Characterizing the fitness landscape on population and global scales

Informations projet

CharFL

N° de convention de subvention: 771209

DOI

10.3030/771209

Projet clôturé le 30 Juin 2022

Date de signature de la CE 23 Mai 2018

Date de début 1 Janvier 2019

Date de fin 31 Decembre 2023

Financé au titre de

• EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 1 998 280,00

Contribution de l’UE

€ 1 998 280,00

Coordonné par

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

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

Période du rapport: 2022-01-01 au 2023-06-30

Résumé du contexte et des objectifs généraux du projet

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.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

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.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

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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