Characterizing the fitness landscape on population and global scales

Project Information

CharFL

Grant agreement ID: 771209

DOI

10.3030/771209

Project terminated on 30 June 2022

EC signature date 23 May 2018

Start date 1 January 2019

End date 31 December 2023

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 998 280,00

EU contribution

€ 1 998 280,00

Coordinated by

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

Periodic Reporting for period 3 - CharFL (Characterizing the fitness landscape on population and global scales)

Reporting period: 2022-01-01 to 2023-06-30

Summary of the context and overall objectives of the project

Can two negative mutations make a positive impact? What about many negative mutations? What about a mix of negative and positive mutations? What are the rules governing their interactions? These are the types of questions we aimed to address in the course of this project. For our work, we focused on on the study of the interaction of thousands of mutations in different contexts. We were looking at random mutations that influence the function of a specific protein. We then took what we learned from our experiments and made an effort to create computation and mathematical tools that would allow us to make simple predictions for different proteins.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The project revolved around three research directions. First, we created many thousands of artificial mutations and gene combinations. We then investigated the nature of how mutations interact. The large libraries of mutations were tested in the lab, which allowed us to look at the impact of many different combinations of mutations. Using this data, we considered several theoretical approaches to see if we can detect any general rules about their interaction. In parallel, we have studied the interaction of mutations that have been undergoing evolutionary change in standing populations.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have achieved an understanding of how mutations interact. We managed to utilized a machine learning approach that consolidates large scale information on the interactions of mutations and is capable of predicting novel functional sequences solely based on the information on interactions. These results will pave the way for better protein design tools and a generalizable understanding of protein evolution.

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