The scripts and input files that accompany this demo can be found in the
demos/public directory of the Rosetta weekly releases.
KEYWORDS: NUCLEIC_ACIDS LOOPS RNA STRUCTURE_PREDICTION
Rhiju Das, firstname.lastname@example.org
Updated in 2016 by Parisa Hosseinzadeh (email@example.com) to enable automatic testing of demos.
Solve structure of an RNA internal loop or multi-helix junction.
Ab initio and comparative modeling of biopolymers (RNA, protein, protein/RNA) often involves solving well-defined small puzzles (4 to 20 residues), like RNA aptamers, RNA tertiary contacts, and RNA/protein interactions. If these problems have torsional combinations that have not been seen previously or are not captured by coarse-grained potentials, most Rosetta approaches will fail to recover their structures. This app implements a stepwise ansatz, originally developed as a 'stepwise assembly' enumeration that was not reliant on fragments or coarse-grained modeling stages, but was computationally expensive. The new mode is a stepwise monte carlo, a stochastic version of stepwise assembly.
Following is for an internal loop ('two-way junction') drawn from the most conserved domain of the signal recognition particle, a core component of the machinery that translates membrane proteins in all kingdoms of life.
If you do not know the rigid body orientations of two helices (typical use case), run: (
$> $ROSETTA3/bin/stepwise.default.linuxgccrelease @uk_orientation.options -score:weights stepwise/rna/rna_res_level_energy4.wts -restore_talaris_behavior
If you have starting coordinates for the two helix endpoints, you can start with that single PDB ('start_native_1lnt_RNA.pdb') instead:
For the purpose of demo, we have lowered the number of generated structures and the cycles, but usually you want to at least run 1000 cycles and generate more structures.
$> $ROSETTA3/bin/stepwise.default.linuxgccrelease @known_ends.options -score:weights stepwise/rna/rna_res_level_energy4.wts -restore_talaris_behavior
To get out models (in this case from the pre-generated file in the rosetta_inputs directory):
$> $ROSETTA3/bin/extract_pdbs.default.linuxgccrelease -silent rosetta_inputs/swm_rebuild.out -score:weights stepwise/rna/rna_res_level_energy4.wts -restore_talaris_behavior
(Or use extract_lowscore_decoys.py which can be installed via tools/rna_tools/.)