KEYWORDS: STRUCTURE_PREDICTION GENERAL

Bold text means that these files and/or this information is provided.

Italicized text means that this material will NOT be conducted during the workshop.

Fixed width text means you should type the command into your terminal.


If you want to try making files that already exist (e.g., input files), write them to a different directory!

## Tutorial 1: Protein Folding

This tutorial presents a protein folding benchmark experiment. Bacteriophage T4 lysozyme is a soluble protein that hydrolyzes peptidoglycan and releases the virus from its bacterial host. The protein was crystallized at a resolution of 1.7 angstroms and the resulting structure was submitted to the Protein Data Bank (PDB) under accession number 2LZM. In this tutorial, you will reconstruct the structure of bacteriophage T4 lysozyme using ab initio protein folding. At the end of the tutorial, the results from this benchmark experiment will be compared to the native structure available from the PDB.

This tutorial was updated on 29 May 2017 by Vikram K. Mulligan (vmullig@uw.edu) to reflect changes due to the new default Rosetta scoring function, called ref2015.

1. Navigate to this tutorial directory. You will work in this directory for the rest of the tutorial.

> cd <path-to-Rosetta>/demos/tutorials/advanced_denovo_structure_prediction


1. Save your protein sequence to a file using FASTA format.

The 2LZMA.fasta file is provided for you in the
input_files/ directory.

1. Get the sequence in FASTA format from NCBI.
1. Go to http://www.ncbi.nlm.nih.gov/protein/.
2. Type in 2LZMA in the search bar at top.
3. Click on the "FASTA" link.
4. Open a text editor, such as gedit. Copy all the sequence information, including the line beginning with ">", into a new file.
5. Remove the first 57 residues of the sequence so that it begins with the sequence ITKDE.
6. Save the file as 2LZMA.fasta to your directory. Alternatively you can copy the file provided for you:
   $> cp input_files/2LZMA.fasta . 2. Prepare a PDB of native structure. The clean and renumbered 2LZMA.pdb file is provided for you in the input_files/ directory. 1. Get the native PDB structure. 1. Go to http://www.pdb.org/. 2. Search for 2LZM. 3. Click "Download Files" and "PDB File (Text)" in the top right hand corner by the PDB ID. 4. Save the PDB file as 2LZM.pdb. 5. The file may end up downloading to your Downloads directory. If so, move it to your working directory using this command: mv ~/Downloads/2LZM.pdb . 2. Clean 2LZM.pdb so that only ATOM records remain: > <path-to-Rosetta>/tools/protein_tools/scripts/clean_pdb.py 2LZM A  • NOTE: This outputs 2 files, 2LZM_A.fasta and 2LZM_A.pdb. 3. In a text editor, remove the first 57 residues from 2LZM_A.pdb that it begins with the sequence ITKDE. 4. Renumber 2LZM_A.pdb: > <path-to-Rosetta>/tools/protein_tools/scripts/pdb_renumber.py \ 2LZM_A.pdb 2LZMA.pdb  5. Alternatively you can copy the provided clean pdb into your directory: $> cp input_files/2LZMA.pdb .

3. Prepare 3mer and 9mer fragment libraries.

The 2LZM fragment libraries (aa2LZMA03_05.200_v1_3 and aa2LZMA09_05.200_v1_3),
secondary structure prediction (2LZMA.psipred_ss2 and 2LZMA.jufo_ss2),
and checkpoint (2LZMA.checkpoint) files are provided for you in the
protein_folding/input_files/ directory.

1. Make fragment libraries using Robetta (for the purposes of this workshop).

1. If you are an academic or non-profit user of Rosetta, make sure you're registered at http://robetta.bakerlab.org/.
2. Under "Services", "Fragment Libraries" click "Submit".
3. Type 2LZMA under "Target Name".
4. Copy/paste all the text in 2LZMA.fasta into the "Paste Fasta" field.
5. If you are benchmarking, you will want to check "Exclude Homologues".
6. Click "Submit".
7. You can see your position in the queue by clicking "Queue" under "Fragment Libraries". This should not take very much time unless the queue is long.
9. Your fragment files should be called aa2LZM_03_05.200_v1_3 and aa2LZM_09_05.200_v1_3. However, it is wise to keep all of the files, especially the checkpoint, psidpred_ss2, and jufo_ss2 files. Right click on the files, and save the files to your working directory.
2. Or, make fragment libraries using make_fragments.pl (not covered by this tutorial; see Appendix).

