schrodinger.application.bioluminate.epitope.psc_workflow module

schrodinger.application.bioluminate.epitope.psc_workflow.save_mkdir(odir, overwrite)

schrodinger.application.bioluminate.epitope.psc_workflow.gen_fastas(spreadsheet_file=None, output_dir=None, overwrite=False)

Read an Excel spreadsheet that contains at least 3 columns with the column names, ‘ID’, ‘VH’ and ‘VL’, indicating the names/labels of the antibodies, heavy chain and light chain sequences, respectively.

class schrodinger.application.bioluminate.epitope.psc_workflow.psc_workflow(project_name, fastas_dir, overwrite_data=False)

Bases: object

Class that creates an paratope similarity clustering workflow object

util_scripts_dir = './utils/'

numbering_scheme = 'EnhancedChothia'

nmodels = 1

S = None

D = None

D1d = None

L = None

N_fab = None

ID_fab = None

plot_width = 7

plot_height = 6

dpi = 300

__init__(project_name, fastas_dir, overwrite_data=False)

fastas_dir = None

project_name = None

overwrite_data = None

data_dir = None

fab_dir = None

build_ab_log_dir = None

faux_epi_dir = None

mif_dir = None

safe_mkdir(dir)

gen_file_list(dir)

gen_names(dir)

gen_jobname(filename)

Remove ‘.fasta’ from the fastas file name and use the base for the jobname.

save_mat_csv(mat, IDs, mat_name)

build_ab(fasta_file, jobname)

Given a fasta file containing the sequences of Vh and Vl domains, create an antibody model.

gen_faux_epitopes()

Generate faux epitopes from Fabs Maestro files in the fab_dir

gen_mifs()

Generate MIFs

gen_sim_mat()

Compute the pairwise similarity matrix between binding sites using Phase Shape approach. Compare all i,j pairs of MIFs in the mif_dir.

sim2dist()

Convert the simularity matrix S in to a symmetric, normalizd distance matrix D

cond_1d_dist_mat(triag='upper')

Convert the fully symmetric distance matrix into a condensed 1D distance matrix triag = ‘upper’ or ‘lower’, specifying whether the upper or lower triangle to use. It shouldn’t matter since D is fully symmetric and both should yield identical results.

plot_mat(data, plot_title, cmap)

generate_dendrogram(cutoff)

Perform clustering using the distance matrix

build_antibody_models(debug)

Build antibody models from the fasta sequences

run_full_workflow(cutoff, debug=False)

Main driver that executes the entire workflow

clean()