Dynamically-Driven Inactivation of the Catalytic Machinery of the SARS 3C-Like Protease by the N214A Mutation on the Extra Domain

Despite utilizing the same chymotrypsin fold to host the catalytic machinery, coronavirus 3C-like proteases (3CLpro) noticeably differ from picornavirus 3C proteases in acquiring an extra helical domain in evolution. Previously, the extra domain was demonstrated to regulate the catalysis of the SARS-CoV 3CLpro by controlling its dimerization. Here, we studied N214A, another mutant with only a doubled dissociation constant but significantly abolished activity. Unexpectedly, N214A still adopts the dimeric structure almost identical to that of the wild-type (WT) enzyme. Thus, we conducted 30-ns molecular dynamics (MD) simulations for N214A, WT, and R298A which we previously characterized to be a monomer with the collapsed catalytic machinery. Remarkably, three proteases display distinctive dynamical behaviors. While in WT, the catalytic machinery stably retains in the activated state; in R298A it remains largely collapsed in the inactivated state, thus implying that two states are not only structurally very distinguishable but also dynamically well separated. Surprisingly, in N214A the catalytic dyad becomes dynamically unstable and many residues constituting the catalytic machinery jump to sample the conformations highly resembling those of R298A. Therefore, the N214A mutation appears to trigger the dramatic change of the enzyme dynamics in the context of the dimeric form which ultimately inactivates the catalytic machinery. The present MD simulations represent the longest reported so far for the SARS-CoV 3CLpro, unveiling that its catalysis is critically dependent on the dynamics, which can be amazingly modulated by the extra domain. Consequently, mediating the dynamics may offer a potential avenue to inhibit the SARS-CoV 3CLpro

Partially glycosylated dendrimers block MD-2 and prevent TLR4-MD-2-LPS complex mediated cytokine responses.

The crystal structure of the TLR4-MD-2-LPS complex responsible for triggering powerful pro-inflammatory cytokine responses has recently become available. Central to cell surface complex formation is binding of lipopolysaccharide (LPS) to soluble MD-2. We have previously shown, in biologically based experiments, that a generation 3.5 PAMAM dendrimer with 64 peripheral carboxylic acid groups acts as an antagonist of pro-inflammatory cytokine production after surface modification with 8 glucosamine molecules. We have also shown using molecular modelling approaches that this partially glycosylated dendrimer has the flexibility, cluster density, surface electrostatic charge, and hydrophilicity to make it a therapeutically useful antagonist of complex formation. These studies enabled the computational study of the interactions of the unmodified dendrimer, glucosamine, and of the partially glycosylated dendrimer with TLR4 and MD-2 using molecular docking and molecular dynamics techniques. They demonstrate that dendrimer glucosamine forms co-operative electrostatic interactions with residues lining the entrance to MD-2's hydrophobic pocket. Crucially, dendrimer glucosamine interferes with the electrostatic binding of: (i) the 4'phosphate on the di-glucosamine of LPS to Ser118 on MD-2; (ii) LPS to Lys91 on MD-2; (iii) the subsequent binding of TLR4 to Tyr102 on MD-2. This is followed by additional co-operative interactions between several of the dendrimer glucosamine's carboxylic acid branches and MD-2. Collectively, these interactions block the entry of the lipid chains of LPS into MD-2's hydrophobic pocket, and also prevent TLR4-MD-2-LPS complex formation. Our studies have therefore defined the first nonlipid-based synthetic MD-2 antagonist using both animal model-based studies of pro-inflammatory cytokine responses and molecular modelling studies of a whole dendrimer with its target protein. Using this approach, it should now be possible to computationally design additional macromolecular dendrimer based antagonists for other Toll Like Receptors. They could be useful for treating a spectrum of infectious, inflammatory and malignant diseases

C-reactive protein level is a prognostic indicator for survival and improves the predictive ability of the R-IPI score in diffuse large B-cell lymphoma patients

BACKGROUND: High levels of C-reactive protein (CRP), an acute phase protein, proofed being associated with decreased clinical outcome in small-scale studies in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to evaluate the prognostic impact of pretreatment CRP levels on overall survival (OS) and disease-free survival (DFS) in a large bicentre study of DLBCL patients. METHODS: Data from 477 DLBCL patients, diagnosed and treated between 2004 and 2013 at two Austrian centres, were evaluated retrospectively. The prognostic influence of CRP and other factors, including age, tumour stage, and revised International Prognostic Index (R-IPI) on 5-year OS and 5-year DFS, were studied by Kaplan–Meier curves as well as univariate and multivariate Cox regression models. Influence of CRP on the predictive accuracy of the R-IPI score was determined by the Harrell concordance index. RESULTS: Kaplan–Meier curves revealed elevated CRP as a factor for decreased 5-year OS and DFS in DLBCL patients (P<0.001, log-rank test). An independent significant association between high CRP levels and poor clinical outcome in multivariate analysis for 5-year OS (HR=1.51, CI 95%=1.04–2.20, P=0.031) and for DFS (HR=1.91, CI 95%=1.28–2.85, P=0.002) was found. The estimated concordance index was 0.75 using the original R-IPI score and 0.79 when CRP was added. CONCLUSIONS: In the present study, we demonstrated high CRP levels at diagnosis of DLBCL as an independent poor prognostic factor for clinical outcome. Adding CRP to the well-established prognostic models such as the R-IPI score might improve their predictive ability

The complexities of knowing what it is you are trapping

The effectiveness of any trapping system is highly dependent on the ability to accurately identify the specimens collected. For many fruit fly species, accurate identification (= diagnostics) using morphological or molecular techniques is relatively straightforward and poses few technical challenges. However, nearly all genera of pest tephritids also contain groups of species where single, stand-alone tools are not sufficient for accurate identification: such groups include the Bactrocera dorsalis complex, the Anastrepha fraterculus complex and the Ceratitis FAR complex. Misidentification of high-impact species from such groups can have dramatic consequences and negate the benefits of an otherwise effective trapping program. To help prevent such problems, this chapter defines what is meant by a species complex and describes in detail how the correct identification of species within a complex requires the use of an integrative taxonomic approach. Integrative taxonomy uses multiple, independent lines of evidence to delimit species boundaries, and the underpinnings of this approach from both the theoretical speciation literature and the systematics/taxonomy literature are described. The strength of the integrative approach lies in the explicit testing of hypotheses and the use of multiple, independent species delimitation tools. A case is made for a core set of species delimitation tools (pre- and post-zygotic compatibility tests, multi-locus phylogenetic analysis, chemoecological studies, and morphometric and geometric morphometric analyses) to be adopted as standards by tephritologists aiming to resolve economically important species complexes. In discussing the integrative approach, emphasis is placed on the subtle but important differences between integrative and iterative taxonomy. The chapter finishes with a case study that illustrates how iterative taxonomy applied to the B. dorsalis species complex led to incorrect taxonomic conclusions, which has had major implications for quarantine, trade, and horticultural pest management. In contrast, an integrative approach to the problem has resolved species limits in this taxonomically difficult group, meaning that robust diagnostics are now available