AS 4OYD

Question: Can we fold 4OYD with and without binder to be itself?

In this scenario, we use 4OYD as template, as the source of our motif and binder and as the final target conformation we are aiming to mimic.

We will run the protocol with and without a binder. Using full CA constraints from the template.

Nomenclature:

Template: Structure that will accomodate the query motif.
Design: Chain that we are designing; contains the query motif and is guided by the template info.
Binder: If any, is whatever protein(s) bind to the query motif.
Target: Protein that we expect our design to resemble to and to which we compare.



In [1]:

    
%load_ext autoreload
%autoreload 2



In [3]:

    
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import rstoolbox
import pandas as pd
import seaborn as sns
import copy

sns.set(font_scale=1.5);



In [3]:

    
experiments = [
    "nobinder/as4oyd_nobinder_1_minisilent",
    "binder/as4oyd_binder_1_minisilent"
]

definition = {
    "scores":{
        "description": "description", "design_score": "score",
        "GRMSD2Target": "GRMSD2Target",
        "LRMSD2Target": "LRMSD2Target", "LRMSDH2Target": "LRMSDH2Target", "LRMSDLH2Target": "LRMSDLH2Target"
    },
    "naming": ["", "", "experiment", "", "cluster", "decoy"]
}



In [4]:

    
dataframes = []
for experiment in experiments:
    dlist = rstoolbox.api.read_rosetta_silent(experiment)
    df    = rstoolbox.api.process_from_definitions(dlist, definition)
    dataframes.append( df )
data = pd.concat(dataframes)



In [8]:

    
g = sns.lmplot(x="GRMSD2Target", y="LRMSD2Target", col="experiment", hue="experiment", data=data,
               fit_reg=False, size=10)
plt.subplots_adjust(top=0.85)
g.axes[0,0].set_ylim(0,4)
g.axes[0,0].set_xlim(0,2)
g.fig.suptitle('Retrieveing 4OYD with its own constraints')
sns.plt.show()



In [6]:

    
ddg_experiments = [
    "nobinder/ddg_evaluation.score",
    "binder/ddg_evaluation.score"
]

ddg_definition = {
    "scores":{
        "description": "description", "ddg": "ddg", "post_ddg": "min_ddg"
    },
    "naming": ["", "", "experiment", "", "cluster", "decoy", ""]
}



In [8]:

    
dataframes = []
for experiment in ddg_experiments:
    dlist = rstoolbox.api.read_rosetta_silent(experiment)
    df    = rstoolbox.api.process_from_definitions(dlist, ddg_definition)
    dataframes.append( df )
ddgdata  = pd.concat(dataframes)
ddgdata  = ddgdata.drop("description", axis=1)
fulldata = pd.merge(data, ddgdata, how='left', on=['cluster', 'decoy', 'experiment'])



In [11]:

    
g = sns.lmplot(x="ddg", y="LRMSD2Target", col="experiment", hue="experiment", data=fulldata, fit_reg=False, size=10)
plt.subplots_adjust(top=0.85)
g.axes[0,0].set_ylim(0,4)
g.axes[0,0].set_xlim(-65,10)
g.fig.suptitle('ddG vs. LocalRMSD')
sns.plt.show()



In [12]:

    
g = sns.lmplot(x="min_ddg", y="LRMSD2Target", col="experiment", hue="experiment", data=fulldata,
               fit_reg=False, size=10)
plt.subplots_adjust(top=0.85)
g.axes[0,0].set_ylim(0,4)
g.axes[0,0].set_xlim(-65,10)
g.fig.suptitle('ddG (minimized) vs. LocalRMSD')
sns.plt.show()



In [16]:

    
g = sns.lmplot(x="min_ddg", y="score", col="experiment", hue="experiment", data=fulldata,
               fit_reg=False, size=10)
plt.subplots_adjust(top=0.85)
#g.axes[0,0].set_ylim(0,4)
g.axes[0,0].set_xlim(-65,10)
g.fig.suptitle('ddG (minimized) vs. score')
sns.plt.show()



In [ ]: