As of mid-2020, your best choice for whole-RNA modeling with fragment assembly is FARFAR2, which uses the same executable and many of the same flags. FARFAR2 represents a new set of defaults and some changes in behavior, so this documentation will be a little out of date.
To produce de novo models of small RNA motifs through Fragment Assembly of RNA with Full Atom Refinement (FARFAR).
Note that most of the functionality of rna_denovo/FARFAR is now available on a ROSIE FARFAR server, if you want to do some easy tests.
The central code for the rna_denovo application is in
For a 'minimal' demo example of the RNA fragment assembly and full-atom minimization protocol and input files, see
This code is intended to give three-dimensional de novo models of single-stranded RNAs or multi-stranded RNA motifs, with the prospect of reaching high (near-atomic-resolution) accuracy. The application slots into workflows for 3D RNA modeling described at the Ribokit site.
The RNA structure modeling algorithm in Rosetta is based on the assembly of short (1 to 3 nucleotide) fragments from existing RNA crystal structures whose sequences match subsequences of the target RNA. The Fragment Assembly of RNA (FARNA) algorithm is a Monte Carlo process, guided by a low-resolution knowledge-based energy function. The models can then be further refined in an all-atom potential to yield more realistic structures with cleaner hydrogen bonds and fewer clashes; the resulting energies are also better at discriminating native-like conformations from non-native conformations. The two-step protocol has been named FARFAR (Fragment Assembly of RNA with Full Atom Refinement).
This method has been demonstrated to reach atomic accuracy for small motifs (12 residues or less) – the current bottleneck for larger RNAs is the difficulty of complete conformational sampling (as in other applications in Rosetta to, e.g., protein de novo modeling). On-going work attempts to resolve this issue, but requires great computational expense (see stepwise).
For larger RNAs, it appears most efficient to just carry out fragment assembly without refinement, specifying secondary structure (as described below). Although atomic accuracy is unlikely, models accurate at nucleotide or helix resolution can be achieved, especially with constraints from experiments. See also: RNA assembly with experimental pair-wise constraints and, more up to date, RNA de novo setup.
As with most other modes in Rosetta, the final ensemble of models is not guaranteed to be a Boltzmann ensemble. There is some progress happening in that direction for RNA with the recces application.
By default, the code runs Monte Carlo fragment assembly, optimized in a knowledge-based low-resolution potential.
It is strongly suggested that you run with "-minimize_rna", which permits the refinement in the high-resolution Rosetta potential, and results in models with few steric clashes and 'cleaner' hydrogen bonds.
There are variations of the code that permit just scoring or just minimizing in the high resolution Rosetta potential. These are described below in Tips.
FARNA (rna_denovo) can accept sequence & secondary structure from command line, and does not require any files. However, using file input can help with organizing runs.
The fasta file: it is a sequence file for your RNA. Its header lines can specify chains and numbering for the output structures, too.
The secondary structure file: holds the secondary structure for the RNA in dot-parens notation, if known.
Native pdb file, if all-heavy-atom rmsd's are desired. Must be in Rosetta's PDB format for RNA.
A sample command line is the following:
rna_denovo.<exe> -sequence "ucaggu aagcag" -secstruct "(....( )....)" -nstruct 2 -out:file:silent test.out -minimize_rna
or if you want to supply the sequence & secondary structure in files:
rna_denovo.<exe> -fasta chunk002_1lnt_.fasta -secstruct_file chunk002_1lnt_.secstruct -nstruct 2 -out:file:silent test.out -minimize_rna
The code takes about 1 minute to generate two models.
