How is Cyrus different from other protein engineering software?
For over a half century, molecular modeling methods have been applied to small molecule discovery, with many notable successes. Focus is now increasingly being directed to proteins as therapeutics or synthetic biology tools, and to computational methods that can be applied to the structure prediction and engineering of these valuable proteins. Much of this protein comparative modeling and engineering work has been based solely on predictive approaches derived from first principle physics.
Rosetta, available commercially as Cyrus Bench, has fundamentally changed protein structure prediction and computational protein engineering through the use of a scoring function that reflects a combination of key physical insights from traditional methods and statistical knowledge from the vast accumulation of protein structures solved over the last six decades. This combined approach is very different from all other commercially-available software toolkits such as Schrodinger and CCG Moe, and has leapfrogged other methods for protein modeling.
This has lead to many “firsts” in the field of computational protein engineering, including the first fully software-designed proteins, the first designed proteins with biochemical characteristics as good as or better than a therapeutic antibody (protein interactions design) or enzyme, and the first designed proteins in human clinical trials (Tocagen and PVP Bio). Rosetta protein engineering software is broadly useful in protein optimization for stability, affinity (protein interaction affinity), selectivity, solubility, activity (enzyme activity) and other key protein performance parameters.
These Rosetta tools are now accessible through Cyrus Bench, and are helping Cyrus customers advance their discovery programs into pre-clinical and clinical trials.
Our mission is to transform therapeutics and synthetic biology discovery, accelerating new treatments and protein-based materials to market, and creating entirely new treatments computationally that are difficult or impossible to identify using conventional means.
Click to Learn More