From ScienceDaily:
Reconstructing evolutionary branches is tricky, especially when many species share a similar type of protein that might have evolved to perform somewhat different functions. Mathematically, the problem quickly becomes very big, but discovering the implications of this protein evolution could lead to a better understanding of how our bodies deal with cancer and other diseases.
The solution to the problem came to Sloutsky while he was studying an important protein in cell signaling common across many different species. He wanted to know how the protein had evolved over time to have different functions in different species. The question was so big, he decided to sample just a few sequences to reconstruct the evolutionary divergence.
“The reconstructions didn’t agree with each other,” he said, despite 1,000 attempts. “That in itself wouldn’t be a huge problem — I didn’t expect them all to agree. But I expected one model to be repeated most of the time, or at least a lot of the time.”
Surprised, he decided to see what all the disagreeing models had in common. “I knew I would have to come up with some way to combine information from all those models, because I couldn’t just use the most common one,” he said. “It was sort of an unexpected challenge that arose and led to this work.”
Over the course of several months refining software and testing on larger and larger reconstruction problems looking at proteins, Naegle and Sloutsky were able to create open-source software that can combine multiple models to very accurately reconstruct evolutionary changes.
“Everything our bodies do is done by proteins,” Sloutsky said. “This is a powerful tool to understand how molecular biology works, how proteins work and when things go wrong, how they go wrong.” Paper. (open access) – Roman Sloutsky, Kristen M Naegle. ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models. eLife, 2019; 8 DOI: 10.7554/life.47676 More.
This approach might be an improvement but a lot depends on the quality of the input.
See also: Researchers: Big Data Shows Math Laws That Underlie Life’s Unity (This seems like a more productive approach.)