Research

Researchers at MIT have developed a computational method to hasten the process of generating optimized versions of proteins, using only a small amount of data. The researchers have generated proteins with mutations capable of improving Green Fluorescent Protein (GFP) and a protein used to deliver DNA for gene therapy from an adeno-associated virus (AAV). The process…

Protein engineering is a complicated process, typically involving the random mutation of a natural protein with a desirable function, repeated until an optimal version of the protein is developed. This process has proven successful for proteins like the green fluorescent protein (GFP), but this isn't the case for all proteins. Researchers at MIT have developed…

MIT researchers have developed a computational approach to help predict mutations that can create optimized versions of certain proteins, working with a relatively small amount of data. The team believes the system could lead to potential medical applications and neuroscience research tools. Usually, protein engineering begins with a natural protein that already has a desirable function,…

MIT researchers have developed a computational approach that predicts protein mutations, based on limited data, that would enhance their performance. The researchers used their model to create optimized versions of proteins derived from two naturally occurring structures. One of these was the green fluorescent protein (GFP), a molecule used to track cellular processes within the…

Scientists at Massachusetts Institute of Technology (MIT) have developed a computational model aimed at simplifying the process of protein engineering. The researchers applied mutations to natural proteins with desirable traits, such as the ability to emit fluorescent light, using random mutation to cultivate better versions of the protein. The technique was deployed using the green…