traumatic Brain Injury in Mice 
Journal of Neurotrauma  2010;27(6):1037-1046.
We previously reported that tumor necrosis factor-α (TNF-α) and Fas receptor induce acute cellular injury, tissue damage, and motor and cognitive deficits after controlled cortical impact (CCI) in mice (Bermpohl et al. 2007); however, the TNF receptors (TNFR) involved are unknown. Using a CCI model and novel mutant mice deficient in TNFR1/Fas, TNFR2/Fas, or TNFR1/TNFR2/Fas, we tested the hypothesis that the combination of TNFR2/Fas is protective, whereas TNFR1/Fas is detrimental after CCI. Uninjured knockout (KO) mice showed no differences in baseline physiological variables or motor or cognitive function. Following CCI, mice deficient in TNFR2/Fas had worse post-injury motor and Morris water maze (MWM) performance than wild-type (WT) mice (p < 0.05 group effect for wire grip score and MWM performance by repeated measures ANOVA). No differences in motor or cognitive outcome were observed in TNFR1/Fas KO, or in TNFR2 or TNFR1 single KO mice, versus WT mice. Additionally, no differences in propidium iodide (PI)-positive cells (at 6 h) or lesion size (at 14 days) were observed between WT and TNFR1/Fas or TNFR2/Fas KO mice. Somewhat surprisingly, mice deficient in TNFR1/TNFR2/Fas also had PI-positive cells, lesion size, and motor and MWM deficits similar to those of WT mice. These data suggest a protective role for TNFR2/Fas in the pathogenesis of TBI. Further studies are needed to determine whether direct or indirect effects of TNFR1 deletion in TNFR2/Fas KO mice mediate improved functional outcome in TNFR1/TNFR2/Fas KO mice after CCI.
PMCID: PMC2943499  PMID: 20205514
controlled cortical impact; gene knockout; inflammation; mice; tumor necrosis factor; traumatic brain injury
24.  Double-stranded RNA Induces Shedding of the Soluble 34-kDa TNFR1 from Human Airway Epithelial Cells via TLR3-TRIF-RIP1-dependent Signaling: Roles for Duox2- and Caspase-dependent Pathways 
Tumor necrosis factor (TNF), an important mediator of inflammatory and innate immune responses, can be regulated by binding to soluble TNF receptors. The type 1, 55-kDa TNF receptor (TNFR1), the key receptor for TNF signaling, is released to the extracellular space by two mechanisms, the inducible cleavage and shedding of 34-kDa soluble TNFR1 ectodomains (sTNFR1) and the constitutive release of full-length 55-kDa TNFR1 within exosome-like vesicles. The aim of this study was to identify and characterize Toll-like receptor (TLR) signaling pathways that mediate TNFR1 release to the extracellular space. We for the first time demonstrate that poly (I:C), a synthetic double-strand RNA (dsRNA) analog that signals via TLR3, induces sTNFR1 shedding from human airway epithelial (NCI-H292) cells, whereas ligands for other microbial pattern recognition receptors, including TLR4, TLR7 and NOD2, do not. Furthermore, poly (I:C) selectively induces the cleavage of 34-kDa soluble TNFR1 ectodomains, but does not enhance the release of full-length 55-kDa TNFR1 within exosome-like vesicles. RNA interference experiments demonstrated that poly (I:C)-induced sTNFR1 shedding is mediated via activation of TLR3-TRIF-RIP1 signaling, with subsequent activation of two downstream pathways. One pathway involves the Duox2-mediated generation of reactive oxygen species (ROS), while the other pathway is via the caspase-mediated activation of apoptosis. Thus, the ability of dsRNA to induce the cleavage and shedding of the 34-kDa sTNFR1 from human bronchial epithelial cells represents a novel mechanism by which innate immune responses to viral infections are modulated.
PMCID: PMC3075875  PMID: 21148036
poly (I:C); TNFR1 shedding; TLR3; TRIF; RIP1; caspase; Duox; ROS; signal transduction
25.  Design principles of nuclear receptor signaling: how complex networking improves signal transduction 
Nuclear receptors often function in the cytoplasm.A triple conveyor belt pumps ligand (signal) into the nucleus and onto the DNA.The active export of importins enhances signaling to the nucleus.Sharing a single nuclear pore may reduce rather than increase crosstalk.
