Version 1

bin/SASA.py

@@ -78,6 +78,7 @@ def SASA():  #The main.
 
         dico[pos] = (name_res, num_res, round(s_exp_abs_res, 2), round(s_exp_rel_res, 2), round(perc_res, 2))  #In homodimers, residue names and numbers are the same in both chains. For this reason, pos (position) is used as key.
 
+    print(dico)
     headers = ["POS", "RES", "NUM", "S-ABS", "S-REL", "PERC"]
     print(tabulate([(k,) + v for k, v in dico.items()], headers=headers))
 
@@ -86,8 +87,8 @@ def SASA():  #The main.
 
     print(f"--- {(time.time() - start_time):.2f} seconds ---")  #Time taken for the program execution.
 
-#NB: The main has no return                   
-#Example of use : python3 SASA.py -pdb data/pdb/1bzv.pdb -n 92
+#NB: The main has no return.                  
+#Example of use : python3 bin/SASA.py -pdb data/pdb/1bzv.pdb -n 92
 
 
 if __name__ == "__main__":  #If the program SASA.py is executed as a script in a shell, the result of the if test will be True and the corresponding instruction block will be executed.

bin/tools.py

@@ -26,13 +26,10 @@ def atomic_coordinates(pdb):
         Return: 
             df: DataFrame containing the atomic coordinates and the other mentioned associated data.
     """
-
     with open(pdb, "r") as pdb_file:  #Open the PDB file in read "r" mode.                                                                  
-
         atom_data = {"res_name" : [], "res_num" : [], "atom_name" : [], "x" : [], "y" : [], "z" : []}  #Declare a dictionary, {key : value}, in which values = empty lists.
         atoms_names, res_names, res_nums, x, y, z = [], [], [], [], [], []  #Create empty lists. 
         #count_atoms = 0 #If we want to count the number of atoms. 
-
         for line in pdb_file:
             if line.startswith("ATOM"):  #Take only the lines starting with ATOM. These lines contain the protein atoms. (It is necessary to exclude the other lines, e.g. the lines starting with HETATM (= correspond to atoms that belong to other molecules, e.g. ions and H2O).                                                               
                 atoms_names.append(line[13].strip())  #Take only the first letter of an atom name (e.g. C instead of CA, CB, ...), remove any leading (spaces at the beginning) and trailing (spaces at the end) characters with the method .strip(), and add it to the list atoms_names using the method .append().                                               
@@ -41,17 +38,13 @@ def atomic_coordinates(pdb):
                 x.append(float(line[30:38]))  #Extracte the x coordinate, and add it to the list called x.
                 y.append(float(line[38:46]))
                 z.append(float(line[46:54]))
-                #count_atoms += 1
-
+                #count_atoms += 1    
         #"This protein contains {} atoms".format(count_atoms)
-
         atom_data["res_name"] = res_names  #Assign the list res_names to the key "res_name".
         atom_data["res_num"] = res_nums 
         atom_data["atom_name"] = atoms_names
         atom_data["x"], atom_data["y"], atom_data["z"] = x, y, z
-
         df = pd.DataFrame(atom_data)  #Create a DataFrame with the atom_data dictionary, (the keys become the column names).
-
     return df
 
 
@@ -66,26 +59,21 @@ def sphere(n, center, r_vdw):
         Return: 
             points_coordinates: Array containing the points coordinates. 
     """
-
     ind = np.arange(n)  #Create an array (vector) object of n integers. 
     phi = np.pi * (3 - np.sqrt(5))  #Golden angle (= 2.39996 rad, 137,51 °)
     theta = phi * ind  #Golden angle increment/Theta angle 
     z = np.linspace(start=1 , stop=-1, num=n)  #Create the variable z with the function linspace() which returns an array of n evenly spaced numbers over the specified interval, [1, -1]. (=Z axis)  
     r = np.sqrt(1 - z**2)  #Circles radius 
     r_sphere = r_vdw + 1.4  #Sphere defined radius (= sphere size): Van der Waals radius of an atom + Van der Waals radius of Oxygen (= 1.4).
-
     #Points coordinates: 
     z = np.add(z * r_sphere, center[2])
     x = np.add(r * np.cos(theta) * r_sphere, center[0])
-    y = np.add(r * np.sin(theta) * r_sphere, center[1])
-
+    y = np.add(r * np.sin(theta) * r_sphere, center[1]) 
     points_coordinates = np.zeros(shape=(n, 3))  #Create an array of given shape (= n sequences containing 3 zeros each).
     points_coordinates[:, 0] = x  #Replace the first zero of each sequence by the x coordinates.
     points_coordinates[:, 1] = y 
     points_coordinates[:, 2] = z
-
     #pp.figure().add_subplot(projection="3d").scatter(x, y, z)  #If we want to visualize the sphere.
-
     return points_coordinates#, pp.show()
 
 
@@ -98,7 +86,6 @@ def check_distance(r_vdw, distance):
         Return: 
             distance < r_vdw + 1.4: True or False. 
     """
-
     return distance < (r_vdw + 1.4)  #True (= hidden points) or False (= solvent-exposed points).
 
 
@@ -113,9 +100,7 @@ def surface_exp(r_vdw, points_exp, n):
             s_exp: Sphere's exposed surface area.
             s_tot: Sphere's total surface area (exposed + hidden).
     """
-
     s_tot = 4 * np.pi * (r_vdw + 1.4)**2  #Surface area of a sphere.
     ratio_exp = points_exp / n  #Accessibility ratio. 
     s_exp = s_tot * ratio_exp  #Cross-multiplication.
-
     return s_exp, s_tot  

environment.yml

@@ -4,11 +4,11 @@ channels:
   - anaconda
 dependencies:
   - python=3.10
-  - matplotlib
   - numpy
   - pandas
+  - matplotlib
   - tqdm
-  - joblib
   - tabulate
+  - joblib