Silico prediction algorithms

In silico Prediction Algorithms. Prediction algorithms have been established that utilize either the knowledge derived from the potential of peptides to bind to HLA molecules or from a small number of X-ray structures of HLA-peptide complexes in combination with computer-based modeling (Van Walle... 

Extensive research has established the relationship between the extent of distribution of compounds and their physicochemical properties. With this information, Vdss can be quite successfully predicted using in silico models [27-30], In silico prediction of distribution is based on physicochemical properties that relates to passive transmembrane diffusion and tissue binding, and it only predicts Vdss. The other factors that contribute to distribution, such as transporter-mediated distribution, were not taken into account. These algorithms are based on the assumption that all compounds will dissolve in intra- and extracellular tissue water, and the unionized portion will partition into the neutral lipids and neutral phospholipids located within tissue cells. For compounds categorized as a strong base (at least one basic group (p/fa >7), an additional mechanism of electrostatic interaction with tissue acidic phospholipids is incorporated. Acids and weak bases are assumed... 

Updating and validating such algorithms and their databases are also critical aspects. At this time, the European Cooperation in the Field of Scientific and Technical Research (Project COST B15) had begun an independent evaluation of existing expert systems used in the in silico prediction of ADME properties, with a view of publishing a consensus paper. 

For decades researchers have been developing in silico models to minimize the number of experiments needed to identify or map the potential epitopes on the antigen surface. Because of the basic differences in the recognition of B- and T-cell epitopes, researchers have derived separate algorithms and tools for the two types of epitope. This chapter discusses only B-cell epitope prediction models (linear and conformational). Although they are not very different from basic B-cell epitope algorithms, T-cell epitope models have been reviewed in detail elsewhere (7, 8). 

A recent study by Marti et al. (2007) used an in silico motif-based allergenicity prediction protocol to generate a recombinant peptide which showed the same IgE-reactivity as shrimp full-length tropomyosin. This motif-generating algorithm may be used in the future to identify major IgE-binding structures of other coiled-coil proteins. 

---