生化学と生理学: オープンアクセス

抽象的な

Common Pitfalls and Novel Opportunities for Predicting Variant Pathogenicity

Tom van den Bergh, Bas Vroling, Remko KP Kuipers, Henk-Jan Joosten and Gert Vriend

The prediction of missense variant pathogenicity is normally performed using analyses of multiple sequence alignments optionally augmented with analyses of the (predicted) protein structure. The most straightforward way, though, is to search the literature to see whether this variant has already been described. Variant data from homologous proteins are also valuable because mutations in a homologous protein often have similar effects as mutations at the equivalent residues of the protein of interest. Transferring variant data seems trivial but is seriously hampered by the fact that homologous residue positions have different numbers in different species. This problem is even bigger when to proteins have such low sequence identities that they can no longer be aligned based on their sequences only and their structures need to be compared to align them accurately. The protein superfamily analysis software suite 3DM solves these problems, because 3DM is a system that combines high quality structure based multiple sequence alignments in which aligned residues have the same number, with all published mutant and variant data for human and all other species. We have used 3DM to analyze nine human proteins for which many disease-related variants are known. This study reveals that mutation data can be transferred even between very distant homologous proteins. Thus, protein superfamily information systems, such as 3DM, offer a wealth of unused information that can be used in the analysis of human variants.