3. Or copy the fragments already provided:

$> cp input_files/aa2LZMA03_05.200_v1_3 .$> cp input_files/aa2LZMA09_05.200_v1_3 .
$> cp input_files/2LZMA.checkpoint .  4. Prepare the topology broker setup file. The topology_broker.tpb setup file is provided for you in the ~/rosetta_workshop/tutorials/protein_folding/input_files/ directory. 1. Save the following text as topology_broker.tpb in your working directory, or you can directly copy the file from input_files directry to your working directory CLAIMER SequenceClaimer FILE ./2LZMA.fasta END_CLAIMER$> cp input_files/topology_broker.tpb .

5. Prepare the topology broker options file.

The 2LZM_broker.options file is already provided for you in the
input_files/ directory.

1. Rosetta ignores lines beginning with # (these are treated as comments).
2. Avoid mixing tabs and spaces. Be consistent in your formatting (tab-delimited or colon-separated).
3. Save the file in your working directory. Or you can copy it from the input_files: $> cp input_files/2LZM_broker.options . 6. Prepare the core residue specification file. The 2LZMA_core.txt file is already provided for you in the ~/rosetta_workshop/tutorials/protein_folding/input_files/ directory. 1. The 2LZMA_core.txt file tells Rosetta over which residues to compute CA-RMSD, easing the analysis burden. Copy it from the input_files directory to the current directory. $> cp input_files/2LZMA_core.txt .

3. Run the Rosetta topology broker using the Minirosetta application.

1. Make sure all the filenames and paths in the options file are correct.
2. Make sure you are in your working directory.
3. Type the following command line:

$>$ROSETTA3/bin/minirosetta.default.linuxgccrelease @2LZM_broker.options

• NOTE: This will take 10-20 minutes per structure.

Example data is provided for you in the
~/rosetta_workshop/tutorials/protein_folding/output_files/example_data/
directory.

1. For practice, we will be analyzing data that has already been generated.

1. Copy the files from the output_files/example_data/ directory.

cp output_files/example_data/* .

2. Identify the best scoring models.

1. If you ran more than one job, you will need to combine the silent files into one file.

<path-to-Rosetta>/main/source/bin/combine_silent.default.linuxgccrelease \
-in:file:silent 2LZM_0*.out -in:file:silent_struct_type binary \
-out:file:silent 2LZM_all_models_silent.out -out:file:silent_struct_type binary

2. Format the score and RMS data for graphing using the score_scatter_plot.py script.

python2.7 ~/rosetta_workshop/rosetta/tools/protein_tools/scripts/score_scatter_plot.py \
--x_axis rms_core --y_axis score --silent 2LZM_all_models_silent.out 2LZM_models.table

• The 2LZM_models.table file output is provided for you in the
~/rosetta_workshop/tutorials/protein_folding/output_files/example_analysis/
directory.
3. Sort the 2LZM_models.table by the score column from lowest to highest.

sort -nk3 2LZM_models.table > 2LZM_models_sorted.table

4. Identify the top 5-10 models by score.

head -n 10 2LZM_models_sorted.table

3. Extract the top scoring models from the binary silent file.

~/rosetta_workshop/rosetta/main/source/bin/score_jd2.default.linuxgccrelease \
-in:file:silent 2LZM_all_models_silent.out -in:file:fullatom -out:pdb -out:file:fullatom \
-in:file:tags S_0179_1 S_0205 S_0257 S_0372_1 S_0436_1 S_0437_1 S_0464 S_0134 S_0145 S_0150_1

4. Look at best-scoring models by opening them in PyMol or the molecular graphics program of your choosing.

pymol *.pdb

5. Generate a Score vs. RMSD plot.

The 2LZM_rescore_vs_rmsCore.pdf can be found in the
~/rosetta_workshop/tutorials/protein_folding/output_files/example_analysis/
directory.

1. Determine which model is the lowest-scoring. It is common practice to use the lowest-scoring model as a surrogate for the native structure when analyzing experimental folding results.

head -n 1 2LZM_models_sorted.table

2. The 2LZMA_core.txt file tells Rosetta over which residues to compute CA-RMSD. Copy it from the input_files directory.

cp ~/rosetta_workshop/tutorials/protein_folding/input_files/2LZMA_core.txt .

