<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.     

Transduction of human dendritic cells with a recombinant modified vaccinia Ankara virus encoding MUC1 and IL-2

The epithelial mucin MUC1 is considered an opportune target antigen for cancer immunotherapy, as it is over-expressed and exhibits aberrant glycosylation in malignant cells. Because dendritic cells (DC) are powerful initiators of immune responses, efforts have focused on tumor antigen-bearing DC as potent cancer vaccines. In this study we have characterized the transduction of monocyte-derived DC with a highly attenuated vaccinia virus vector [modified vaccinia Ankara (MVA)] encoding human MUC1 and the immunostimulatory cytokine IL-2. Analysis of transduced DC cultures generated from a number of donors revealed MUC1 expression in the range of 27-54% of the cells and a co-regulated secretion of bioactive IL-2. As shown by FACS analysis with MUCI-specific antibodies, the MVA-MUC1/IL-2-transduced DC predominantly expressed the fully processed glycoform of MUC1, typical of that displayed by normal epithelia. Over a 3-day period after transduction, transgene expression declined concurrent with an increase in MVA-induced cytopathic effects. The transduced DC stimulated allogeneic lymphocyte proliferation, indicating that DC immunostimulatory function is not impaired by vector transduction. In the presence of IL-2, MVA-transduced DC were able to enhance autologous lymphocyte proliferation. Also, vector expression was analyzed in DC cultures treated with TNF-alpha, a known DC maturation factor. As indicated by the up-regulation of several DC maturation markers, neither virus infection nor transgene expression influenced the maturation capacity of the cells. The MVA-MUC1/IL-2 vector effectively transduced both immature and TNF-alpha-matured DC. Overall, our results are encouraging for the clinical application of MVA-MUC1/IL-2-transduced DC.