The fasta file has the RNA name on the first line (after >), and the sequence on the second line. Valid letters are a,c,g, and u. Example fasta and secstruct files are available in
-in:fasta Fasta-formatted sequence file. [FileVector] -sequence Sequence on command line (put in quotes; fasta input is preferred) -secstruct_file RNA sec struct to model in dot-parens notation -secstruct RNA sec struct on command line (put in quotes; secstruct_file input is preferred) -out:file:silent Name of output file [scores and torsions, compressed format]. default="default.out" [String] -in:native Native PDB filename. [File]. -out:nstruct Number of models to make. default: 1. [Integer] -minimize_rna High resolution optimize RNA after fragment assembly.[Boolean] -vary_geometry Vary bond lengths and angles (with harmonic constraints near Rosetta ideal) for backbone and sugar degrees of freedom [Boolean]
-cycles Number of Monte Carlo cycles.[default 10000]. [Integer] -bps_moves Base pair step moves. For adjacent base pairs within stems or that are obligate pairs, draw sequence-matched fragments that encompass both pairs. Adjacent means that base pairs have contiguous residues on one strand, and at most 3 intervening residues on the other. -output_lores_silent_file If high resolution minimizing, output intermediate low resolution models. [Boolean] -dump Generate pdb output. [Boolean] -vall_torsions Source of RNA fragments. [default: 1jj2.torsions]. [Boolean] -jump_library_file Source of base-pair rigid body transformations if base pairs are specified. [default: 1jj2_RNA_jump_library.dat] [String] -obligate_pair Residue pairs that must form a base pair (possibly non canonical) -secstruct_general Specification of -obligate_pair in dot-parens format -obligate_pair_explicit Residue pairs that must form a base pair, with specification of base edges (W/H/S/X) and orientation (A/P/X for antiparallel/ parallel/unknown; C/T/X allowed too for cis/trans) -cst_file Specify constraints (typically atom pairs) in Rosetta-style constraint file. [String] -output_lores_silent_file if doing full-atom minimize, also save models after fragment assembly but before refinement (file will be called *.LORES.out) [Boolean] -dump output pdbs that occur during the run, even if using silent file output.
Advanced -s Input PDBs to be used as fixed 'chunks' in fragment assembly -in:file:silent List of input files (in 'silent' format) that specify potential template structures or 'chunks' -input_res Positions at which 'chunks' are applied. If there is more than one chunk file, specify indices for the first file and then the second file, etc. (Used to be called -chunk_res.) -fixed_stems Seed each stem with a Watson-Crick base pair instead of having the strands find each other -cutpoint_closed Positions at which to force transient chainbreaks (may be needed if you get fold-tree errors) -cutpoint_open Positions at which strands end (better to specify separate strands in FASTA file, or with spaces between strings in sequence) -data_file RDAT or legacy-format file with RNA chemical mapping data
-filter_lores_base_pairs Filter for models that satisfy structure parameters. [Boolean] True by default. -params_file RNA params file name.[String]. For Example: -params_file chunk002_1lnt_.prm Deprecated by -working_res option above. -in:database Path to rosetta databases. Default is based on location of rosetta executables. [PathVector] -output_res_num Numbering (and chain) of residues in output PDB or silent file. Better to specify in headers in .fasta file. -staged_constraints Apply constraints in stages depending on sequence separation -close_loops Attempt closure across chainbreaks by cyclic coordinate descent after fragment moves [Boolean] Defaults to true.
Note that in older versions of Rosetta, the PDBs may have residue types marked as rA, rC, rG, and rU and unusual atom names. Versions of Rosetta released after 3.5 have residue and atom names matching BMRB/NDB standard nomenclature. If you have a "standard" PDB file, there is a python script available to convert it to current Rosetta format:
tools/rna_tools/bin/make_rna_rosetta_ready.py <pdb file>
You can also specify base pairs that must be forced, even at the expense of creating temporary chainbreaks, in the params file, with a flag like
-obligate_pair_explicit 2 11 W W A
This also allows the specification of non-Watson-Crick base pairs. In the line above, you can change the W's to H (hoogsteen edge) or S (sugar edge); and the A to P (antiparallel to parallel). The base edges are essentially the same as those defined in the classification by Leontis & Westhof. The latter (A/P) are determined by the relative orientation of base normals. [The cis/trans classification of Leontis & Westhof would be an alternate to the A/P, but we found A/P more convenient to compute and to visually assess. You can supply C/T for cis/trans, and it will be converted based on a lookup table.] The base pairs are drawn from a library of base pairs extracted from the crystallographic model of the large ribosomal subunit 1JJ2.
