Researchers at the University of Texas at Austin have developed a groundbreaking AI model, EvoRank, that harnesses the power of natural evolution to revolutionize the design of protein-based therapies and vaccines. This innovative approach paves the way for more effective and less toxic treatments, as well as new preventive strategies in medicine.
Harnessing Nature’s Playbook
The reasoning underlying Evo Rank is to exploit the diversity in millions of proteins from billions of years of evolution. This has allowed the scientists to uncover the fundamentals of protein evolution by taking advantage of natural variation to identify mechanistic insights that can be exploited for the design of novel protein-based applications.
Essentially, the AI model is a means of learning from nature’s experiments going back millennia to some essential principles which underlie protein evolution and allow proteins to adapt to different environments and functions. These natural ‘experiments’ are a huge, yet unexplored, collection of sequences which the AI model can learn from and uses to make original protein designs for many biomedical and biotechnological applications.
Changing Protein Engineering
While earlier methods aimed to predict the structure and shape of proteins, the EvoRank model goes a step further by proposing which changes in proteins ought to be made to improve their function. This is an important step in the design of protein based therapies and vaccines as it enables researchers to make synthetic proteins that have functions natural proteins lack.
The researchers have already found cause for optimism, because the AI-generated designs are leading to protein variants that are stable and also show characteristics that couldn’t have been predicted using traditional methods. This provides new options for the design of more efficacious and less toxic agents, or novel preventive interventions in medical field.
The potential of this technology is enormous since protein therapeutics are inherently less toxic and better tolerated than small molecule drugs or vaccines. The global protein therapeutics market currently exceeds $400 billion and is expected to grow by over 50% in the next ten years. But they are taken forever to develop, are expensive and very risky Script investigates protein-based drugs. Through EvoRank, we hope to provide a set of tools for navigating the treacherous pathway that is faster and more sensible protein engineering.
Conclusion
Making Moves in Protein Engineering A leap forward in protein engineering comes as the Univ. of Texas at Austin debut the EvoRank AI model. If we can replicate in the laboratory over thousands of years, what nature has created over billions of years, a future generation of protein-based therapeutics and vaccines may be able to provide a much healthier future. The ability to safely examine biological fluids and tissues at a cellular level with these systems will push the boundaries of what can be realized in medicine, driving improved patient outcomes and a step change in the way we think about healthcare.
