How is Cyrus different from other protein structure prediction 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 design of these valuable proteins. Much of this protein comparative modeling and design 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 protein design 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 protein structure prediction toolkits such as Schrodinger and CCG Moe, and has leapfrogged other methods for protein modeling.
Since the early 2000’s Rosetta has been a top performer at the CASP protein structure prediction competition, and is the overall top performer among automated protein structure prediction and comparative modeling methods over the last 20 years of CASP. Protein structure prediction is a key tool for protein function prediction and protein stability prediction. Protein structure models can also be used for protein interaction predictions or for computational protein engineering or prediction of other protein properties, such as development liabilities (aggregation, solubility, immunogenicity).
All of these Rosetta protein structure prediction and protein modeling 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.
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