When specifying pairs, if there are not sufficient strand breaks to allow all the pairs to form, the code will attempt to choose a (non-stem) RNA suite to put in a cutpoint, which can be closed during fragment assembly with the -close_loops option. If you want to pre-specify where this cutpoint will be chosen, add a flag like
By default the RNA fragment assembly makes use of bond torsions derived from the large ribosome subunit crystal structure 1jj2, which have been pre-extracted in 1jj2. torsions (available in the database). If you want to use torsions drawn from a separate PDB (or set of PDBs), the following command will do the job.
rna_database.<exe> -vall_torsions -s my_new_RNA1.pdb my_new_RNA2.pdb -o my_new_set.torsions
The resulting file is just a text file with the RNA's torsion angles listed for each residue. Then, when creating models, use the following flag with the rna_denovo application:
Similarly, the database of base pair geometries can be created with
rna_database -jump_library, and then specified in the rna_denovo application with
Last, a database of base pair step geometries (see below) can be created with
rna_database -bps_database. By default, this creates files for the standard canonical base pair steps. To also parse out noncanonical base pair steps, use
-use_lores_base_pair_classification catches all pairs, including ones that are held in place by base-phosphate contacts but no base-base hydrogen bonds (as occurs in the sarcin/ricin loop).
Yes, by using the flags
-bps_moves, you can ask the application to try to draw from a database of "base pair steps". There are two kinds of those steps.
First, for stems (specified by secondary structure file), adjacent base pairs form base pair steps, involving four nucleotides (i,i+1,j,j+1) where (i,j+1) and (i+1,j) are paired. There is a set of such steps in Rosetta's database, drawn from the ribosome. The RNA's fold tree will be set up with appropriate jump connections and cutpoints so that those base step conformations can be substituted in during fragment assembly.
Second, if you have specified obligate pairs -- but with unknown pairing edges and orientations ('X' in the params file) -- special fragments will be set up for such pairs that involve nucleotides that are adjacent in sequence. For example, if your params file contains a flag like:
using the flag
-obligate_pair 2 11 3 8
-bps_moveswill trigger moves that substitute sequence-matched fragments for the nucleotides at (2,3,8,11). This happens if on at least one strand, the base pair step involves residues that are immediately contiguous (2 and 3 in this example). On the other strand, the base pair step must involve residues that are contiguous are have no more than 3 intervening 'bulge' residues (8 and 11 in this example). Note that these base pair steps will generally include noncanonical pairs. There's a demo of this functionality applied to model the sarcin/ricin loop in
Note: For noncanonical pairs, we don't allow specification of edges and orientations at the moment -- the database gets pretty sparse with that level of specification. Also note: If there is a base pair step that includes a pair both inside a Watson/Crick stem and a more general 'obligate pair', the stem pairing may actually come out as non-Watson-Crick, which often happens anyway for base pairs at the edge of stems.
A common question: what do the terms in the 'SCORE lines' of silent files mean? Here's a brief rundown, with more explanation in the Das, 2010 paper cited above.
