Coverage for biobb_io/api/memprotmd_sim_search.py: 91%
33 statements
« prev ^ index » next coverage.py v7.10.6, created at 2025-09-04 08:31 +0000
1#!/usr/bin/env python
3"""Module containing the MemProtMDSimSearch class and the command line interface."""
5from typing import Optional
6from biobb_common.generic.biobb_object import BiobbObject
7from biobb_common.tools.file_utils import launchlogger
9from biobb_io.api.common import check_output_path, get_memprotmd_sim_search, write_json
12class MemProtMDSimSearch(BiobbObject):
13 """
14 | biobb_io MemProtMDSimSearch
15 | This class is a wrapper of the MemProtMD to perform advanced searches in the MemProtMD DB using its REST API.
16 | Wrapper for the `MemProtMD DB REST API <http://memprotmd.bioch.ox.ac.uk/>`_ to perform advanced searches.
18 Args:
19 output_simulations (str): Path to the output JSON file. File type: output. `Sample file <https://github.com/bioexcel/biobb_io/raw/master/biobb_io/test/reference/api/output_sim_search.json>`_. Accepted formats: json (edam:format_3464).
20 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
21 * **collection_name** (*str*) - ("refs") Name of the collection to query.
22 * **keyword** (*str*) - (None) String to search for in the database metadata. Examples are families like gpcr or porin. Values: porin, outer membrane protein, membrane protein, gpcr (7-transmembrane domain receptors transducing extracellular signals into cells), ion channels, rhodopsin (The most famous GPCRs), abc, mip (Major Intrinsic Protein (MIP)/FNT superfamily: specific for the transport of water and small neutral solutes), ligand-gated (Ligand-dependent signal conversion from chemical signals to electric signals), ammonia (Regulating transepithelial ammonia secretion), mapeg (Eicosanoid and Glutathione metabolism proteins), transmembrane (Heme biosynthesis), protein, kinase (Tyrosine-protein kinases: regulate central nervous system; gene transcription and cell differentiation), glycoprotein (Expression of TCR complex), immunoglobulin (Recognition; binding and adhesion process of cells), integrin (Bridges for cell-cell and cell-extracellular matrix interaction), bnip3 (BNip3 protein family: protect cell from apoptosis), bcl-2 (Regulating cell-death; either induce apoptotic or inhibit apoptosis), atpase (ATPase regulators; P-P-bond hydrolysis-driven transporter), cytochrome (Terminal oxidase enzyme in electron transfer chain), nadp (Transmembrane proteins with NAD(P)-binding Rossmann-fold domains: monoamine oxidase; deaminates norepinephrine; epinephrine; serotonin and dopamine), a4 (Amyloid beta A4 protein; involved in alzheimer's diseases), lysosome (Lysosome-associated membrane glycoprotein: specific to lysosomes; CD107), necrosis (Tumor necrosis factor recepto: binding with TNF and NGF; interacting with a variety of signal molecules; highly associated with apoptosis), oxidoreductase (DHODH; biosynthesis of orotate), ceramidase (Neutral/alkaline ceramidase: converting sphingolipid to sphingosine), dehydrogenase (Aldehyde dehydrogenase:ALDH; Oxidation of aldehydes), mitochondrial, plastid.
23 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
24 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
25 * **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
27 Examples:
28 This is a use example of how to use the building block from Python::
30 from biobb_io.api.memprotmd_sim_search import memprotmd_sim_search
31 prop = {
32 'collection_name': 'refs',
33 'keyword': 'porin'
34 }
35 memprotmd_sim_search(output_simulations='/path/to/newSimulationSearch.json',
36 properties=prop).launch()
38 Info:
39 * wrapped_software:
40 * name: MemProtMD DB
41 * license: Creative Commons
42 * ontology:
43 * name: EDAM
44 * schema: http://edamontology.org/EDAM.owl
46 """
48 def __init__(self, output_simulations, properties=None, **kwargs) -> None:
49 properties = properties or {}
51 # Call parent class constructor
52 super().__init__(properties)
53 self.locals_var_dict = locals().copy()
55 # Input/Output files
56 self.io_dict = {"out": {"output_simulations": output_simulations}}
58 # Properties specific for BB
59 self.collection_name = properties.get("collection_name", "refs")
60 self.keyword = properties.get("keyword", None)
61 self.properties = properties
63 # Check the properties
64 self.check_properties(properties)
65 self.check_arguments()
67 def check_data_params(self, out_log, err_log):
68 """Checks all the input/output paths and parameters"""
69 self.output_simulations = check_output_path(
70 self.io_dict["out"]["output_simulations"],
71 "output_simulations",
72 False,
73 out_log,
74 self.__class__.__name__,
75 )
77 @launchlogger
78 def launch(self) -> int:
79 """Execute the :class:`MemProtMDSimSearch <api.memprotmd_sim_search.MemProtMDSimSearch>` api.memprotmd_sim_search.MemProtMDSimSearch object."""
81 # check input/output paths and parameters
82 self.check_data_params(self.out_log, self.err_log)
84 # Setup Biobb
85 if self.check_restart():
86 return 0
88 self.keyword = self.keyword.strip().lower()
90 # get JSON object
91 json_string = get_memprotmd_sim_search(
92 self.collection_name, self.keyword, self.out_log, self.global_log
93 )
95 # write JSON file
96 write_json(json_string, self.output_simulations, self.out_log, self.global_log)
98 self.check_arguments(output_files_created=True, raise_exception=False)
100 return 0
103def memprotmd_sim_search(
104 output_simulations: str, properties: Optional[dict] = None, **kwargs
105) -> int:
106 """Execute the :class:`MemProtMDSimSearch <api.memprotmd_sim_search.MemProtMDSimSearch>` class and
107 execute the :meth:`launch() <api.memprotmd_sim_search.MemProtMDSimSearch.launch>` method."""
108 return MemProtMDSimSearch(**dict(locals())).launch()
111memprotmd_sim_search.__doc__ = MemProtMDSimSearch.__doc__
112main = MemProtMDSimSearch.get_main(memprotmd_sim_search, "Wrapper for the MemProtMD DB REST API (http://memprotmd.bioch.ox.ac.uk/) to perform advanced searches.")
114if __name__ == "__main__":
115 main()