A theoretical model of the MPO-like domain has been reported [52] and was made available to us. protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was NMDA also demonstrated. Conclusion PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at http://diagtools.sysdiag.cnrs.fr/PEPOP/. Background In antibody-antigen (Ab-Ag) interactions, the paratope of the Ab binds to the epitope of the Ag. The identification of epitopes is an important step for understanding molecular recognition rules and is also helpful for diagnosis of diseases and for drug and vaccine Rabbit Polyclonal to ADCK4 design. The ultimate method to precisely define an epitope is to solve the 3D structure of the Ab-Ag complex either by X-ray crystallography or NMR [1]. These techniques are, however, demanding and generally time-consuming. Faster epitope identification methods have been described such as site-directed mutagenesis of the Ag [2,3]. Another popular approach to map an epitope is parallel peptide synthesis [4,5], based on the synthesis of overlapping peptides covering the entire Ag sequence. In this case, mainly continuous (sequential or linear) epitopes can be identified. Screening chemical or biological combinatorial libraries [6] for Ab binders allows selection of peptides also called mimotopes [7], mimicking more or less faithfully the epitope. Bioinformatics tools have been developed to help experimentalists in localizing the epitope by NMDA the sequence analysis of the selected mimotopes [8,9]. Synthetic peptides are commonly used as immunogens to raise anti-peptide Abs that may cross-react with proteins [10], thus allowing their detection and quantification. These peptides are generally designed by using methods that attempt to predict antigenic determinants of a protein. Numerous algorithms have been developed over the past 25 years. They are based on different theoretical physicochemical characteristics of the target protein such as hydrophilicity, flexibility, accessibility, and secondary structure, especially turns [11]. Other methods are combinations of the latter approaches [12], the most recent [13] being an extension and combination of the methods of Parker em et al /em . [14] and Jameson and Wolf [15]. Likewise, Welling em et al /em . [16] developed an antigenicity scale, with the aim of predicting antigenic regions and synthesizing the corresponding antigenic peptides to elicit Abs reactive with the intact protein. All these algorithms have led to the development of several softwares or web interfaces that make the use of such methods very easy. It is, however, difficult to assess the efficacy of all predictive methods. A comparative study published some years ago [11,17] indicated that the most accurate predictive method at that time is based on the prediction of turns. This method was implemented in BEPITOPE [18]. A more recent and more exhaustive comparative study [19] concluded that the methods based on sequence analysis do not predict epitopes better than chance. All these methods predict antigenic determinants from the protein sequence alone, neglecting 3D structure information. This is surprising because the 3D structure of an increasing number of proteins has been solved by X-ray crystallography or NMR, and predictive modeling methods are available that show increasing accuracy [20]. Recently, however, a few recent studies [21-24] propose bioinformatics tools based on 3D information to predict epitopes. In this article, we describe PEPOP, an algorithm that makes use of the 3D information of a protein to predict peptides which could serve as immunogens to raise site-specific anti-protein Abs. Clusters NMDA of surface accessible segments.