Algorithm for predicting protein pairings could help show how living systems work

An algorithm which models how proteins inside cells interact with each other will enhance the study of biology, and sheds light on how proteins work together to complete tasks such as turning food into energy. Being able to predict these interactions will help us understand how proteins fit and work together to complete required tasks. Lucy Colwell Researchers have developed an algorithm that aids our understanding of how living systems work, by identifying which proteins within cells will interact with each other, based on their genetic sequences alone. The ability to generate huge amounts of data from genetic sequencing has developed rapidly in the past decade, but the trouble for researchers is in being able to apply that sequence data to better understand living systems. The new research , published in the journal Proceedings of the National Academy of Sciences , is a significant step forward because biological processes, such as how our bodies turn food into energy, are driven by specific protein-protein interactions. 'We were really surprised that our algorithm was powerful enough to make accurate predictions in the absence of experimentally-derived data,' said study co-author Dr Lucy Colwell, from the University of Cambridge's Department of Chemistry, who led the study with Ned Wingreen of Princeton University. 'Being able to predict these interactions will help us understand how proteins fit and work together to complete required tasks - and using an algorithm is much faster and much cheaper than relying on experiments.' When proteins interact with each other, they stick together to form protein complexes.