Characterizing the fitness landscape on population and global scales

Información del proyecto

CharFL

Identificador del acuerdo de subvención: 771209

DOI

10.3030/771209

Proyecto finalizado el 30 Junio 2022

Fecha de la firma de la CE 23 Mayo 2018

Fecha de inicio 1 Enero 2019

Fecha de finalización 31 Diciembre 2023

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 998 280,00

Aportación de la UE

€ 1 998 280,00

Coordinado por

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

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

Período documentado: 2022-01-01 hasta 2023-06-30

Resumen del contexto y de los objetivos generales del proyecto

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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

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.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

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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