3. Rescore the models, computing RMSD to the lowest-scoring model.

~/rosetta_workshop/rosetta/main/source/bin/score_jd2.default.linuxgccrelease \
-in:file:silent 2LZM_all_models_silent.out -in:file:silent_struct_type binary \
-in:file:fullatom -out:file:silent 2LZM_all_models_rescored_silent.out \
-out:file:silent_struct_type binary -out:file:fullatom \
-evaluation:rmsd S_0437_1_0001.pdb _core_low 2LZMA_core.txt

4. Make a table of the scores and RMSDs of your models:

grep SCORE 2LZM_all_models_rescored_silent.out | \
awk '{print($NF"\t"$(NF-1)"\t"$2)}' > 2LZM_rescore_vs_rmsd.table  5. Make a scatter plot using the program of your choosing. This plot gives you an idea of the relationship between the Rosetta energy of a model and its quality, or accuracy. There are many ways to generate this scatter plot, among them: 1. For this tutorial use the provided script to run a series of R commands. This script will output the score_vs_rmsCoreLow plot as a PDF file (2LZM_rescore_vs_rmsCoreLow.pdf). The -100 is the maximum energy (worst) which will be ploted. Manually specifying it keeps the plot from being compressed by outliers, but the value would need to be adjusted for different proteins. Rscript \ ~/rosetta_workshop/tutorials/protein_folding/scripts/score_vs_rmsCoreLow.R \ 2LZM_rescore_vs_rmsd.table 2LZM_rescore_vs_rmsCoreLow.pdf -100 \ 2LZM_score_vs_rmsCoreLow  2. Or, use R (http://www.r-project.org) to make these plots. This workshop does not cover the use of R, but example commands are available in the Appendix. 3. Or, read in the 2LZM_score_vs_rmsd.table file into a graphing spreadsheet software (like Excel) and make an X-Y scatter plot. 6. Review the resulting scatter plot. If a quality model has been generated you should observe a funnel-shaped distribution with low-scoring models having low RMSD values and incorrect models with higher RMSD values having higher scores. ## Tutorial 2: Protein Folding with Restraints 1. Prepare your working directory. You will work in this directory for the rest of the tutorial.  cd <path-to-Rosetta>/demos/tutorials/advanced_denovo_structure_prediction  2. Prepare your input files. 1. Generate the constraints (cst) file. The 2LZM_dist_w1.cst file is provided for you in the ~/rosetta_workshop/tutorials/protein_folding/input_files/ directory. 1. Copy the cst file to your working directory. cp input_files/2LZM_dist_w1.cst .  2. Review the cst file by opening it in a text editor. For more information, check the constraint tutorial. 3. The cst file has the basic format: #cst type atom1 resno1 atom2 resno2 function EPR potential dist weight bin_size AtomPair CB 32 CB 36 SPLINE EPR_DISTANCE 16.0 1.0 0.5 AtomPair CB 59 CB 74 SPLINE EPR_DISTANCE 19.0 1.0 0.5 AtomPair CB 62 CB 71 SPLINE EPR_DISTANCE 19.0 1.0 0.5 AtomPair CB 62 CB 74 SPLINE EPR_DISTANCE 25.0 1.0 0.5 AtomPair CB 63 CB 74 SPLINE EPR_DISTANCE 14.0 1.0 0.5  4. Another common type of constraint score is a bounded quadratic penalty: #cst type atom1 resno1 atom2 resno2 function LowerBound Upper StDev comment AtomPair CB 32 CB 36 BOUNDED 11.5 21.5 1.0 NOE ;dist AtomPair CB 59 CB 74 BOUNDED 11.5 21.5 1.0 NOE ;dist AtomPair CB 62 CB 71 BOUNDED 11.5 21.5 1.0 NOE ;dist AtomPair CB 62 CB 74 BOUNDED 12.5 27.5 1.0 NOE ;dist AtomPair CB 63 CB 74 BOUNDED 1.5 16.5 1.0 NOE ;dist  2. Generate the options file. This file provides the options needed to fold soluble proteins, and also contains the Rosetta options needed specifically for folding proteins with restraints (EPR distance restraints in this case). The 2LZM_broker_cst.options file is provided for you in the input_files/ directory. 1. Try generating this options file on your own. Rosetta ignores lines beginning with # (these are treated as comments). 2. Avoid mixing tabs and spaces. Be consistent in your formatting (tab-delimited or colon-separated). 3. Save the file in your working directory or copy from the input_files: $> cp input_files/2LZM_broker_cst.options .

