Cloaked similarity between HIV-1 and SARS-CoV suggests an anti-SARS strategy

BACKGROUND: Severe acute respiratory syndrome (SARS) is a febrile respiratory illness. The disease has been etiologically linked to a novel coronavirus that has been named the SARS-associated coronavirus (SARS-CoV), whose genome was recently sequenced. Since it is a member of the Coronaviridae, its spike protein (S2) is believed to play a central role in viral entry by facilitating fusion between the viral and host cell membranes. The protein responsible for viral-induced membrane fusion of HIV-1 (gp41) differs in length, and has no sequence homology with S2. RESULTS: Sequence analysis reveals that the two viral proteins share the sequence motifs that construct their active conformation. These include (1) an N-terminal leucine/isoleucine zipper-like sequence, and (2) a C-terminal heptad repeat located upstream of (3) an aromatic residue-rich region juxtaposed to the (4) transmembrane segment. CONCLUSIONS: This study points to a similar mode of action for the two viral proteins, suggesting that anti-viral strategy that targets the viral-induced membrane fusion step can be adopted from HIV-1 to SARS-CoV. Recently the FDA approved Enfuvirtide, a synthetic peptide corresponding to the C-terminal heptad repeat of HIV-1 gp41, as an anti-AIDS agent. Enfuvirtide and C34, another anti HIV-1 peptide, exert their inhibitory activity by binding to a leucine/isoleucine zipper-like sequence in gp41, thus inhibiting a conformational change of gp41 required for its activation. We suggest that peptides corresponding to the C-terminal heptad repeat of the S2 protein may serve as inhibitors for SARS-CoV entry

Towards Alignment Independent Quantitative Assessment of Homology Detection

Identification of homologous proteins provides a basis for protein annotation. Sequence alignment tools reliably identify homologs sharing high sequence similarity. However, identification of homologs that share low sequence similarity remains a challenge. Lowering the cutoff value could enable the identification of diverged homologs, but also introduces numerous false hits. Methods are being continuously developed to minimize this problem. Estimation of the fraction of homologs in a set of protein alignments can help in the assessment and development of such methods, and provides the users with intuitive quantitative assessment of protein alignment results. Herein, we present a computational approach that estimates the amount of homologs in a set of protein pairs. The method requires a prevalent and detectable protein feature that is conserved between homologs. By analyzing the feature prevalence in a set of pairwise protein alignments, the method can estimate the number of homolog pairs in the set independently of the alignments' quality. Using the HomoloGene database as a standard of truth, we implemented this approach in a proteome-wide analysis. The results revealed that this approach, which is independent of the alignments themselves, works well for estimating the number of homologous proteins in a wide range of homology values. In summary, the presented method can accompany homology searches and method development, provides validation to search results, and allows tuning of tools and methods

Signal peptide is a conserved feature of homologous proteins.

<p>Homologous proteins of human/mouse, human/fruit fly, and human/C. elegans were extracted from HomoloGene triplets. The proportion of matching protein pairs, in which both proteins have or lack a signal peptide, was calculated for each pair of organisms (gray columns). For comparison, the expected proportions (black columns), is calculated under the assumption that signal peptide is not conserved between homologs, using the prevalence of proteins having signal peptides in each organism.</p

Improving Classical Substructure-Based Virtual Screening to Handle Extrapolation Challenges

Target-oriented substructure-based virtual screening (sSBVS) of molecules is a promising approach in drug discovery. Yet, there are doubts whether sSBVS is suitable also for extrapolation, that is, for detecting molecules that are very different from those used for training. Herein, we evaluate the predictive power of classic virtual screening methods, namely, similarity searching using Tanimoto coefficient (MTC) and Naive Bayes (NB). As could be expected, these classic methods perform better in interpolation than in extrapolation tasks. Consequently, to enhance the predictive ability for extrapolation tasks, we introduce the Shadow approach, in which inclusion relations between substructures are considered, as opposed to the classic sSBVS methods that assume independence between substructures. Specifically, we discard contributions from substructures included in (“shaded” by) others which are, in turn, included in the molecule of interest. Indeed, the Shadow classifier significantly outperforms both MTC (<i>pValue</i> = 3.1 × 10^–16) and NB (<i>pValue</i> = 3.5 × 10^–9) in detecting hits sharing low similarity with the training active molecules

Robustness of the <i>Fhom Estimator.</i>

<p> <i>Fhom</i> was estimated to be 94% for HomoloGene human/mouse pairs. Next, random protein pairs were added according to the original signal peptide prevalence of these organisms. A linear regression curve and the coefficient of determination (R²) confirm that the <i>Fhom Estimator</i> has a wide dynamic range, and is robust to variation in signal-to-noise ratio.</p

Alignment independence of the <i>Fhom Estimator.</i>

<p>HomoloGene homologous protein pairs were divided into five groups according to the identity level of their alignment. The organisms used are human, mouse, fruit fly and C. elegans. The figure reveals that the fraction of homologs estimation (Fhom) is applicable for both closely-related and distantly-related protein pairs.</p

Gp96 Peptide Antagonist gp96-II Confers Therapeutic Effects in Murine Intestinal Inflammation

Background The expression of heat shock protein gp96 is strongly correlated with the degree of tissue inflammation in ulcerative colitis and Crohn’s disease, thereby leading us to the hypothesis that inhibition of expression via gp96-II peptide prevents intestinal inflammation. Methods We employed daily injections of gp96-II peptide in two murine models of intestinal inflammation, the first resulting from five daily injections of IL-12/IL-18, the second via a single intrarectal application of TNBS (2,4,6-trinitrobenzenesulfonic acid). We also assessed the effectiveness of gp96-II peptide in murine and human primary cell culture. Results In the IL-12/IL-18 model, all gp96-II peptide-treated animals survived until day 5, whereas 80% of placebo-injected animals died. gp96-II peptide reduced IL-12/IL-18-induced plasma IFNγ by 89%, IL-1β by 63%, IL-6 by 43% and tumor necrosis factor (TNF) by 70% compared to controls. The clinical assessment Disease Activity Index of intestinal inflammation severity was found to be significantly lower in the gp96-II-treated animals when compared to vehicle-injected mice. gp96-II peptide treatment in the TNBS model limited weight loss to 5% on day 7 compared with prednisolone treatment, whereas placebo-treated animals suffered a 20% weight loss. Histological disease severity was reduced equally by prednisolone (by 40%) and gp96-II peptide (35%). Mice treated with either gp96-II peptide or prednisolone exhibited improved endoscopic scores compared with vehicle-treated control mice: vascularity, fibrin, granularity, and translucency scores were reduced by up to 49% by prednisolone and by up to 30% by gp96-II peptide. In vitro, gp96-II peptide reduced TLR2-, TLR4- and IL-12/IL-18-induced cytokine expression in murine splenocytes, with declines in constitutive IL-6 (54%), lipopolysaccharide-induced TNF (48%), IL-6 (81%) and in Staphylococcus epidermidis-induced TNF (67%) and IL-6 (81%), as well as IL-12/IL-18-induced IFNγ (75%). gp96-II peptide reduced IL–1β, IL-6, TNF and GM-CSF in human peripheral blood mononuclear cells to a similar degree without affecting cell viability, whereas RANTES, IL-25 and MIF were twofold to threefold increased. Conclusion gp96-II peptide protects against murine intestinal inflammation by regulating inflammation in vivo and in vitro, pointing to its promise as a novel treatment for inflammatory bowel disease