From 8ef6a2c59222004b8f67f6396d0b539f154703a4 Mon Sep 17 00:00:00 2001 From: terwill Date: Mon, 4 Oct 2021 09:59:24 -0700 Subject: [PATCH] Update test to include maximum_fraction_close --- mmtbx/regression/tst_process_predicted_model.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/mmtbx/regression/tst_process_predicted_model.py b/mmtbx/regression/tst_process_predicted_model.py index 0ddd0bddcb..f996c809ef 100644 --- a/mmtbx/regression/tst_process_predicted_model.py +++ b/mmtbx/regression/tst_process_predicted_model.py @@ -83,6 +83,7 @@ def tst_01(log = sys.stdout): # mark_atoms_to_ignore_with_occ_zero print("\nConverting lddt to B values and using mark_atoms_to_ignore_with_occ_zero", file = log) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'lddt' params.process_predicted_model.remove_low_confidence_residues = True params.process_predicted_model.maximum_rmsd = 1.5 @@ -96,11 +97,10 @@ def tst_01(log = sys.stdout): assert model_occ_values.count(1) == n1 assert model_occ_values.count(0) == n2 - - # use process_predicted_model to convert lddt or rmsd to B print("\nConverting lddt to B values", file = log) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'lddt' params.process_predicted_model.remove_low_confidence_residues = False params.process_predicted_model.split_model_by_compact_regions = False @@ -117,6 +117,7 @@ def tst_01(log = sys.stdout): ph.atoms().set_b(fractional_lddt) test_model = model.as_map_model_manager().model_from_hierarchy(ph, return_as_model = True) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'lddt' params.process_predicted_model.remove_low_confidence_residues = False params.process_predicted_model.split_model_by_compact_regions = False @@ -132,6 +133,7 @@ def tst_01(log = sys.stdout): return_as_model = True) print("\nConverting rmsd to B values", file = log) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'rmsd' params.process_predicted_model.remove_low_confidence_residues = False params.process_predicted_model.split_model_by_compact_regions = False @@ -145,11 +147,13 @@ def tst_01(log = sys.stdout): (model_b_values > 59).count(True), model_b_values.size()), file = log) print("\nConverting rmsd to B values and selecting rmsd < 1.5", file = log) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'rmsd' params.process_predicted_model.remove_low_confidence_residues = True params.process_predicted_model.maximum_rmsd = 1.5 params.process_predicted_model.split_model_by_compact_regions = False params.process_predicted_model.input_lddt_is_fractional = None + model_info = process_predicted_model(test_model, params) model = model_info.model print("Residues before: %s After: %s " %( @@ -158,7 +162,8 @@ def tst_01(log = sys.stdout): # Check splitting model into domains print("\nSplitting model into domains", file = log) - model_info = split_model_into_compact_units(model, log = log) + model_info = split_model_into_compact_units(model, + maximum_fraction_close = 0.5, log = log) chainid_list = model_info.chainid_list print("Segments found: %s" %(" ".join(chainid_list)), file = log) @@ -167,7 +172,7 @@ def tst_01(log = sys.stdout): # Check processing and splitting model into domains print("\nProcessing and splitting model into domains", file = log) - + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'lddt' params.process_predicted_model.remove_low_confidence_residues = True params.process_predicted_model.maximum_rmsd = 1.5 @@ -201,6 +206,7 @@ def tst_01(log = sys.stdout): print("\nProcessing and splitting model into domains with pae", file = log) + params.process_predicted_model.maximum_fraction_close = 0.5 params.process_predicted_model.b_value_field_is = 'lddt' params.process_predicted_model.remove_low_confidence_residues = True params.process_predicted_model.maximum_rmsd = 0.7