3. Copy the remaining files needed for protein folding from the input_files directory.

$> cp input_files/2LZM_dist_w1.cst .$> cp input_files/2LZMA.pdb . $> cp input_files/2LZMA.fasta .$> cp input_files/aa2LZMA03_05.200_v1_3 . $> cp input_files/aa2LZMA09_05.200_v1_3 .$> cp input_files/2LZMA.psipred_ss2 . $> cp input_files/topology_broker_cst.tpb .$> cp input_files/2LZMA_core.txt .

3. Run the topology broker to de novo fold the input protein guided by experimental restraints.

1. Make sure all the filenames and paths in the options file are correct.
2. Make sure you are in your working directory for the constraints run.
3. Type the following command line:

$> <path-to-Rosetta>/main/source/bin/minirosetta.default.linuxgccrelease @2LZM_broker_cst.options  • NOTE: This will take 15-20 minutes per structure. 4. Analyze your data. Example output files are provided for you in the ~/rosetta_workshop/tutorials/protein_folding/output_files_cst/example_data directory. 1. For practice, we will be analyzing data that has already been generated. 1. Copy the files from the example_data directory. cp output_files_cst/example_outputssc/* .  2. Identify the best scoring models. 1. If you ran more than one job, you will need to combine the silent files into one file. <path-to-Rosetta>/combine_silent.default.linuxgccrelease \ -in:file:silent 2LZM_broker_cst_0*.out -in:file:silent_struct_type binary \ -out:file:silent 2LZM_broker_cst_all.out -out:file:silent_struct_type binary  2. Format the score and RMS data for graphing using the score_scatter_plot.py script. <path-to-Rosetta>/tools/protein_tools/scripts/score_scatter_plot.py \ --x_axis rms_core --y_axis score --silent 2LZM_broker_cst_all.out 2LZM_cst_models.table  • The 2LZM_cst_models.table file is provided for you in the /output_files_cst/example_analysis/ directory. 3. Sort the 2LZM_cst_models.table by the score column from lowest to highest. sort -nk3 2LZM_cst_models.table > 2LZM_cst_models_sorted.table  4. Identify the top 5-10 models by score. head -n 10 2LZM_cst_models_sorted.table  3. Extract the top scoring models from the binary silent file. <path-to-Rosetta>/main/source/bin/score_jd2.default.linuxgccrelease \ -in:file:silent 2LZM_broker_cst_all.out -in:file:silent_struct_type binary \ -in:file:fullatom -out:pdb -out:file:fullatom \ -in:file:tags S_0331_1 S_0260_1 S_0035 S_0387_1 S_0478 S_0272 S_0035_1 \ S_0023 S_0359_1 S_0420_1  4. Re-score the models in order to take the atom_pair_constraint score into account. 1. The default ref2015 weights file doesn't add constraint scores. You have to use a version with the constraints enabled (ref2015_cst). 2. Score your models using these scoring weights. <path-to-Rosetta>/main/source/bin/score_jd2.default.linuxgccrelease \ -in:file:silent 2LZM_broker_cst_all.out -in:file:silent_struct_type binary \ -in:file:fullatom -out:file:silent 2LZM_broker_cst_all_rescore.out \ -out:file:silent_struct_type binary -out:file:fullatom \ -evaluation:rmsd S_0331_1_0001.pdb _core_low 2LZMA_core.txt \ -constraints:cst_fa_file 2LZM_dist_w1.cst -constraints:cst_fa_weight 4 \ -constraints:epr_distance -score:weights ref2015_cst -overwrite  5. Analyze how well the models satisfy the restraints. 1. Create a score_vs_rmsCoreLow table: grep SCORE 2LZM_broker_cst_all_rescore.out | \ awk '{print($2"\t"$14"\t"$(NF-1)"\t"$NF)}' > \ 2LZM_broker_cst_all_score_vs_rmsCoreLow.table  2. You can sort the models by atom_pair_constraint score and see which models satisfy the restraints the best. For example, for 25 restraints weighted by a factor of 4 and scored with the RosettaEPR knowledge-based potential, the best score (100% of restraints satisfied) is -100.00 REU. Often, you want to filter models by some combination of total score and restraint score (see Hirst et al., J. Struct. Biol. 2011). sort -nk2 2LZM_broker_cst_all_score_vs_rmsCoreLow.table | \ head -n10 > top10_atom_pair_constraint_score.txt  • NOTE: Sometimes, it is useful to plot total score (y-axis) vs. atom_pair_constraint score and look at models that are both low-scoring and have good (low) restraint scores. • NOTE: Scripts for generating score vs. rmsCore, score vs. rmsCoreLow, and score vs. atom_pair_constraint figures can be found in the ~/rosetta_workshop/tutorials/protein_folding/scripts directory. 3. You can also see how much the restraints are violated in terms of distance for each restraint in each PDB file. 1. In a bash shell, execute: for pdb in ls *.pdb; do scripts/calc_exp_viol_spline.pl \${pdb} 2LZM_dist_w1.cst 25 > ${pdb}_cst_viol.txt done  2. To get the summary of violations for each PDB, execute: for file in ls *cst_viol.txt; do tail -n1${file} >> all_pdb_viol_summary.txt
done

