Characterizing the fitness landscape on population and global scales

Projektinformationen

CharFL

ID Finanzhilfevereinbarung: 771209

DOI

10.3030/771209

Das Projekt endete am 30 Juni 2022

EK-Unterschriftsdatum 23 Mai 2018

Startdatum 1 Januar 2019

Enddatum 31 Dezember 2023

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten

€ 1 998 280,00

EU-Beitrag

€ 1 998 280,00

Koordiniert durch

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

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

Berichtszeitraum: 2022-01-01 bis 2023-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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.

ist-logo-rgb.jpg