Silico algorithms

In summary, in silico algorithms tend to be overpredictive and are still not accurate enough to predict primarily the relevant immunodominant T cell epitopes that may trigger immunogenicity in vivo. However, in silico algorithms may be used as a supplementary tool to verify T cell epitopes identified by alternative approaches described below. 

Fig. 8. Reconstruction of Young s modulus map in a simulated object. A 3D breast phantom was first designed in silico from MR anatomical images. Then a given 3D Young s modulus distribution was supposed with a 1 cm diameter stiff inclusion of 200 kPa (A). The forward problem was the computing of the 3D-displacement field using the partial differential equation [Eq. (5)]. The efficiency of the 3D reconstruction (inverse problem) of the mechanical properties from the 3D strain data corrupted with 15% added noise can be assessed in (B). The stiff inclusion is detected by the reconstruction algorithm, but its calculated Young s modulus is about 130 kPa instead of 200 kPa. From Ref. 44, reprinted by permission of Wiley-Liss, Inc., a subsidiary of John Wiley Sons, Inc.

This chapter aims to present a very practical description of how to set up in silico docking experiments. Many problems and pitfalls that can be encountered during these experiments will be discussed. Because a thorough discussion of the theoretical background of the algorithms is beyond the scope of this chapter, references to important papers describing fundamental aspects are specifically mentioned in the text. 

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). 

The contribution of in silico models to vaccine development comprises algorithms for accelerated in silico identification of relevant protein candidates in silico design of novel immunogens with improved expression, safety, and immunogenicity profiles and in silico design of nucleic acid-based, vectored, or live attenuated vaccines. In small molecule development, in silico models play a major role in comparative genomics, whole genome analysis,... 

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. 

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