Luckily, a form of artificial intelligence called deep learning may provide an elegant way to create proteins that did not exist previously – hallucination.
Designing proteins from scratch
Proteins are made up of hundreds to thousands of smaller building blocks called amino acids. These amino acids are connected to one another in long chains that fold up to form a protein. The order in which these amino acids are connected to one another determines each protein’s unique structure and function.
The biggest challenge protein engineers face when designing new proteins is coming up with a protein structure that will perform a desired function. To get around this problem, researchers typically create design templates based on naturally occurring proteins with a similar function. These templates have instructions on how to create the unique folds of each particular protein. However, because a template must be created for each individual fold, this strategy is time-consuming, labor-intensive and limited by what proteins are available in nature.
Over the past few years, various research groups, including the lab I work in, have developed a number of dedicated deep neural networks – computer programs that use multiple processing layers to “learn” from input data to make predictions about a desired output.
When the desired output is a new protein, millions of parameters describing different facets of a protein are put into the network. What’s predicted is a randomly chosen sequence of amino acids mapped onto the most probable 3D structure that sequence would take.
Network predictions for a random amino acid sequence are blurry, meaning the final structure of the protein is not very clear-cut, while both naturally occurring proteins…