Metadata-Version: 2.1
Name: ISAMBARD
Version: 2.2.0
Summary: A package for biomolecular analysis, modelling and design
Home-page: https://github.com/isambard-uob/isambard
Author: Woolfson Group, University of Bristol
Author-email: chris.wood@bristol.ac.uk
License: UNKNOWN
Description: # ISAMBARD
        
        Intelligent System for Analysis, Model Building And Rational Design.
        
        [![CircleCI](https://circleci.com/gh/isambard-uob/isambard.svg?style=shield)](https://circleci.com/gh/isambard-uob/isambard)
        [![Python Version](https://img.shields.io/badge/python-3.5%2C%203.6-lightgrey.svg)](https://woolfson-group.github.io/isambard/)
        [![MIT licensed](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/woolfson-group/isambard/blob/master/LICENSE.md)
        
        ISAMBARD is a Python-based framework for structural analysis and rational
        design of biomolecules, with a particular focus on parametric modelling of
        proteins. It is developed and maintained by members of the [Woolfson group, University of Bristol](http://www.chm.bris.ac.uk/org/woolfson/index.html).
        
        ### Citing ISAMBARD
        Any publication arising from use of the ISAMBARD software package should cite the following reference:
        
        [Wood CW *et al* (2017) ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design. *Bioinformatics*, **33**, 3043-50](https://doi.org/10.1093/bioinformatics/btx352)
        
        ## Installation
        
        ISAMBARD can be installed straight from PyPI using `pip`:
        
        ```
        pip install isambard
        ```
        Or if you want to try an experimental build (you'll need a C compiler), download
        from GitHub either by downloading the zipped file or cloning, then navigate to
        the ISAMBARD folder and type:
        
        ```
        pip install .
        ```
        
        ## External Programs
        
        If you want to add side chains to your designs, you need to have [Scwrl4](
        http://dunbrack.fccc.edu/scwrl4/) installed and available on your system path. 
        
        ## Upgrading to ISAMBARD 2
        
        If you were already using ISAMBARD prior to the 2.0.0 release, [here's a handy
        guide](https://gist.github.com/ChrisWellsWood/578fcea671acbb68d4a130315874027b)
        on the differences between version 1 and 2.
        
        ## Quick Start
        
        > Note<br />
        > If you're not sure what parametric modelling of proteins is, have a
        > play with [CCBuilder 2.0](http://coiledcoils.chm.bris.ac.uk/ccbuilder2/builder).
        
        Let's build a coiled-coil dimer with typical parameters:
        
        ```Python
        import isambard.specifications as specifications
        import isambard.modelling as modelling
        import isambard.optimisation
        
        my_dimer = specifications.CoiledCoil.from_parameters(2, 28, 5, 225, 283)
        dimer_sequences = [
            'EIAALKQEIAALKKENAALKWEIAALKQ',
            'EIAALKQEIAALKKENAALKWEIAALKQ'
        ]
        my_dimer = modelling.pack_side_chains_scwrl(my_dimer, dimer_sequences)
        print(my_dimer.pdb)
        # OUT: 
        # HEADER ISAMBARD Model                                                                  
        # ATOM      1  N   GLU A   1      -5.364  -1.566  -0.689  1.00  0.00           N  
        # ATOM      2  CA  GLU A   1      -4.483  -2.220   0.308  1.00  0.00           C  
        # ATOM      3  C   GLU A   1      -3.886  -1.143   1.216  1.00  0.00           C  
        # ATOM      4  O   GLU A   1      -3.740  -1.337   2.425  1.00  0.00           O  
        # ATOM      5  CB  GLU A   1      -3.389  -3.028  -0.392  1.00  0.00           C  
        # ...
        ```
        
        Don't know what your parameters might be? Let's optimise them then!
        
        ```Python
        import budeff
        import isambard.optimisation.evo_optimizers as ev_opts
        from isambard.optimisation.evo_optimizers import Parameter
        
        specification = specifications.CoiledCoil.from_parameters
        sequences = [
            'EIAALKQEIAALKKENAALKWEIAALKQ',
            'EIAALKQEIAALKKENAALKWEIAALKQ'
        ]
        parameters = [
            Parameter.static('Oligomeric State', 2),
            Parameter.static('Helix Length', 28),
            Parameter.dynamic('Radius', 5.0, 1.0),
            Parameter.dynamic('Pitch', 200, 60),
            Parameter.dynamic('PhiCA', 283, 27),  # 283 is equivalent a g position
        ]
        
        def get_buff_total_energy(ampal_object):
            return budeff.get_internal_energy(ampal_object).total_energy
        
        opt_ga = ev_opts.GA(specification, sequences, parameters, get_buff_total_energy)
        opt_ga.run_opt(100, 5, cores=8)
        # OUT:
        # gen	evals	avg     	std    	min     	max     
        # 0  	61   	-820.401	42.0119	-908.875	-750.001
        # 1  	59   	-859.86 	31.4194	-950.15 	-807.265
        # 2  	60   	-887.028	23.8683	-951.153	-847.346
        # 3  	70   	-907.257	15.9615	-952.863	-882.028
        # 4  	81   	-922.522	14.6206	-972.335	-903.444
        # Evaluated 431 models in total in 0:00:29.523487
        # Best fitness is (-972.3348571854714,)
        # Best parameters are [2, 28, 4.678360526981807, 151.35365923229745, 277.2061538048508]
        optimized_model = opt_ga.best_model
        ```
        
        This quick example of parametric modelling with ISAMBARD, the next thing to do
        is take a look at the [docs](https://isambard-uob.github.io/isambard/) from
        tutorials on the tools available, or just take a look through the code base and
        hack around. Feel free to contact us through email or the issues if you get
        stuck.
        
        # Release Notes
        
        ### v2.2.0
        
        * **Adds pacc module for parametric analysis of coiled coils.**
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
