Biology is a wondrous yet delicate tapestry. At the heart is DNA, the master weaver that encodes proteins, responsible for orchestrating the many biological functions that sustain life within the human body. However, our body is akin to a finely tuned instrument, susceptible to losing its harmony. After all, we’re faced with an ever-changing and relentless natural world: pathogens, viruses, diseases, and cancer.
Imagine if we could expedite the process of creating vaccines or drugs for newly emerged pathogens. What if we had gene editing technology capable of automatically producing proteins to rectify DNA errors that cause cancer? The quest to identify proteins that can strongly bind to targets or speed up chemical reactions is vital for drug development, diagnostics, and numerous industrial applications, yet it is often a protracted and costly endeavor.
To advance our capabilities in protein engineering, MIT CSAIL researchers came up with “FrameDiff,” a computational tool for creating new protein structures beyond what nature has produced. The machine learning approach generates “frames” that align with the inherent properties of protein structures, enabling it to construct novel proteins independently of preexisting designs, facilitating unprecedented protein structures.
"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,” says MIT CSAIL PhD student Jason Yim, a lead author on a new paper about the work. “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.”
Proteins have complex structures, made up of many atoms connected by chemical bonds. The most important atoms that determine the protein’s 3D shape are called the “backbone,” kind of like the spine of the protein. Every triplet of atoms along the backbone shares the same pattern of bonds and atom types. Researchers noticed this pattern can be exploited to build machine learning algorithms using ideas from differential geometry and probability. This is where the frames come in: Mathematically, these triplets can be modeled as rigid bodies called “frames” (common in physics) that have a position and rotation in 3D.
These frames equip each triplet with enough information to know about its spatial surroundings. The task is then for a machine learning algorithm to learn how to move each frame to construct a protein backbone. By learning to construct existing proteins, the algorithm hopefully will generalize and be able to create new proteins never seen before in nature.
Training a model to construct proteins via “diffusion” involves injecting noise that randomly moves all the frames and blurs what the original protein looked like. The algorithm’s job is to move and rotate each frame until it looks like the original protein. Though simple, the development of diffusion on frames requires techniques in stochastic calculus on Riemannian manifolds. On the theory side, the researchers developed “SE(3) diffusion” for learning probability distributions that nontrivially connects the translations and rotations components of each frame.