Quantifying Intramolecular Binding in Multivalent Interactions: A Structure-Based Synergistic Study on Grb2-Sos1 Complex

Numerous signaling proteins use multivalent binding to increase the specificity and affinity of their interactions within the cell. Enhancement arises because the effective binding constant for multivalent binding is larger than the binding constants for each individual interaction. We seek to gain both qualitative and quantitative understanding of the multivalent interactions of an adaptor protein, growth factor receptor bound protein-2 (Grb2), containing two SH3 domains interacting with the nucleotide exchange factor son-of-sevenless 1 (Sos1) containing multiple polyproline motifs separated by flexible unstructured regions. Grb2 mediates the recruitment of Sos1 from the cytosol to the plasma membrane where it activates Ras by inducing the exchange of GDP for GTP. First, using a combination of evolutionary information and binding energy calculations, we predict an additional polyproline motif in Sos1 that binds to the SH3 domains of Grb2. This gives rise to a total of five polyproline motifs in Sos1 that are capable of binding to the two SH3 domains of Grb2. Then, using a hybrid method combining molecular dynamics simulations and polymer models, we estimate the enhancement in local concentration of a polyproline motif on Sos1 near an unbound SH3 domain of Grb2 when its other SH3 domain is bound to a different polyproline motif on Sos1. We show that the local concentration of the Sos1 motifs that a Grb2 SH3 domain experiences is approximately 1000 times greater than the cellular concentration of Sos1. Finally, we calculate the intramolecular equilibrium constants for the crosslinking of Grb2 on Sos1 and use thermodynamic modeling to calculate the stoichiometry. With these equilibrium constants, we are able to predict the distribution of complexes that form at physiological concentrations. We believe this is the first systematic analysis that combines sequence, structure, and thermodynamic analyses to determine the stoichiometry of the complexes that are dominant in the cellular environment

Kinetics of the hydrogen abstraction ·C2H5 + alkane → C2H6 + alkyl reaction class: an application of the reaction class transition state theory

This paper presents an application of the reaction class transition state theory (RC-TST) to predict thermal rate constants for hydrogen abstraction reactions at alkane by the C2H5 radical on-the-fly. The linear energy relationship (LER), developed for acyclic alkanes, was also proven to hold for cyclic alkanes. We have derived all RCTST parameters from rate constants of 19 representative reactions, coupling with LER and the barrier height grouping (BHG) approach. Both the RC-TST/LER, where only reaction energy is needed, and the RC-TST/BHG, where no other information is needed, can predict rate constants for any reaction in this reaction class with satisfactory accuracy for combustion modeling. Our analysis indicates that less than 50% systematic errors on the average exist in the predicted rate constants using either the RC-TST/LER or RC-TST/BHG method, while in comparison with explicit rate calculations, the differences are within a factor of 2 on the average. The results also show that the RC-TST method is not sensitive to the choice of density functional theory used

Predicting protein-protein binding sites in membrane proteins

<p>Abstract</p> <p>Background</p> <p>Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.</p> <p>Results</p> <p>We present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.</p> <p>Conclusion</p> <p>Given a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.</p

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Allogeneic hematopoietic stem cell transplantation in China: where we are and where to go

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is an effective and sometimes the only curative therapy for patients with certain hematological diseases. Allo-HSCT has been practiced in China for approximately 30 years, and great improvements have been made within the past decade, particularly in fields such as the haploidentical HSCT system, strategies to overcome relapse and GVHD, and modified HSCT for elderly patients. This review will describe the current situation and provide a prospective of these unique aspects of Allo-HSCT in China

Manganese(II) Complexes with Schiff Bases Immobilized on Nanosilica as Catalysts of the Reaction of Ozone Decomposition

In this article, we submit the description of synthesis and identification of manganese(II) complexes with pyrogenic nanosilica-immobilized (d av = 10 nm; S sp = 290 m2/g) hydroxyaldimine ligands (Mn(L)2/Si): salicilaldiminopropyl (L1); 5-bromosalicilaldiminopropyl (L2); 2-hydroxynaphtaldiminopropyl (L3); 2-hydroxy-3-methoxybenzaldiminopropyl (L4); 2-hydroxy-3,5-dichloroacetophenoniminopropyl (L5); and 4-hydroxy-3-methoxybenzaldiminopropyl (L6). The ligands and complexes were characterized by UV-VIS and IR spectrometry. Nanocomposites consisting of complexes Mn(L)2/Si showed a high catalytic activity in low-temperature ozone decomposition in the range of concentrations between 2.1 × 10−6 and 8.4 × 10−6 mol/l. The number of catalytic cycles increased for isostructural pseudotetrahedral complexes Mn(L)2/Si (L1–L5) in the following order: Mn(L3)2 >> Mn(L4)2 > Mn(L1)2 > Mn(L2)2 > Mn(L5)2. In the case of pseudooctahedral complexes with L6, the change of coordination polyhedral does not influence the kinetics and stoichiometric parameters of the reaction

Epidermal Growth Factor Gene Polymorphism and Risk of Hepatocellular Carcinoma: A Meta-Analysis

BACKGROUND: Hepatocarcinogenesis is a complex process that may be influenced by many factors, including polymorphism in the epidermal growth factor (EGF) gene. Previous work suggests an association between the EGF 61*A/G polymorphism (rs4444903) and susceptibility to hepatocellular carcinoma (HCC), but the results have been inconsistent. Therefore, we performed a meta-analysis of several studies covering a large population to address this controversy. METHODS: PubMed, EMBASE, Google Scholar and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. Data were abstracted independently by two reviewers. A meta-analysis was performed to examine the association between EGF 61*A/G polymorphism and susceptibility to HCC. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. RESULTS: Eight studies were chosen in this meta-analysis, involving 1,304 HCC cases (1135 Chinese, 44 Caucasian and 125 mixed) and 2,613 controls (1638 Chinese, 77 Caucasian and 898 mixed). The EGF 61*G allele was significantly associated with increased risk of HCC based on allelic contrast (OR = 1.29, 95% CI = 1.16-1.44, p<0.001), homozygote comparison (OR = 1.79, 95% CI = 1.39-2.29, p<0.001) and a recessive genetic model (OR = 1.34, 95% CI = 1.16-1.54, p<0.001), while patients carrying the EGF 61*A/A genotype had significantly lower risk of HCC than those with the G/A or G/G genotype (A/A vs. G/A+G/G, OR = 0.66, 95% CI = 0.53-0.83, p<0.001). CONCLUSION: The 61*G polymorphism in EGF is a risk factor for hepatocarcinogenesis while the EGF 61*A allele is a protective factor. Further large and well-designed studies are needed to confirm this conclusion

Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method

<p>Abstract</p> <p>Background</p> <p>Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling.</p> <p>Results</p> <p>We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach.</p> <p>Conclusions</p> <p>Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors.</p