The scripts and input files that accompany this demo can be found in the
demos/protocol_captures directory of the Rosetta weekly releases.
This is a protocol capture, and represents the protocol at a fixed point in time. It may not work with the current version of Rosetta.
KEYWORDS: STRUCTURE_PREDICTION GENERAL
Rhiju Das, rhiju@stanford.edu
Follow up to talk:
An enumerative ansatz for high resolution RNA and Protein Folding Rhiju Das, Parin Sripakdeevong, Das Lab, Stanford Biochemistry Tuesday Aug. 3, 2010
associated with paper: "Four Small Puzzles that Rosetta Doesn't Solve", submitted in Jan. 2011 to RosettaCon 2010 Special Collection of PLoS One
A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular “puzzles” should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.
A full set of READMEs is available in input_files. These are directly copied from my run directories, so they better work.
A few runs involve 'StepWise Assembly' and require a master python script to setup the job, and another master python script to queue up the resulting computation, which is described as a directed acyclic graph. A full set of README_SETUPs and README_SUBs is available in input_files. These are directly copied from my run directories, so they better work too. The runs work on an LSF queuing system, as is available on Stanford's BioX2 cluster; they should also run fine via Condor's DAGMAN, though that queuing system has a lot of latency. [We have also written scripts to carry out the calculation on condor and torque clusters, and are almost finished with versions that use Amazon's EC2/S3. It usually takes a day or so to rewrite what we have for arbitrary systems. Our next step may be to write a version for Amazon's ElasticMapReduce, but it gets complicated since we actually have a series of map/reduce steps, not just one. ]
https://svn.rosettacommons.org/source/branches/das_lab/mini
https://svn.rosettacommons.org/source/branches/das_lab/minirosetta_database
Revision: 36561
[This branched off trunk in the winter of 2009.]Several scripts to generate a loop modeling job are in:
https://svn.rosettacommons.org/source/workspaces/rhiju/python
Revision: 40199
[the scripts include:
grind_dagman.py
stepwise_post_process_cluster.py
stepwise_post_process_combine_and_filter_outfiles.py
stepwise_pre_process_setup_dirs.py
extract_lowscore_decoys.py
and various helper python scripts...
]Finally, the queuing scripts are in:
https://svn.rosettacommons.org/source/branches/das_lab/SWA_dagman_python
Revision: 40199The protein and RNA de novo modeling make use of published protocols, e.g.:
i. Das R, Qian B, Raman S, Vernon R, Thompson J, et al. (2007) Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 69: 118-128.
ii. Mandell DJ, Coutsias EA, Kortemme T (2009) Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat Methods 6: 551-552.
iii. Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7: 291-294.
There are two manuscripts in preparation on StepWise Assembly for proteins and RNA.