From ff7fa928f007496538e491a54ab7bf6a6208c7e5 Mon Sep 17 00:00:00 2001 From: Gabriel Studer <gabriel.studer@unibas.ch> Date: Fri, 10 Jul 2020 12:29:42 +0200 Subject: [PATCH] README updates --- other/mod_pipeline/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/other/mod_pipeline/README.md b/other/mod_pipeline/README.md index b85a900..27c42a8 100644 --- a/other/mod_pipeline/README.md +++ b/other/mod_pipeline/README.md @@ -14,7 +14,7 @@ instance which is not publicly available. The steps are nevertheless documented here for internal reference. - Fetch a SWISS-MODEL project with already executed template search specific to - our target sequence. 'BF.sm' contains such a project. + our target sequence. *BF.sm* contains such a project. - Execute `sm fetch_data.py` to extract templates, alignments and profiles. They are dumped in *data* and all the info is summarized in *data.csv*. @@ -23,11 +23,11 @@ Modelling and evaluation - In a first step we model the target with all available templates by executing `pm build_models_from_all_templates.py`. This reads the information in - *data.csv* to build and dump the models in *models*. + *data.csv* and builds/dumps the models in *models*. - Executing `pm score_all_models.py` compares all previously built models to *target.pdb* and prints the respective lDDT scores. - The custom modelling pipeline is defined in the two scripts denoted as - listing_2.py and listing_3.py which refer to the listings in the main + *listing_2.py* and *listing_3.py* which refer to the listings in the main manuscript. Executing `pm listing_2.py` loads all templates in *data* and creates the custom StructureDB and FragDB. The final model gets built with the custom -- GitLab