Molecular Modeling of Protein Structure, Biology of Disease and Clinical Electroretinography in Human X-Linked Retinoschisis (XLRS)

We describe a promising approach using in silico structure-function studies of protein atomic structure and computational medicine to understand disease. In silico studies use an atomic structure of proteins and molecular modeling for structure-function analysis. This approach is critical for large-scale genetic studies in order to understand a possible functional role of genetic mutations. Indeed, structural changes associated with missense mutations might impact protein folding, protein-protein interaction sites, or solubility or stability of protein molecules. The structural effect of mutational changes can be analyzed in silico on the basis of 3-dimensional structure, multiple alignments of homologous sequences, molecular modeling, and molecular dynamics simulations. The parameters derived from 3dimensional protein structure could be used in clinical studies to predict a severity of protein structure-function changes caused by genetic mutations and evaluate genotype-tophenotype relationships. In this chapter, we use X-linked retinoschisis (XLRS) as our disease model. XLRS is a form of juvenile macular and retinal degeneration in which schisis or splitting within the retinal layers leads to early and progressive vision loss. XLRS is a rare disease estimated to affect 1:5000 males (George et al., 1995; Wang et al., 2002) and is a disease with considerable clinical and electrophysiological variation. Precise analysis of XLRS is pertinent to identify disease severity and genotype-phenotype correlation. Due to the lack of a protein assay, a correlation between phenotype and genotype is difficult. Advances in molecular modeling give new insight to mutation severity at atomic level and provide a possible connection between genotype-phenotype correlations. This creates the hope that disease risk assessment at the atomic level will be a reality in the future. For our study, we use the electroretinogram (ERG) for our phenotypical data set, which we correlated with expected mutation severity.