Characterizing the fitness landscape on population and global scales

Informacje na temat projektu

CharFL

Identyfikator umowy o grant: 771209

DOI

10.3030/771209

Realizacja projektu zakończyła się 30 Czerwca 2022

Data podpisania przez KE 23 Maja 2018

Data rozpoczęcia 1 Stycznia 2019

Data zakończenia 31 Grudnia 2023

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 998 280,00

Wkład UE

€ 1 998 280,00

Koordynowany przez

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

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

Okres sprawozdawczy: 2022-01-01 do 2023-06-30

Podsumowanie kontekstu i ogólnych celów projektu

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.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

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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