Protein sequence-based predictions

Ph.D. job offer: Predicting biophysical characteristics of proteins from their amino acid sequence

ESR 1: Protein sequence-based predictions

In this Ph.D. project, you will develop prediction approaches that are based on estimations of dynamics and conformation directly from experimental NMR data. These estimations are less accurate but can encompass the whole range of protein behaviour, from intrinsically disorded via molten globule to fully folded. We have evidence that the predictions add a new, physical, dimension to the protein sequence that has many uses. In this project we will apply this approach to the design of RRM proteins, especially to encompass their dynamics.

You will spend a 3 month of secondment at the CSIC (Valencia, Spain) to learn about incorporating molecular information in pathway modelling for synthetic biology.

The project is highly interdisciplinary. Good programming skills (preferentially Python and/or C++) are essential, with knowledge of machine learning/artificial intelligence very desirable, and skills in discrete mathematics and statistics much appreciated. A background knowledge of (structural) biology is a bonus. Candidates must be motivated to learn about all disciplines relevant to the project.

Candidates must be fluent in English.

More detailed information available here.

Funding

H2020-MSCA-ITN-2018 813239 (Jan. 1, 19-Dec. 31, 23)
RNAct - Enabling proteins with RNA recognition motifs for synthetic biology and bio-analytics