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The thesis discusses the challenges faced by methods which predict the effect of protein sequence variants. The analyses illustrate that methods are biased towards deleterious, high effect variants which exhibit strong sequence conservation. Furthermore, variants with beneficial effect are generally neglected. Overall, no method performs best under all circumstances. Accounting for deficiencies will allow methods to support clinical action and drive large-scale adoption of precision medicine. Diese Arbeit befasst sich mit Herausforderungen für Methoden, die den Effekt von Proteinsequenzvarianten vorhersagen. Die Analysen zeigen, dass sie dazu neigen, Varianten mit starkem, schädlichem Effekt zu detektieren. Des Weiteren werden Varianten mit positivem Effekt generell vernachlässigt. Keine einzelne Methode zeigt unter allen Umständen die beste Leistung. Zukünftige Anstrengungen werden klinische Anwendungen und damit die umfassende Einführung von Präzisionsmedizin unterstützen.

Any two unrelated individuals differ by about 10,000 single amino acid variants (SAVs). Do these impact molecular function? Experimental answers cannot answer comprehensively, while state-of-the-art prediction methods can. We predicted the functional impacts of SAVs within human and for variants between human and other species. Several surprising results stood out. Firstly, four methods (CADD, PolyPhen-2, SIFT, and SNAP2) agreed within 10 percentage points on the percentage of rare SAVs predicted with effect. However, they differed substantially for the common SAVs: SNAP2 predicted, on average, more effect for common than for rare SAVs. Given the large ExAC data sets sampling 60,706 individuals, the differences were extremely significant (p-value

Additional file 1. Supporting Online Material (SOM) containing additional figures, tables and notes.

Additional file 1 Additional figures and tables referenced in the article.

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease., Author Summary The variations in the genetic sequence between individuals affect the gene-product, i.e. the protein differently. Some variants have no measurable effect (are neutral), while others affect protein function. Some of those effects are so severe they cause so called monogenic Mendelian diseases, i.e. diseases triggered by a single letter change. Some in silico methods predict the molecular impact of sequence variation. However, both experimental and computational analyses struggle to generalize from the effect upon molecular protein function to the effect upon the organism such as a disease. Here, we confirmed that methods predicting molecular effects correctly capture the type of effects causing Mendelian diseases in human and introduced a data set for animal diseases that was also captured by predictions methods. Predicted effects were less when in silico testing human variants in an animal model (here mouse). This is important to know because “mouse models” are common to study human diseases. Overall, we provided some evidence for a link between the molecular level and some type of disease.