Nuclear receptors (NRs) derive their family name from the early observation that they are located in the nucleus, despite responding to extracellular signals such as hormones (e.g., cortisol) (Fanestil and Edelman, 1966). According to the ‘classical' paradigm of NR signaling, the NR resides in the nucleus, attached to a DNA response element, waiting for its ligand to bind. The actual systems have multiple additional features (reviewed in Cutress et al, 2008; Cao et al, 2009; Levin, 2009a; Bunce and Campbell, 2010), such as that NRs shuttle between the nucleus and the cytoplasm (Von Knethen et al, 2010) and ligand addition changes receptor location dynamically (Pratt et al, 1989; Liu and DeFranco, 2000; Kumar et al, 2004, 2006; Tanaka et al, 2005; Heitzer et al, 2007; Prüfer and Boudreaux, 2007; Ricketson et al, 2007; Cutress et al, 2008): Figure 1 summarizes the current understanding of the topology of the reaction networks involved in NR signaling, in systems biological graphical notation (SBGN), with NR activation, importin-α and -β binding, nuclear pore complex (NPC)-mediated import, recycling of importins, NR binding to target promoter sequences, exportin-mediated nuclear export of the NR, exportin cycling and free energy-driven Ran recycling. This topology is surprisingly complex when compared with the ‘classical' paradigm. To address to what extent this extra complexity is just detail or contributes essential functionality, we have simulated the dynamics of the NR transcriptional response in maximally realistic mathematical models of increasingly complex designs.
The calculations revealed significant disadvantages of the classical and simplest mechanism for endocrine NR-mediated signaling, i.e., the one with localization of the NR exclusively on the DNA (design 1 in Figure 2A): the transcriptional response was very low (Figure 2B). A high concentration of free NR in the nucleus (design 2) improved sensitivity, but made the responsiveness much slower (Figure 2B). If the NR was equally distributed between the nucleus and the cytoplasm without the NR being able to traverse the nucleocytoplasmic membrane (design 3), then, although the NR diffuses more slowly than the much smaller ligand molecule, the higher concentration of the NR increased flux from the plasma membrane to the nuclear membrane; the steady state was reached faster (Figure 2B and C; compare design 3 relative to design 2). Enabling the NR to traverse the nucleocytoplasmic membrane (design 4), further accelerated the response (Figure 2B and C).
Designs 1–4 considered the permeation of the NR through the nuclear membrane to be passive, implying an import/export activity ratio of 1. When varying the import to export activity ratio (design 5), a trade-off between the fast responsiveness of design 4 and the high sensitivity of design 2 was calculated (Figure 2B). In order to maximize responsiveness, core-NR should be concentrated in the cytoplasm, whereas to gain sensitivity, liganded NR should be concentrated in the nucleus. This suggested that performance could be improved by making nuclear import and export selective for liganded over unliganded NR (design 6; Figure 2A). Indeed, retention of core-NR in the cytoplasm provided high influx of ligand into the nucleus (Figure 2D), and also the highest concentration of ligand in the nucleus (Figure 2C): Apart from its classical receptor role in transcription regulation, the NR may function as (part of) an active pump for its ligand, resembling a triple conveyor belt: importins and exportins cycle as conveyor belts and drive the cycling of the third conveyor belt consisting of the NR that pumps ligand into the nucleus.
Two other striking features of the NR signaling network (Figure 1) are related to the facts that the energy of GTP hydrolysis is coupled to an active export of importins rather than to direct active import of NR and that the same NPC is used for all transport processes. At first sight, the former may waste free energy and the latter might cause fragility due to interferences between different NRs and other signaling pathways. However, our models show that active nuclear export of importins is a design preventing NR sequestration in the nucleus by nuclear importins and, equally paradoxically, the transport of all cargo through the same NPC makes the transport of any particular cargo robust with respect to perturbations in the availability of any other cargo.
Our calculations also predict that there is an optimal ratio of nuclear to cytoplasmic fractions of the NR (Figure 2G) that depends on the specific properties of the ligand and on the transcription activation requirements. This may help to explain the observation that different NRs have different predominant intracellular localizations. Our model calculations are thereby in line with many experimental observations, but specific cases of NR signaling may only exhibit a subset of all features. Our models can aid in identifying which subsets are important in any particular case of NR signaling, as we demonstrate for an example.
In this study, we have shown that complex networks of biochemical and signaling reactions can harbor subtle design principles that can be understood rationally in terms of simplified but not simple models (which are available to the reader).
The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands.
PMCID: PMC3018161  PMID: 21179018
biochemical network; kinetic model; nuclear receptor; signaling; systems biology

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