Article Abstract: Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides […]
ABOUT CYRUS BENCH
Cyrus Bench ™
Cyrus Bench is an easy-to-use version of the Rosetta molecular modeling and protein design software package. Rosetta is the leading protein structure prediction tool, with top performance in the CASP and CAMEO competitions. Rosetta is the first software experimentally proven to design new proteins computationally, including the first designed protein binder with antibody-like affinity.
Cyrus Bench delivers Rosetta with the complete associated array of bio-molecular computation tools (e.g. BLAST) required to use Rosetta to its full potential. This scientific backbone is delivered via a custom-designed new Rosetta interface GUI that works in a modern web browser, paired with automation of a growing set of standard user procedures, and deployed on the best cloud compute resources.
Rosetta is the leading protein modeling tool, with proven performance in protein structure prediction, protein-ligand docking, protein-protein docking, antibody modeling, and structure modeling with experimental information (X-ray, NMR, Cryo-EM). Rosetta algorithms are tested and refined on real experimental data, and have consistently delivered actionable and verifiable wet-lab results.
Protein Structure Prediction
Rosetta consistently outperforms the competition in protein homology modeling at the bi-annual CASP competition and the weekly CAMEO contest (Song, Structure, 2013). Rosetta was the first software package to consistently predict small protein structures “ab initio”, with no homology (Kim, Proteins, 2014).
Rosetta can be combined with experimental structural data to produce better structures, or new atomic-resolution structures that are otherwise impossible. Low-quality x-ray data can be used to produce high-resolution structures (Adams, Ann. Rev. Biophys. 2013), or sparse NMR data can be transformed into useful structures (Lange, PNAS, 2012).
Rosetta is the world leader in computer design of proteins, and has achieved a number of “firsts”, including the first full-computationally designed and experimentally verified protein and the first protein-binding protein designed in a computer.
Rosetta has proven the ability to re-design natural enzymes to act on novel substrates, even in cases where traditional in vitro evolution methods have failed (Liu & Nivon, PNAS, 2014) . Rosetta has also shown the ability to design nano-molar affinity small-molecule binders “de novo” into previously inactive scaffolds (Tinberg, Nature, 2013).
Rosetta is able to design novel target-protein-binding activity into a huge number of inactive protein scaffolds. The hemagluttinin (influenza virus) binder, HB36, was the first-in-class computationally designed nano-molar protein/protein binder (Fleishman, Science, 2011), and more recently a BHRF1-protein binder was demonstrated using Rosetta (Procko, Cell, 2014).
CORE TECHNOLOGY: ROSETTA
Rosetta has been developed over the past 16 years, beginning at the lab of Prof. David Baker at the University of Washington, and at over 30 other labs around the world. Rosetta began as a tool for protein modeling, combining knowledge-based and physical modeling approaches with a consistent focus on actionable experimental results. Over the last 10 years Rosetta has evolved into the world-leading tool for computational protein design.
Rosetta primarily uses Monte Carlo based sampling using knowledge of protein structure from the protein data bank (pdb). Protein backbones are primarily modeled using “fragments” derived from the pdb using an array of powerful bioinformatics tools, for example BLAST — all of which is built in to Cyrus Bench behind the scenes. Protein sidechains are modeled using the now 30-year-old “rotamer” concept, with constant refinement over the years.
Rosetta uses a combination of physical and knowledge-derived potentials to score proteins during modeling and design. For example, a physics-based coulombic potentials is employed, and statistically-derived hydrogen bonding potential. Each protocol is highly tuned on large amounts of experimental data, often with custom scoring methods, and has been tested multiple times in real experimental contexts. For example a method to predict mutational free energies (Kellogg et al, Proteins, 2011) is tuned on large experimental datasets before deployment in Bench.
Article Abstract: We describe a general approach for refining protein structure models on the basis of cryo-electron microscopy maps with near-atomic resolution. The method integrates Monte Carlo sampling with local density-guided optimization, Rosetta all-atom refinement and real-space B-factor fitting. In tests on experimental maps of three different systems with 4.5-Å resolution or better, the method […]
Changing a few residues can change the function of homologous proteins. The chloride and proton affinity in the inward chloride-pumping halorhodopsin (HR) and outward proton-pumping bacteriorhodopsin (BR) are compared using classical electrostatic simulations. BR binds and releases protons from acidic residues that have been removed from HR. In the states where these acids are ionized […]