A package for biomolecular analysis, modelling and design
Intelligent System for Analysis, Model Building And Rational Design.
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.
Any publication arising from use of the ISAMBARD software package should cite the following reference:
ISAMBARD can be installed straight from PyPI using
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 .
If you want to add side chains to your designs, you need to have 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 on the differences between version 1 and 2.
If you're not sure what parametric modelling of proteins is, have a play with CCBuilder 2.0.
Let's build a coiled-coil dimer with typical parameters:
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!
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 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.
- Adds pacc module for parametric analysis of coiled coils.
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