Research Groups

We study phenotypic plasticity of proteins, its mechanism, origin, evolutionary history, potential for innovations, and its role in diseases. We complement the increasing amount of high-throughput data with rigorous computational studies, and provide hypotheses for experimental validation.

Predicting phenotypic mutations

Transcriptional and translational errors generate diverse sequences, most dysfunctional, some harboring novel functions. We are developing an algorithm for predicting transcriptional and ribosomal slippage sites by training on known sites in transcriptomics and proteomics data and using evolutionary information. We aim at discovering novel frameshift and STOP codon read-through variants that we will validate by shotgun proteomics in collaboration with MPI-CBG Mass-spectrometry unit. We will explore the evolutionary potential of slippage sites and test whether they could lead to promiscuous functions.

Phenotypic variability: how do new functions emerge?

Evolution needs raw material for selection, for instance promiscuous function that is generated by phenotypic variability. We will continue exploring how new protein functions evolve by: i) functional characterization of the novel sequences generated by phenotypic mutations (predicted in the previous section); ii) studying disordered proteins, which can have different functions originating from different structural states. We previously found that co-evolutionary information can reveal the potential for functional structural states of disordered proteins that take on multiple conformations or fold only upon binding. We are further exploring this experimentally challenging class of proteins and predict their functional diversification and evolutionary origins using co-evolutionary and phylogenetic methods.

Quantifying protein plasticity 

We plan to unify our knowledge about genetic, phenotypic and environmental noise and quantify phenotypic plasticity from a systems perspective. We explore associations and epistatic relationships between the different types of phenotypic mutations. Predicting the fitness effects of mutations is a long-standing challenge. We will incorporate the phenotypic plasticity score to create an algorithm to predict the effects of mutations. We will provide an accessible database and tool for the biomedical community as well as seek collaboration with genetic diagnostics researchers. We hope to transform disease gene prioritization for Mendelian gene discovery, as more than 4000 Mendelian phenotypes still lack an associated gene despite worldwide efforts.