Bench delivers a full array of molecular modeling software, e.g. sequence and structure alignment, to use Rosetta to its full potential.
Thousands of CPUs, instant updates, massive databases, all at the touch of a button. Your data is private and encrypted, and all data for sequences and biological structures belong to the user, unlike with public servers.
Cyrus’ Rosetta interface (GUI) was designed from scratch to be powerful but intuitive to a biochemist or chemist, making complex tasks accessible via a web browser.
Cyrus Bench includes the features most
requested by BioPharma scientists
An introduction to Rosetta protein modeling and engineering software, and Cyrus’ software and services
Cyrus in drug discovery
A summary of practical applications of Rosetta and Bench focused on clinical applications
Cyrus Scientific Validation
An overview of scientific studies validating Rosetta software in vitro and in vivo
Cyrus Software Demo
An in-depth sample use case demo of Bench showing how to run a protein stabilization calculation on a therapeutic candidate enzyme
Bench is available now. It offers the most in-demand features from Rosetta, including:
- The homology modeling protein structure prediction pipeline, with world-leading performance in the peer-reviewed CASP contests over the last 14 years
- Basic protein design tools in Rosetta, the same tools responsible for an array of “firsts” in protein design, including the first fully-computational protein design (top7) and first computational design of a protein-protein interaction with stronger binding than an antibody (HB36).
- Protein Mutational Free Energy (ddG) Calculations using Rosetta’s unique backbone flexibility modeling.
- Protein/Protein Interface Redesign with fixed or flexible backbone methods used for Biologics optimization in academia and Big Pharma, as presented in multiple conferences and published papers.
- Protein Loop Structure Prediction/Protein Loop Modeling using the latest NGK protocol in Rosetta
- Protein/Small Molecule Ligand Interface Design for engineering of natural enzymes or small molecule binding proteins for therapeutic or other biotech tools use cases.
- Protein/Nucleic Acid Complex Design and Modeling – For protein/DNA or protein/RNA complexes where a starting pdb model exists or can be generated, these tools automatically and accurately parameterize DNA/RNA and allow basic modeling (structure minimization, loop modeling, alternative conformations) and protein re-design around the nucleic acid. These methods do not allow nucleic acid design (e.g. of aptamers) but do allow design of protein/nucleic acid complexes – please check out the Das lab for the latest in DNA/RNA design.
- Modeling of Protein/Small Molecule Structural Fluctuations – Similar to molecular dynamics (MD) for analysis of small molecule drug complexes, in partnership with Openeye Scientific. Very useful for detailed analysis of protein/ligand docking or virtual screening output in a Computer Assisted Drug Design (CADD) workflow.
- Protein Immunogenicity Prediction (Biologics Immunogenicity) – Machine learning based MHC-II (T-cell) epitope prediction for triage and optimization of Biologics candidates. Contact us for information about our highly automated structure-based deimmunization or epitope removal for Biologics candidates.
- Antibody Structure Prediction/Antibody Homology Modeling – Cyrus’ version of the extensively validated RosettaAntibody tool out of Jeff Gray’s lab at Johns Hopkins, with a Machine Learning workflow to automated nearly any input antibody Fv sequence.
- Homology Modeling with User-Defined Templates – upload your own proprietary template pdb structures (e.g. from in house crystallography), or add multiple templates to control the automated multi-template RosettaCM method.
- More features coming soon, which include: homology modeling with a small molecule ligand (allowing high-accuracy protein/ligand docking in cases with appropriate homologs), protein/protein docking (general and antibody/antigen docking), automated machine-learning/Rosetta based solubility prediction and design, automated machine-learning/Rosetta based aggregation prediction and design, and more.