Sequence to function mapping of condensate proteomes (B3)

The goal of of B3 is to understand how condensate proteomes and their functions evolve. We aim to decipher which sequence perturbations can lead to gain/loss of condensates and tune specific molecular inter-actions within condensates in evolution and disease.

Extensive research on condensates focused on identifying their components, material proper-ties, and function. However, we still lack understanding of the mechanisms that target proteins into condensates and how condensates emerged during evolution. Building on the hypothesis that localization into condensates is encoded in protein sequences, we aim to advance the sequence-function mapping of condensate forming proteins and specifically intrinsically disordered regions (IDRs) that are common in condensates. Here, we will study not only genetic mutations but also phenotypic mutations that occur via transcription and translation errors, which we study in the Toth-Petroczy group. In collaboration with the Hyman group we built a condensate protein database that revealed wide-spread occurrence of condensates across the tree of life. Based on this curated data, we developed a machine learning algorithm to predict condensate proteins in any organism. Further, we have designed a suit of alignment-free algorithms to assess homology between unalignable IDR sequences (SHARK-dive) and developed a method to identify conserved motifs within a set of homologous IDR sequences (SHARK-capture). These tools allow us to address the origin and evolution of condensates and their proteomes, as well as to identify conserved and functional sequence features.

1. How do condensate proteomes change in evolution?
2. How do sequence perturbations impact condensate formation?

The evolution of condensate proteomes

The PhD students will be trained in data science, coding, machine learning, statistics, and image analysis, mass spectrometry, molecular biology, biochemistry and biophysics techniques in collaborations with experimental groups.

• Candidates have a Masters degree in biology, data science, or related fields
• Candidates should have a sound basis in computational or evolutionary biology, or closely related fields.
• Experience in coding and omics data analysis are expected

We are currently recruiting the first cohort of motivated doctoral candidates to join our research training group “RTG 3120 Biomolecular Condensates”. If you are a potential applicant, register and complete the following form . If you have questions about the research topic, then email the project supervisor >