***Energy interpreter for low resolution silent output: score Final total score rna_rg Radius of gyration for RNA rna_vdw Low resolution clash check for RNA rna_base_backbone Bases to 2'-OH, phosphates, etc. rna_backbone_backbone 2'-OH to 2'-OH, phosphates, etc. rna_repulsive Mainly phosphate-phosphate repulsion rna_base_pair_pairwise Base-base interactions (Watson-Crick and non-Watson-Crick) rna_base_pair Base-base interactions (Watson-Crick and non-Watson-Crick) rna_base_axis Force base normals to be parallel rna_base_stagger Force base pairs to be in same plane rna_base_stack Stacking interactions rna_base_stack_axis Stacking interactions should involve parallel bases. atom_pair_constraint Harmonic constraints between atoms involved in Watson-Crick base pairs specified by the user in the params file rms all-heavy-atom RMSD to the native structure ***Energy interpreter for fullatom silent output: score Final total score fa_atr Lennard-jones attractive between atoms in different residues fa_rep Lennard-jones repulsive between atoms in different residues fa_intra_rep Lennard-jones repulsive between atoms in the same residue lk_nonpolar Lazaridis-karplus solvation energy, over nonpolar atoms hack_elec_rna_phos_phos Simple electrostatic repulsion term between phosphates hbond_sr_bb_sc Backbone-sidechain hbonds close in primary sequence hbond_lr_bb_sc Backbone-sidechain hbonds distant in primary sequence hbond_sc Sidechain-sidechain hydrogen bond energy ch_bond Carbon hydrogen bonds geom_sol Geometric Solvation energy for polar atoms rna_torsion RNA torsional potential. atom_pair_constraint Harmonic constraints between atoms involved in Watson-Crick base pairs specified by the user in the params file angle_constraint (not in use) N_WC number of watson-crick base pairs N_NWC number of non-watson-crick base pairs N_BS number of base stacks [Following are provided if the user gives a native structure for reference] rms all-heavy-atom RMSD to the native structure rms_stem all-heavy-atom RMSD to helical segments in the native structure, defined by 'STEM' entries in the parameters file. f_natWC fraction of native Watson-Crick base pairs recovered f_natNWC fraction of native non-Watson-Crick base pairs recovered f_natBP fraction of base pairs recovered
To get a score of an input PDB, you can run the 'denovo' protocol but ask there to be no fragment assembly cycles and no rounds of minimization:
rna_score.<exe> -database <path to database> -s <pdb file> [<pdb file 2> ...] -out:file:silent SCORE.out [-native <native pdb>]
If you want to minimize under the low resolution RNA potential (used in FARNA), add the flag '-score:weights rna_lores.wts'. Then you can check the score in SCORE.out:
grep SCORE SCORE.out
But this is not recommended if you are trying to score a model deposited in the PDB or created by other software – see next How do I just minimize?
rna_score can take your models (in 'silent' format) and figure out all-heavy-atom RMSD while preserving all the energy terms from the run. Try:
rna_score -just_calc_rmsd -in:file:silent <input file > -out:file:silent <output file> -native <native PDB>
If you take a PDB created outside Rosetta, very small clashes may be strongly penalized by the Rosetta all-atom potential. Instead of scoring, you should probably do a short minimize, run:
rna_minimize.<exe> -database <path to database> -s <pdb file> [<pdb file 2> ...] -out:file:silent MINIMIZE.out [-native <native pdb>]
If you want to minimize under the low resolution RNA potential (used in FARNA), add the flag '-score:weights rna_lores.wts'. Then check out the scores in MINIMIZE.out.
grep SCORE MINIMIZE.out
You can extract models from silent files as described in Extraction Of Models Into PDB Format, but you'll also get models with the same names as your input with the suffix '_minimize.pdb'.
For building models of larger RNAs, check these sections: RNA assembly with experimental pair-wise constraints and RNA denovo setup.
The models from the above run are stored in compressed format in the file test.out, along with lines representing the score components. You can see the models in PDB format with the conversion command.
rna_extract.<exe> -in:file:silent test.out -in:file:silent_struct_type rna -database <path to database>
There is one executable for clustering, it currently requires that all the models be in a silent file and have scores. (If you don't have such a silent file, use the rna_score executable described in How do I just score? ). Here's the command line:
rna_cluster.<exe> -database <path to database> -in:file:silent <silent file with models> -out:file:silent <silent file with clustered models> [-cluster:radius <rmsd threshold>] [-nstruct <maximum number of clusters>]
The way this clustering works is it simply goes through the models in order of energy, and if a model is more than the rmsd threshold than the existing clusters, it spawns a new cluster.
Written in 2008.
Last updates: Nov. 2011 and Aug. 2014 by Rhiju Das (rhiju [at] stanford.edu). Added applications rna_minimize, rna_helix, rna_cluster. Updated torsional potential to be smooth.
Under-the-hood refactoring is occurring in 2015-2016. Link to plan (for developers only)