Newswise — Engineering proteins for desirable traits has been the holy grail of modern biotechnology. For example, the food industry can benefit from engineered enzymes which have the ability to enhance biochemical reactions at higher temperatures, as compared to natural enzymes. This can save valuable resources such as labor, money, and time.
However, the process of arriving at a functional protein of interest with the desired trait presents significant challenges. Current protein engineering approaches, such as directed evolution, rely heavily on chance to narrow down ideal variants of the protein of interest. Directed evolution uses repeated introductions of protein sequence alterations called mutations (iterative mutagenesis) followed by quick screening of large numbers of the variant proteins (high-throughput screening).
Not surprisingly, this method is labor-intensive and inefficient. To overcome these limitations, a group of researchers from China led by Dr. Huifeng Jiang from the Tianjin Institute of Industrial Biotechnology at the Chinese Academy of Sciences and National Center of Technology Innovation for Synthetic Biology, developed a protein engineering strategy predicated on artificial intelligence called “DeepEvo.
” Explaining further, Dr. Jiang states, " DeepEvo uses a deep evolution strategy, combining principles of deep learning—a process that emulates how the living brain functions—and evolutionary biology. " The study was published online in BioDes.
