public.antibody_utils

RAbD_Jade.py

GUI application to analyze designs output by RosettaAntibodyDesign. Designs should first be analyzed by both the AntibodyFeatures and CDRClusterFeatures reporters into sqlite3 databases.

usage: RAbD_Jade.py [-h] [--db_dir DB_DIR] [--analysis_name ANALYSIS_NAME]
                    [--native NATIVE] [--root_dir ROOT_DIR]
                    [--cdrs [{L1,H1,L1,H2,L3,H3} [{L1,H1,L1,H2,L3,H3} ...]]]
                    [--pyigclassify_dir PYIGCLASSIFY_DIR]
                    [--jsons [JSONS [JSONS ...]]]

Named Arguments

--db_dir	Directory with databases to compare. DEFAULT = databases Default: “databases”
--analysis_name
	Main directory to complete analysis. DEFAULT = prelim_analysis Default: “prelim_analysis”
--native	Any native structure to compare to
--root_dir	Root directory to run analysis from Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs”
--cdrs	Possible choices: L1, H1, L1, H2, L3, H3 A list of CDRs for the analysis (Not used for Features Reporters) Default: [‘L1’, ‘L2’, ‘L3’, ‘H1’, ‘H2’, ‘H3’]
--pyigclassify_dir
	Optional PyIgClassify Root Directory with DBOUT. Used for debugging. Default: “”
--jsons, -j	Analysis JSONs to use. See RAbD_MB.AnalysisInfo for more on what is in the JSON.The JSON allows us to specify the final name, decoy directory, and features db associated with the benchmark as well as all options that went into it.

convert_IMGT_to_fasta.py

This script converts an IMGT output file (5_AA-seqs.csv) to a FASTA. All Framework and CDRs are concatonated. * is skipped. The FASTA file can then be used by PyIgClassify.

usage: convert_IMGT_to_fasta.py [-h] --inpath INPATH --outpath OUTPATH

Named Arguments

--inpath, -i	Input IMGT file path
--outpath, -o	Output Fasta outfile path.

create_features_json.py

This script will create either cluster features or antibody features json for use in Features R script. Example Cmd-line: python create_features_json.py –database databases/baseline_comparison.txt –scripts cluster

usage: create_features_json.py [-h] [--databases [DATABASES [DATABASES ...]]]
                               [--script {cluster,antibody,interface,antibody_minimal}]
                               [--db_path DB_PATH] [--outdir OUTDIR]
                               [--outname OUTNAME]
                               [--add_comparison_to_this_json ADD_COMPARISON_TO_THIS_JSON]
                               [--run]

Named Arguments

--databases, -l
	List of dbs: db_name,short_name,ref keyword if the reference databaseSeparated by white space. Default: []
--script, -s	Possible choices: cluster, antibody, interface, antibody_minimal Script type. Will setup the appropriate output formats and R scripts Default: “antibody_minimal”
--db_path, -p	Path to databases. Default is pwd/databases Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs/databases”
--outdir, -o	Where to put the result of the analysis scripts. Currently unsupported by the features framework. Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs/plots”
--outname, -n	Output file name of json file Default: “local_json_compare_ss.json”
--add_comparison_to_this_json, -a
	Add all this data to this json as more sample sources.
--run, -r	Go ahead and run compare_sample_sources.R. Must be in path!! Default: False

generate_rabd_features_dbs.py

Generates RAbD Features DBs using RunRosettaMPI in db mode.

usage: generate_rabd_features_dbs.py [-h]

match_antibody_structures.py

This App aims to make pymol alignments using the PyIgClassify database and structures, matching specific criterion.

usage: match_antibody_structures.py [-h] --db DB --ab_dir AB_DIR --where WHERE
                                    [--outdir OUTDIR] [--prefix PREFIX]
                                    [--cdr CDR] [--native NATIVE]

Required Arguments

--db, -d	Database to use from PyIgClassify.
--ab_dir, -b	Directory with renumbered antibody PDBs (Full or CDRs-only)
--where, -w	Your where clause for the db in quotes. Not including WHERE. Use ‘ ‘ for string matches

Other Arguments

--outdir, -o	Output directory. Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs”
--prefix, -p	Output prefix
--cdr, -c	Optionally load the CDR PDBs of the given type in the ab_dir. If this option is set, the ab_dir should be of CDRs only from PyIgClassify.
--native, -n	Align everything to this PDB, the native or something you are interested in.

order_ab_chains.py

Reorders PDBFiles in a dirctory according to A_LH in order for Rosetta Antibody Design benchmarking. Removes HetAtm

usage: order_ab_chains.py [-h] [--in_dir IN_DIR] [--in_pdblist IN_PDBLIST]
                          [--in_single IN_SINGLE] [--out_dir OUT_DIR]
                          [--reverse]

Named Arguments

--in_dir, -i	Input Directory of PDB files listed in any passed PDBLIST. Default=PWD Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs”
--in_pdblist, -l
	Input PDBList file. Assumes PDBList has no paths and requires an input directory as if we run Rosetta. Default: “”
--in_single, -s
	Path to Input PDB File, instead of list. Default: “”
--out_dir, -d	Output Directory. Resultant PDB files will go here. Default: “reordered”
--reverse, -r	Reverse order (LH_A instead of A_LH). Used for snugdock Default: False

split_antibody_components.py

Script for splitting AHO renumbered antibodies into Fv, Fc, and linker regions

usage: split_antibody_components.py [-h] [--any_structure] --ab_dir AB_DIR
                                    --output_dir OUTPUT_DIR

Named Arguments

--any_structure
	Be default, we only output structures with both L/H. Pass this option to split structures that are L or H only. Default: False
--ab_dir, -a	Antibody Directory with AHO-renumbered structures to split. Can be .pdb, or .pdb.gz
--output_dir, -o
	Output Directory for antibody structures.