A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

For the design of potent subunit vaccines, it is of paramount importance to identify all antigens immunologically recognized by a patient population infected with a pathogen. We have developed a rapid and efficient procedure to identify such commonly recognized antigens, and here we provide a comprehensive in vivo antigenic profile of Staphylococcus aureus, an important human pathogen. S. aureus peptides were displayed on the surface of Escherichia coli via fusion to one of two outer membrane proteins (LamB and FhuA) and probed with sera selected for high Ab titer and opsonic activity. A total of 60 antigenic proteins were identified, most of which are located or predicted to be located on the surface of the bacterium or secreted. The identification of these antigens and their reactivity with individual sera from patients and healthy individuals greatly facilitate the selection of promising vaccine candidates for further evaluation. This approach, which makes use of whole genome sequence information, has the potential to greatly accelerate and facilitate the formulation of novel vaccines and is applicable to any pathogen that induces Abs in humans and͞or experimental animals.

Based on integration site preferences, retroviruses can be placed into three groups. Viruses that comprise the first group, murine leukemia virus and foamy virus, integrate preferentially near transcription start sites. The second group, notably human immunodeficiency virus and simian immunodeficiency virus, preferentially targets transcription units. Avian sarcoma-leukosis virus (ASLV) and human T-cell leukemia virus (HTLV), forming the third group, show little preference for any genomic feature. We have previously shown that some human cells sustain mouse mammary tumor virus (MMTV) infection; therefore, we infected a susceptible human breast cell line, Hs578T, and, without introducing a species-specific bias, compared the MMTV integration profile to those of other retroviruses. Additionally, we infected a mouse cell line, NMuMG, and thus we could compare MMTV integration site selection in human and mouse cells. In total, we examined 468 unique MMTV integration sites. Irrespective of whether human or mouse cells were infected, no integration bias favoring transcription start sites was detected, a profile that is reminiscent of that of ASLV and HTLV. However, in contrast to ASLV and HTLV, not even a modest tendency in favor of integration within genes was observed. Similarly, repetitive sequences and genes that are frequently tagged by MMTV in mammary tumors were not preferentially targeted in cell culture either in mouse or in human cells; hence, we conclude that MMTV displays the most random dispersion of integration sites among retroviruses determined so far.

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.