Reconstructed the whole project, 2.0 release

Author: Gabor Erdos

PyRAMA.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+
+"""
+Written by Gábor Erdős, 2017
+Contact info: gerdos[at]caesar.elte.hu
+
+The preferences were calculated from the following artice:
+Lovell et al. Structure validation by Calpha geometry: phi,psi and Cbeta deviation. 2003
+DOI: 10.1002/prot.10286
+"""
+
+import sys
+from rama_lib.ramachandran_calc import ramachandran
+if len(sys.argv) < 2:
+    sys.exit("Usage: pyrama my_pdb_file.pdb")
+
+ramachandran(sys.argv[1:])

bin/pyrama

@@ -0,0 +1,124 @@
+import math
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+from Bio import PDB
+from matplotlib import colors
+
+
+def ramachandran(file_name_list):
+    """
+    Main calculation and plotting definition
+    :param file_name_list: List of PDB files to plot
+    :return: Nothing
+    """
+    # General variable for the background preferences
+    rama_preferences = {
+        "General": {
+            "file": "data/pref_general.data",
+            "cmap": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),
+            "bounds": [0, 0.0005, 0.02, 1],
+        },
+        "GLY": {
+            "file": "data/pref_glycine.data",
+            "cmap": colors.ListedColormap(['#FFFFFF', '#FFE8C5', '#FFCC7F']),
+            "bounds": [0, 0.002, 0.02, 1],
+        },
+        "PRO": {
+            "file": "data/pref_proline.data",
+            "cmap": colors.ListedColormap(['#FFFFFF', '#D0FFC5', '#7FFF8C']),
+            "bounds": [0, 0.002, 0.02, 1],
+        },
+        "PRE-PRO": {
+            "file": "data/pref_preproline.data",
+            "cmap": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),
+            "bounds": [0, 0.002, 0.02, 1],
+        }
+    }
+
+    # Read in the expected torsion angles
+    __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    rama_pref_values = {}
+    for key, val in rama_preferences.items():
+        rama_pref_values[key] = np.full((360, 360), 0, dtype=np.float64)
+        with open(os.path.join(__location__, val["file"])) as fn:
+            for line in fn:
+                if not line.startswith("#"):
+                    # Preference file has values for every second position only
+                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 180] = float(
+                        line.split()[2])
+                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 179] = float(
+                        line.split()[2])
+                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 180] = float(
+                        line.split()[2])
+                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 179] = float(
+                        line.split()[2])
+
+    normals = {}
+    outliers = {}
+    for key, val in rama_preferences.items():
+        normals[key] = {"x": [], "y": []}
+        outliers[key] = {"x": [], "y": []}
+
+    # Calculate the torsion angle of the inputs
+    for inp in file_name_list:
+        if not os.path.isfile(inp):
+            print("{} not found!".format(inp))
+            continue
+        structure = PDB.PDBParser().get_structure('input_structure', inp)
+        for model in structure:
+            for chain in model:
+                polypeptides = PDB.PPBuilder().build_peptides(chain)
+                for poly_index, poly in enumerate(polypeptides):
+                    phi_psi = poly.get_phi_psi_list()
+                    for res_index, residue in enumerate(poly):
+                        res_name = "{}".format(residue.resname)
+                        res_num = residue.id[1]
+                        phi, psi = phi_psi[res_index]
+                        if phi and psi:
+                            if str(poly[res_index + 1].resname) == "PRO":
+                                aa_type = "PRE-PRO"
+                            elif res_name == "PRO":
+                                aa_type = "PRO"
+                            elif res_name == "GLY":
+                                aa_type = "GLY"
+                            else:
+                                aa_type = "General"
+                            if rama_pref_values[aa_type][int(math.degrees(psi)) + 180][int(math.degrees(phi)) + 180] < \
+                                    rama_preferences[aa_type]["bounds"][1]:
+                                print("{} {} {} {}{} is an outlier".format(inp, model, chain, res_name, res_num))
+                                outliers[aa_type]["x"].append(math.degrees(phi))
+                                outliers[aa_type]["y"].append(math.degrees(psi))
+                            else:
+                                normals[aa_type]["x"].append(math.degrees(phi))
+                                normals[aa_type]["y"].append(math.degrees(psi))
+
+    # Generate the plots
+    for idx, (key, val) in enumerate(sorted(rama_preferences.items(), key=lambda x: x[0].lower())):
+        plt.subplot(2, 2, idx + 1)
+        plt.title(key)
+        plt.imshow(rama_pref_values[key], cmap=rama_preferences[key]["cmap"],
+                   norm=colors.BoundaryNorm(rama_preferences[key]["bounds"], rama_preferences[key]["cmap"].N),
+                   extent=(-180, 180, 180, -180))
+        plt.scatter(normals[key]["x"], normals[key]["y"])
+        plt.scatter(outliers[key]["x"], outliers[key]["y"], color="red")
+        plt.xlim([-180, 180])
+        plt.ylim([-180, 180])
+        plt.plot([-180, 180], [0, 0], color="black")
+        plt.plot([0, 0], [-180, 180], color="black")
+        plt.locator_params(axis='x', nbins=7)
+        plt.xlabel(r'$\phi$')
+        plt.ylabel(r'$\psi$')
+        plt.grid()
+
+    plt.tight_layout()
+    # plt.savefig("asd.png", dpi=300)
+    plt.show()
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        sys.exit("Usage: ramachandran_calc.py my_pdb_file.pdb")
+    ramachandran_calc(sys.argv[1:])

rama_lib/__init__.py

@@ -2,12 +2,14 @@
 
 setup(
     name='pyrama',
-    version='1.0',
-    packages=[''],
+    version='2.0.1',
+    scripts=['bin/pyrama'],
+    packages=['rama_lib'],
+    package_data={'rama_lib': ['data/*.data']},
     url='https://github.com/gerdos/PyRAMA',
     license='',
-    package_data={'': ['*.data']},
     author='gerdos',
     author_email='gerdos@caesar.elte.hu',
-    description='Ramachandran plot generator'
+    description='Ramachandran plot generator',
+    install_requires=['biopython', 'numpy', 'matplotlib']
 )