Merge remote-tracking branch 'origin/readthedocs-update' into readthedocs-update

Author: yusuferentunc

README.md

@@ -16,24 +16,28 @@ However, the identification of Lo or Ld domains from coarse grained or atomistic
 
 DomHMM is bound by a [Code of Conduct](https://github.com/BioMemPhys-FAU/domhmm/blob/main/CODE_OF_CONDUCT.md).
 
-### Installation
+Features
+--------------
+
+With DomHMM you can:
+
+- Calculate the area per lipid for each lipid in every leaflet.
+- Compute the average S<sub>CC</sub> parameter for each acyl chain of every lipid.
+- Easily identify lateral nano- and microdomains in your membrane simulations.
+
+Installation
+--------------
 
 To build DomHMM from source,
 we highly recommend using virtual environments.
 If possible, we strongly recommend that you use
 [Anaconda](https://docs.conda.io/en/latest/) as your package manager.
-Below we provide instructions both for `conda` and
+Below we provide instructions both for `conda`/`mamba` and
 for `pip`.
 
-First clone repository:
-```
-git clone https://github.com/BioMemPhys-FAU/domhmm
-cd domhmm
-```
-
-#### With conda
+#### Virtual environment
 
-Ensure that you have [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) installed.
+Ensure that you have [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) or [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) installed.
 
 Create a virtual environment and activate it:
 
@@ -42,7 +46,29 @@ conda create --name domhmm
 conda activate domhmm
 ```
 
-Install the development and documentation dependencies:
+Or,
+```
+mamba create --name domhmm
+mamba activate domhmm
+```
+
+#### Installing directly from the repository without cloning
+
+If you want to install the latest version of DomHMM directly from the repository (either from the `main` branch or any other branch), you can use the following command:
+
+```
+python3 -m pip install git+https://github.com/BioMemPhys-FAU/domhmm@main
+```
+
+#### Installing directly from the repository with cloning
+
+First clone repository:
+```
+git clone https://github.com/BioMemPhys-FAU/domhmm
+cd domhmm
+```
+
+Install the development and documentation dependencies (with conda or with mamba):
 
 ```
 conda env update --name domhmm --file devtools/conda-envs/test_env.yaml
@@ -76,6 +102,32 @@ the dependencies required for tests and docs with:
 pip install -e ".[test,doc]"
 ```
 
+Our test suite requires the installation of `pytest`. To install this package via `conda` or `mamba` run:
+
+```
+conda install pytest
+```
+
+Or,
+```
+mamba install pytest
+```
+
+To run the test, first clone the repository
+```
+git clone https://github.com/BioMemPhys-FAU/domhmm
+```
+
+Change into the directory and run `pytest`,
+
+```
+cd domhmm
+pytest --pyargs domhmm
+```
+
+Usage
+--------------
+
 ### Example Script
 You can use DomHMM library like in this example script
 ```

docs/source/how-to-run.rst

@@ -119,6 +119,10 @@ Let's dive into each parameter's details.
 Optional Parameters
 -------------------
 
+* ``asymmetric_membrane``
+
+It needs to be enabled if leaflets are not symmetric. With this option, models are fitted by separated data for each leaflets.
+
 * ``frac``
 
 Fraction of box length in x and y outside the unit cell considered for area per lipid calculation by Voronoi. It is an optimization process parameter which is set to 0.5 as default.
@@ -171,4 +175,17 @@ Parameter option for reusing past DomHMM HMM models. If there are several analys
     model_2 = domhmm.PropertyCalculation( ... ,
                                          trained_hmms=reuse_hmm_models)
 
+* ``tmd_protein_list``
+
+Transmembrane domain (tmd) protein list to include area per lipid calculation. Since tmd proteins are take up space in upper, lower or both leaflets, three backbone atoms of protein for each leaflet should be included as in this parameter to increase success of identification.
+
+.. code-block::
+
+    # Selecting three backbone atoms that is touching to upper leaflet
+    upBB = uni.select_atoms('name BB')[0:3]
+    # Selecting three backbone atoms that is touching to lower leaflet
+    loBB = uni.select_atoms('name BB')[-3:]
+    # List can be expended with multiple dictionary objects as in more than one tmd protein scenarios.
+    tmd_protein_list = [{"0": upBB, "1": loBB}]
+
 We encourage to check :doc:`tips` section that may contain useful information for your progress.

domhmm/tests/test_domhmm.py

@@ -183,12 +183,12 @@ def test_domhmm_imported(self):
         """
         assert "domhmm" in sys.modules
 
-    def test_run(self, analysis):
-        """
-        Demo run with standard options
-        """
-        analysis.run(start=0, stop=100)
-        self.result_parameter_check(analysis, "analysis")
+    # def test_run(self, analysis):
+    #     """
+    #     Demo run with standard options
+    #     """
+    #     analysis.run(start=0, stop=100)
+    #     self.result_parameter_check(analysis, "analysis")
 
     def test_run_reuse_hmm_model(self, analysis_reuse_hmm_model):
         """

examples/tmd-protein-example/main.py

@@ -0,0 +1,41 @@
+import os
+
+import MDAnalysis as mda
+
+import domhmm
+
+if __name__ == "__main__":
+    # MDAnalysis universe for membrane simulation
+    data_dir = os.path.dirname(__file__)
+    path2xtc = os.path.join(data_dir, "md_center_mol.xtc")
+    path2tpr = os.path.join(data_dir, "mem.tpr")
+    uni = mda.Universe(path2tpr, path2xtc)
+
+    # Selecting three backbone atoms that is touching to upper leaflet
+    upBB = uni.select_atoms('name BB')[0:3]
+    # Selecting three backbone atoms that is touching to lower leaflet
+    loBB = uni.select_atoms('name BB')[-3:]
+
+    tmd_protein_list = [{"0": upBB, "1": loBB}]
+    membrane_select = "resname PUPC POPC CHOL"
+    heads = {"PUPC": "PO4", "POPC": "PO4"}
+    tails = {"POPC": [["C1B", "C2B", "C3B", "C4B"], ["C1A", "D2A", "C3A", "C4A"]],
+             "PUPC": [["C1B", "C2B", "C3B", "C4B"], ["D1A", "D2A", "D3A", "D4A", "D5A"]]}
+    sterol_heads = {"CHOL": "ROH"}
+    sterol_tails = {"CHOL": ["ROH", "C1"]}
+
+    # leaflet_kwargs should contain all head group atoms/molecules of lipids for LeafletFinder function
+    model = domhmm.PropertyCalculation(universe_or_atomgroup=uni,
+                                       leaflet_kwargs={"select": "name P*", "pbc": True},
+                                       membrane_select=membrane_select,
+                                       leaflet_select="auto",
+                                       heads=heads,
+                                       sterol_heads=sterol_heads,
+                                       sterol_tails=sterol_tails,
+                                       tails=tails,
+                                       result_plots=True,
+                                       tmd_protein_list=tmd_protein_list,
+                                       )
+
+    # Run option can be updated by parameters such as start=0, stop=100, step=5
+    model.run(start=0, stop=100)