Biomedical subjects

Alan S Lapedes

Publications and source records attributed to Alan S Lapedes.

4 recordsLinked to original sources

PubMed2004

Mapping the antigenic and genetic evolution of influenza virus.

The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.

Amino Acid Substitution↗

PubMed2004

Recognition of homo- and heterosubtypic variants of influenza A viruses by human CD8+ T lymphocytes.

In the present study, the recognition of epitope variants of influenza A viruses by human CTL was investigated. To this end, human CD8(+) CTL clones, specific for natural variants of the HLA-B*3501-restricted epitope in the nucleoprotein (NP(418-426)), were generated. As determined in (51)Cr release assays and by flow cytometry with HLA-B*3501-peptide tetrameric complexes, CTL clones were found to be specific for epitopes within one subtype or cross-reactive with heterosubtypic variants of the epitope. Using eight natural variants of the epitope, positions in the 9-mer important for T cell recognition and involved in escape from CTL immunity were identified and visualized using multidimensional scaling. It was shown that positions 4 and 5 in the 9-mer epitope were important determinants of T cell specificity. The in vivo existence of CD8(+) cells cross-reactive with homo- and heterosubtypic variants of the epitope was further confirmed using polyclonal T cell populations obtained after stimulation of PBMC with different influenza A viruses. Based on the observed recognition patterns of the clonal and polyclonal T cell populations and serology, it is hypothesized that consecutive infections with influenza viruses containing different variants of the epitope select for cross-reactive T cells in vivo.

Adult↗

PubMed2002

Probabilistic code for DNA recognition by proteins of the EGR family.

A recognition code for protein-DNA interactions would allow for the prediction of binding sites based on protein sequence, and the identification of binding proteins for specific DNA targets. Crystallographic studies of protein-DNA complexes showed that a simple, deterministic recognition code does not exist. Here, we present a probabilistic recognition code (P-code) that assigns energies to all possible base-pair-amino acid interactions for the early growth response factor (EGR) family of zinc-finger transcription factors. The specific energy values are determined by a maximum likelihood method using examples from in vitro randomisation experiments (namely, SELEX and phage display) reported in the literature. The accuracy of the model is tested in several ways, including the ability to predict in vivo binding sites of EGR proteins and other non-EGR zinc-finger proteins, and the correlation between predicted and measured binding affinities of various EGR proteins to several different DNA sites. We also show that this model improves significantly upon the prediction capabilities of previous qualitative and quantitative models. The probabilistic code we develop uses information about the interacting positions between the protein and DNA, but we show that such information is not necessary, although it reduces the number of parameters to be determined. We also employ the assumption that the total binding energy is the sum of the energies of the individual contacts, but we describe how that assumption can be relaxed at the cost of additional parameters.

Algorithms↗

PubMed2002

Is there a code for protein-DNA recognition? Probab(ilistical)ly. . .

Transcriptional regulation of all genes is initiated by the specific binding of regulatory proteins called transcription factors to specific sites on DNA called promoter regions. Transcription factors employ a variety of mechanisms to recognise their DNA target sites. In the last few decades, attempts have been made to describe these mechanisms by general sets of rules and associated models. We give an overview of these models, starting with a historical review of the somewhat controversial issue of a "recognition code" governing protein-DNA interaction. We then present a probabilistic framework in which advantages and disadvantages of various models can be discussed. Finally, we conclude that simplifying assumptions about additivity of interactions are sufficiently justified in many situations (and can be suitably extended in other situations) to allow a unifying concept of a "probabilistic code" for protein-DNA recognition to be defined.

Animals↗