#!/usr/bin/env python
# coding: utf-8

# # Complex design example
# 
# Design a complex ensemble intend to predominantly adopt a 4-stranded stick figure target structure. 
# 
# Material: RNA    
# Temperature: 23 C
# 
# While we illustrate complex design, we recommend performing test tube design so that it is possible to actively design against formation of off-target complexes that can compete with the desired on-target complex in solution (see tube design example notebook). Indeed, we show at the bottom of this example that a test tube analysis reveals that while the designed sequences perform well within the context of the complex ensemble (as intended by the complex design algorithm), the on-target complex is out-competed by off-target complexes in the context of a test tube ensemble. 

# In[1]:


# Import Python NUPACK module
from nupack import *


# In[2]:


# Define physical model
my_model = Model(material='rna', celsius=23)

# Define sequence domains
da = Domain('N27', name='da')
db = Domain('N29', name='db')
dc = Domain('N25', name='dc')
dd = Domain('N18', name='dd')

# Define strands containing these domains
sa = TargetStrand([da], name='sa')
sb = TargetStrand([db], name='sb')
sc = TargetStrand([dc], name='sc')
sd = TargetStrand([dd], name='sd')

# Define a target complex
cstickfigure = TargetComplex([sa, sb, sc, sd], '..((((((((..((((((((((((...+))))))(((.........)))((((((..+.))))))))))))..((((((((..+.)))))))))))))))).', name='cstickfigure')

# Set a stop condition of 1% and a seed for random number generation to get a reproducible result for this demo
my_options = DesignOptions(f_stop=0.01, seed=93)

# Define and run the complex design job
my_design = complex_design(complexes=[cstickfigure], model=my_model, options=my_options)
my_results = my_design.run(trials=1)[0]

# Display the design results
my_results


# In[3]:


# Analyze a test tube ensemble containing the designed sequences formaing all complexes of up to 4 strands
# Fromn the list of concentrations below, we see that the on-target complex "cstickfigure" is dominated by 
# many off-target complexes. This can be avoided by activately designing against formation of the off-target 
# complexes using test tube design. 


on_target = my_results.to_analysis(cstickfigure)
my_tube = Tube(strands={s: 1e-8 for s in on_target.strands}, 
               complexes=SetSpec(max_size=4, include=[on_target]), name='Tube')
tube_analysis(tubes=[my_tube], model=my_model)