Rosetta protein manual

Create symmetry definition - Create Rosetta symmetry definition files for a point group. Batch distances - Calculate the closest approach for residue-residue pairs. Fragment picker - Pick fragments to be used in conjunction with other fragment-aware Rosetta applications. Make exemplars - Create an exemplar for surface pockets on a protein that touch a target residue. OptE - Refit reference weights in a scorefunction to optimize given metrics.

Pocket target residue suggestion - Suggest the best pair of target residues for pocket optimization for the purpose of inhibiting a protein-protein interaction.

Pocket relax - Relax followed by full atom minimization and scoring with no PocketConstraint. Useful when performing pocket optimization. Sequence recovery - Calculate the mutations and native recovery from Rosetta design runs. Torsional potential correction - Remove double counting interactions from the sidechain torsional potential. Collection of example commandlines. Tools : List of useful accessory scripts included with Rosetta.

Application Documentation Search. Ab initio modeling - Predict 3-dimensional structures of proteins from their amino acid sequences. Membrane abinitio - Ab initio for membrane proteins. Metalloprotein ab initio - Ab inito modeling of metalloproteins. Backrub - Create backbone ensembles using small, local backbone changes. Fold-and-dock - Predict 3-dimensional structures of symmetric homooligomers. Helical bundle structure prediction - Predict structures of predominantly helical heteropolymers from sequence.

Uses a fragment-free approach that is good for proteins and non-natural heterpolymers with no close relatives of known structure. Molecular replacement protocols - Use Rosetta to build models for use in X-ray crystrallography molecular replacement. Prepare template for MR - Setup script for molecular replacement protocols. Relax - "Locally" optimize structures, including assigning sidechain positions.

Works for full denovo modeling or refinement. GlycanInfo Get information on all glycan trees within a pose GlycanClashCheck Obtain data on model clashes with and between glycans, or between glycans and other protein chains. SimpleGlycosylateMover Glycosylate poses with glycan trees.

GlycanTreeSelector Select individual glcyan trees or all of them GlycanResidueSelector Select specific residues of each glycan tree of interest.

Kinematic loop modeling - Sample loop conformations using the kinematic closure algorithm. Next-generation KIC - A newer version of loop modeling with kinematic closure.

KIC with fragments - The latest version of loop modeling, combining kinematic closure with sampling of coupled degrees of freedom from fragments. Stepwise assembly of protein loops - Generate three-dimensional de novo models of protein segments - Stepwise assembly of long loops - For loops greater than residues. Development Documentation. Rosetta is a comprehensive software suite for modeling macromolecular structures.

As a flexible, multi-purpose application, it includes tools for structure prediction, design, and remodeling of proteins and nucleic acids. Since , Rosetta web servers have run billions of structure prediction and protein design simulations, and billions or trillions more have been run on supercomputer clusters. Researchers use Rosetta to better understand treatments of infectious diseases, cancers, and autoimmune disorders.

Looking for an easy to follow layout pattern? Look no further than the Technical Guide, which provides install patterns for Rosetta products. No problem. View typical cross-sections in the Technical Guide. Are you creating a pillar with Kodah? Or, in search of the curve radius for a Belvedere wall. In most cases, one starts from a random 3D conformation or a 2D or 1D chemical formula e. I use OpenEye's Omega, like this:.

Only the partial charges on the first conformer in the. Special records can be added to the file to specify the atom tree root or split the molecule into multiple residues, but these are not generally used right now.

Otherwise, just the conformation in the input PDB will be used. For docking benchmarks, those coordinates can be used for the reference "native" structure, so Rosetta will calculate meaningful RMSD values. When running Rosetta, the 1t3r. Only sidechains near the initial ligand position are repacked during docking, to save time.

This means all sidechains should be repacked before docking, so that any pre-existing clashes according to Rosetta's energy function can be resolved. Otherwise, a ligand placed near the clashing residues will allow them to repack and thus gain a large energy bonus that does not accurately reflect its binding affinity in that position.

The input PDB should consist of the protein chain s , each with its own chain ID, followed by any metal ions PDB residue names must be right aligned, e. The ligand s to be docked should be omitted to avoid biasing the docking toward the known structure. Some sidechains may repack into the binding pocket; the docking algorithm will move them out of the way as needed. Because the repacking is stochastic, I typically generate 10 structures per receptor and choose the one lowest in energy:.

Once we start introducing backbone flexibility, the backbone will have to be preminimized as well, possibly with a relax protocol. Because the backbone flexibility we have introduced so far involves few residues, and those are restrained, we do not currently seem to need pre-minimization of the protein backbone.

The input PDB for docking should consist of the protein chain s followed by the ligand residue in a separate chain; this generally just means appending one of the ligand PDBs from the previous step onto the cleaned up protein PDB. Any other information should be removed from the PDB before starting, although Rosetta scoring information and the like are generally ignored without problems. Cofactor ligands may be left in place as long as the ligand to be docked comes last in the file; they will not move.

Waters and ions may also be left in the PDB after the protein, before the ligand if appropriate. Generally, I use the first Omega-generated conformer plus the repacked protein coordinates as the input to docking. Using the crystalized ligand coordinates may lead to artificially good results in docking benchmarks. The input is essential, and is provided via the -s switch. The native is optional; it allows Rosetta to calculate meaningful RMSD values and is provided via -native.

If no native is provided, the input will be used to calculate RMSDs. The unbound is also optional; it tells Rosetta what sidechain conformations were observed in the crystal structure these will be favored during repacking. The unbound is specified with -unboundrot. For cross docking, it is important not to include the co-crystal ligand coordinates in either the input or the unbound PDB file: If you do, it will bias the docking by giving Rosetta the bioactive ligand conformation as one of its rotamers.

It's safe to include the co-crystal ligand coordinates in the "native" PDB file; these are only used for calculating RMS and will not influence the course of the docking. A total of - docking trajectories are often necessary to be reasonably sure of correctly docking the ligand, depending on how well the location of the binding site is known.

Docking against the whole protein surface could require many times more trajectories. I typically run on the Baker lab's large "syd" cluster, which uses Condor for scheduling, and I typically run 5 - 10 processes per ligand with -nstruct of - Each process should have its own output directory to avoid overwhelming the NFS file servers. For example, an entry from my Condor submit script:.

In this case, I'm defining the binding site I want to sample with 6 spheres, each with a 5A radius. A total of trajectories will run 10 x Especially for large ligands, I typically run trajectories per one 5A sphere or equivalent volume: two 4A spheres, etc. Each process will produce a "silent file" with that contains the endpoint of every trajectory.