In recent months, the process of protein design at UW has been revolutionized by the implementation of a machine learning computational approach. In a new paper published in the journal Nature Computational Science, the UW molecular design Berndt Lab reports its findings.

Machine learning, recently applied to the realm of protein engineering, has been effective in reducing the amount of time needed to design proteins that can efficiently perform a biochemical task. The current trial-and-error method of mutating an amino acid sequence can take anywhere from several months to upward of years of tedious analysis. However, with the recent use of machine learning at the Berndt Lab, the future of protein engineering appears promising.

The application of machine learning was used to analyze how mutations to GCaMP, a biosensor that tracks calcium in cells, would affect its behavior. Collaborators provided empirical knowledge of GCaMP, which was then combined with an AI algorithm that could predict the effects of the protein mutations. Well-developed proteins can provide valuable insight to disease and a patients response to treatment.

The machine learning model achieved the equivalent of several years worth of lab mutations in a single night, with a very high rate of success. Of the 17 mutations implemented in real biological cells, five or six were absolute successes. According to Andre Berndt, assistant professor in the department of bioengineering and senior author on the paper, out of 10 mutations you are typically lucky if just one provides a gain of function.

A lot of the mutations that were predicted to be better were indeed better at a much, much faster pace from a much larger pool of virtually tested mutations, Berndt said. So this was a very efficient process just based on the trained model.

Berndts team was comprised of graduate and undergraduate students who collaborated on the study. Lead author Sarah Wait, a Ph.D. candidate in molecular engineering,spearheaded the research by undertaking various roles such as testing mutation variants, engineering data, establishing the machine learning framework, and analyzing the results.

Computational programs can discover all of the really hard-to-observe patterns that, maybe, we wouldnt be able to observe ourselves, Wait said. It's just a really great tool to help us as the researcher[s] discover these really small patterns that may be hidden to us given the amount of data we have to look at in order to actually see them.

Reach contributing writer Ashley Ingalsbe at news@dailyuw.com X: @ashleyiing

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Study uses AI and machine learning to accelerate protein engineering process - Dailyuw