- Generative AI has the potential to change Proteins, the building blocks of life
- FrameDiff, uses generative diffusion to design protein
- OmegaFold software is being used for tests in the lab
The computational biology has brought up a groundbreaking development. With the help of Generative AI, we can now imagine new protein structures, a feat that could change the field of molecular biology and drug design. This advanced technology, developed by a team of researchers, is making huge strides in understanding the complex world of proteins and their structures.
Proteins, the building blocks of life, are complex molecules that play crucial roles in virtually all biological processes. Their function is largely determined by their structure, a unique three-dimensional configuration that is as intricate as it is essential. Traditionally, determining the structure of a protein has been a costly and time-consuming process. However, the advent of generative AI has the potential to change this.
How can generative AI change Biology?
Generative AI, a subset of machine learning, has been used to create models that can generate new, unique outputs based on the patterns it learns from existing data. In the context of protein structures, generative AI uses the patterns found in known protein structures to imagine new ones. This is a significant advance in the field of computational biology, as it reduces the time and resources required to determine protein structures.
The researchers' work has been focused on developing a generative model that can create new protein structures. This model, known as FrameDiff, uses a process called generative diffusion, the same technology behind image-creation platforms like Midjourney and DALL-E. The model is trained on existing protein structures and then uses this knowledge to generate new ones.
Challenges in the new field
One of the major challenges in this field, as reported by the researchers, has been to imagine folds that are both possible and functional. Proteins are made from amino acids that fold into three-dimensional shapes, and these folding patterns dictate their function. Predicting which folds will be functional in a protein structure is a complex task, but one that generative AI is well-suited to tackle.
The development of FrameDiff and the use of generative AI in protein structure design represent a significant step forward in the field of molecular biology. The potential applications of this technology are vast, including the design of new drugs and the understanding of diseases at a molecular level. The researchers' work has been validated using OmegaFold software and experimental testing in the lab, further cementing the potential of this technology.
Looking ahead, the researchers plan to focus on developing this new innovative tool for antibodies and other therapeutic proteins. The ultimate goal is to move towards the joint design of protein sequences and structures, including side-chain conformations. This represents a new frontier in the field of computational biology, one that could have far-reaching implications for science and medicine.
The use of generative AI to imagine new protein structures is a significant development in the field of computational biology. It represents a new tool in the arsenal of scientists and researchers, one that could revolutionize our understanding of proteins and their structures. As this technology continues to advance, we can expect to see even more exciting developments in the near future.
How will the synthetic protein-making process work?
As per the researchers at the MIT CSAIL, PhD student Jason Yim who is also the lead author of the paper published on protein structure says, “In nature, protein design is a slow-burning process that takes millions of years. Our technique aims to provide an answer to tackling human-made problems that evolve much faster than nature's pace.”
He further also stated “The aim, with respect to this new capacity of generating synthetic protein structures, opens up a myriad of enhanced capabilities, such as better binders. This means engineering proteins that can attach to other molecules more efficiently and selectively, with widespread implications related to targeted drug delivery and biotechnology, where it could result in the development of better biosensors. It could also have implications for the field of biomedicine and beyond, offering possibilities such as developing more efficient photosynthesis proteins, creating more effective antibodies, and engineering nanoparticles for gene therapy.”
As we mentioned earlier, the model is totally based on constructing proteins via the process of diffusion, but it involves injecting noise, which randomly moves all the frames and blurs the structure of how the original protein looks.