3. The format for the individual *cst_viol.txt files is as follows:

resA  atomA resB atomB experimental_value distance_in_model violation_magnitude

4. At the end of the *cst_viol.txt file, there is a line that provides the total sum of all violations in that model (sum_current_viol), as well as the maximum distance violation for that model (max_current_viol).

5. OPTIONAL: If you have BOUNDED restraints, you can use the script called calc_exp_viol.pl in the same manner as above.

1. Using make_fragments.pl - (NOTE: The workshop machines are not configured for making fragments.)

1. For more information on the fragment picker, please go to https://www.rosettacommons.org/docs/latest/application_documentation/utilities/app-fragment-picker and see Gront, et al., PLoS ONE, 2011.
2. The make_fragments.pl script is in /tools/fragment_tools/
4. In order to use make_fragments.pl, you will first need to install PSI-BLAST (ftp://ftp.ncbi.nih.gov/blast/executables/blast+/LATEST; Requires non-blast+ NCBI version), the non-redundant (NR) database (ftp://ftp.ncbi.nih.gov/blast/db/), and perhaps PSI-PRED (http://bioinf.cs.ucl.ac.uk/psipred/).
5. You will also need a Vall database, which is in the above directory (vall.jul19.2011.gz)
6. You will need to modify make_fragments.pl in order to reflect the paths specific to your case (we will not do this during the workshop). The comments in the scripts should tell you how to modify paths.*
7. For a usage statement, run the script without arguments:

<path-to-Rosetta>/tools/fragment_tools/make_fragments.pl

8. To run in the directory where your fasta file is located:

<path-to-Rosetta>/tools/fragment_tools/make_fragments.pl 2LZMA.fasta >& make_fragments.log &

9. OPTIONAL: You can use any other secondary structure prediction method, such as JUFO (http://www.meilerlab.org/index.php/servers/show?s_id=5) or Porter (http://distillf.ucd.ie/porterpaleale/), but it must be in PSI-PRED ss2 vertical format. This can be done by running:

<path-to-Rosetta>/tools/fragment_tools/ss_pred_converter.py \
-j 2LZMA.jufo_ss > 2LZMA.jufo_ss2


or

<path-to-Rosetta>/rosetta/tools/fragment_tools/ss_pred_converter.py \
-p 2LZMA.porter_ss > 2LZMA.porter_ss2

2. Using R (http://www.r-project.org) to generate score vs. rmsd plots.

1. Open R by typing R on the command line.
2. Read in the score table and put into a data object called data:

data<-read.table("2LZM_rescore_vs_rmsd.table", header=TRUE)

3. Look at the first line of data:

data[1,]

4. Plot score vs. rms_core_low:

plot(data$rms_core_low,data$score,pch=4,cex.axis=1.5,xlab="",ylab="",main="")

5. You may have to play with the range of the y-axis to see the distribution and excluding outliers.

plot(data$rms_core_low,data$score,pch=4,cex.axis=1.5,xlab="",ylab="", \
main="",ylim=range(min(data\$score),-100))

title(main="2LZM_score_vs_rmsCoreLow",xlab="rms_core_low (Angstroms)", \