diff --git a/other/mod_pipeline/README.md b/other/mod_pipeline/README.md
index b85a900ec286396b01608b7c3a7404ec0938c180..27c42a82cb322b3c93add0b8e2919294f1993467 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