Role of HIV-1 Gag domains in viral assembly

AbstractAfter entry of the human immunodeficiency virus type 1 (HIV-1) into T cells and the subsequent synthesis of viral products, viral proteins and RNA must somehow find each other in the host cells and assemble on the plasma membrane to form the budding viral particle. In this general review of HIV-1 assembly, we present a brief overview of the HIV life cycle and then discuss assembly of the HIV Gag polyprotein on RNA and membrane substrates from a biochemical perspective. The role of the domains of Gag in targeting to the plasma membrane and the role of the cellular host protein cyclophilin are also reviewed

A Novel Empirical Free Energy Function That Explains And Predicts Protein–Protein Binding Affinities

A free energy function can be defined as a mathematical expression that relates macroscopic free energy changes to microscopic or molecular properties. Free energy functions can be used to explain and predict the affinity of a ligand for a protein and to score and discriminate between native and non-native binding modes. However, there is a natural tension between developing a function fast enough to solve the scoring problem but rigorous enough to explain and predict binding affinities. Here, we present a novel, physics-based free energy function that is computationally inexpensive, yet explanatory and predictive. The function results from a derivation that assumes the cost of polar desolvation can be ignored and that includes a unique and implicit treatment of interfacial water-bridged interactions. The function was parameterized on an internally consistent, high quality training set giving R 2 =0.97 and Q 2 =0.91. We used the function to blindly and successfully predict binding affinities for a diverse test set of 31 wild-type protein–protein and protein–peptide complexes (R 2 =0.79, rmsd=1.2 kcal mol−1). The function performed very well in direct comparison with a recently described knowledge-based potential and the function appears to be transferable. Our results indicate that our function is well suited for solving a wide range of protein/peptide design and discovery problems

Evidence for a fence that impedes the diffusion of phosphatidylinositol 4,5-bisphosphate out of the forming phagosomes of macrophages

This is the publisher's version. Copyright 2011 by The American Society for Cell Biology.To account for the many functions of phosphatidylinositol 4,5-bisphosphate (PIP2), several investigators have proposed that there are separate pools of PIP2 in the plasma membrane. Recent experiments show the surface concentration of PIP2 is indeed enhanced in regions where phagocytosis, exocytosis, and cell division occurs. Kinases that produce PIP2 are also concentrated in these regions. However, how is the PIP2 produced by these kinases prevented from diffusing rapidly away? First, proteins could act as “fences” around the perimeter of these regions. Second, some factor could markedly decrease the diffusion coefficient, D, of PIP2 within these regions. We used fluorescence correlation spectroscopy (FCS) and fluorescence recovery after photobleaching (FRAP) to investigate these two possibilities in the forming phagosomes of macrophages injected with fluorescent PIP2. FCS measurements show that PIP2 diffuses rapidly (D ∼ 1 μm2/s) in both the forming phagosomes and unengaged plasma membrane. FRAP measurements show that the fluorescence from PIP2 does not recover (>100 s) after photobleaching the entire forming phagosome but recovers rapidly (∼10 s) in a comparable area of membrane outside the cup. These results (and similar data for a plasma membrane–anchored green fluorescent protein) support the hypothesis that a fence impedes the diffusion of PIP2 into and out of forming phagosomes

γ-Synuclein Interacts with Phospholipase Cβ2 to Modulate G Protein Activation

Phospholipase Cβ2 (PLC β2) is activated by G proteins and generates calcium signals in cells. PLCβ2 is absent in normal breast tissue, but is highly expressed in breast tumors where its expression is correlated with the progression and migration of the tumor. This pattern of expression parallels the expression of the breast cancer specific gene protein 1 which is also known as γ-synuclein. The cellular function of γ-synuclein and the role it plays in proliferation are unknown. Here, we determined whether γ-synuclein can interact with PLCβ2 and affect its activity. Using co-immunprecitation and co-immunofluorescence, we find that in both benign and aggressive breast cancer cell lines γ-synuclein and PLCβ2 are associated. In solution, purified γ-synuclein binds to PLCβ2 with high affinity as measured by fluorescence methods. Protease digestion and mass spectrometry studies show that γ-synuclein binds to a site on the C-terminus of PLCβ2 that overlaps with the Gαq binding site. Additionally, γ-synuclein competes for Gαq association, but not for activators that bind to the N-terminus (i.e. Rac1 and Gβγ). Binding of γ-synuclein reduces the catalytic activity of PLCβ2 by mechanism that involves inhibition of product release without affecting membrane interactions. Since activated Gαq binds more strongly to PLCβ2 than γ-synuclein, addition of Gαq(GTPγS) to the γ-synuclein –PLCβ2 complex allows for relief of enzyme inhibition along with concomitant activation. We also find that Gβγ can reverse γ-synuclein inhibition without dissociating the γ-synuclein- PLCβ2- complex. These studies point to a role of γ-synuclein in promoting a more robust G protein activation of PLCβ2

A three gene DNA methylation biomarker accurately classifies early stage prostate cancer

Background: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. Materials and methods: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. Results: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. Conclusion: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis