Modelling
In our project we mainly depended on metal binding proteins, for that reason we begin at the first place to assess the function of targeted proteins, this was made using protein modeling tools, Protein modeling is theoretical process where we could predict some parameter to identify protein folding, stability, ligand binding site prediction. In the process we used three tools: the first was Expasy Protparam tool; second was I-tasser, web server to predict protein secondary and 3D structure in addition to ligand binding site prediction; last one was SCooP a tool to predict the stability of protein using the submitted PDB file of the protein and forming thermal stability curve. We focused on characterizing those parts in silico, in order to be easier to predict their function and charchatrize them in vitro.
CutA:ID of work on I-tasser "S494196"
Using Expasy ProtParam tool, theoretical protein extinction coefficient has been identified in addition to several other factors that predict the protein stability, pI and halophilicity.
● We generated model in I-tasser in order to view the folding structure and predict its affinity to bind metal ligands in addition, to confirm the molecular function.
Model one structurally generated most close to Crystal structure of possible CutA1 divalent ion tolerance protein from Cryptosporidium parvum Iowa II, with TM-Score 0.95, and sequence coverage 0.9991.(fig1)
● The biological process identified in the model in response to metal ion, through the ligand binding site prediction, we were able to predict that CutA1 could bind to sodium ion with c-score=0.1;Gene Ontology predicted in the model belonged to copper binding(fig2).
● We used model 1 to generate the thermostability curve using Scoop web server, thermostability indicate the Tm, the temperature at which half the protein is folded and the other half is unfolded and deltaG is =0, the delta free energy of transition from the unfolded to the native state at room temperature, the heat capacity of the folding transition.
The following graph predict that at 72.9 half of the protein will become unfolded.
CSP1: ID of work on I-tasser S499508
Using Expasy ProtParam tool*, theoretical protein extinction coefficient has been identified in addition to several other factors that predict the protein stability, pI and halophilicity.
● We modeled copper storage protein to define its ability to bind certain metal, view the b-factor of which and to identify the most similar protein to it, model predicted it as most similar to APO-CSP3 (COPPER STORAGE PROTEIN 3) from Methylosius with Tm-Score of 0.704,and sequence coverage 0.782.
● Both predicted ligand binding site and Gene Ontology acknowledged copper as ligand that protein binds and with characteristic of metal binding, respectively.
● Then thermal stability curve was created using SCoop, to identify the thermal resistance of the protein
Bacterioferritin: ID of work on I-tasser
Using Expasy ProtParam tool*, theoretical protein extinction coefficient has been identified in addition to several other factors that predict the protein stability, pI and halophilicity.
● The same process was applied to the bacterioferritin protein, identifying the folding of the protein, and predicting the Gene Ontology, the protein predicted to have multiple metal binding protein capacity for Zinc,TM-score 0.6052, and Nickel, with TM-score0.6078; in the ligand binding site prediction,protein predicted to bind Copper mainly.
Then thermal analysis was made identifying the melting temperature and deltaG.
Two parts improvement:CSP1,Bacterioferritin
● We thought of improving CSP1 and Bacterioferritin to be more stable as its predicted stability index is more than 40,and increase its solubility to make the GRAVY more negative value, we added GST to the protein sequence and re-modeled the protein and identified its stability index, GRAVY, and deltaG of transition at room temperature.
● Using primary structure , we tested the solubility indicated by GRAVY and stability indicated by Instability Index to find that the addition of Gst reduced both of them as primary indicator
● A second indicator of the stability improvement upon GST addition was the thermostability curve, detlaG of transition at room temperature is an indicator to the stability ,while thermal resistance sometimes indicate the general stability. Often increasing thermal stability could be met by three strategies one of which is heat capacity of transition becomes less negative, which leads to an increase of Tm through a modification of the shape of the curve , upon producing the thermostability curve we found that the heat capacity became less negative and deltaG of transition increase in the negativity, curve indicated in the following two figures
The first curve shows thermal stability upon adding GST to CSP1 , whilw the right curve shows the thermal stability upon adding GST to Bacterioferritin
From in silico protein modeling results,we have chosen to tag CSP1, bacterioferritin with GST tag and test the results in vitro, specially the stability of both before and after adding GST tag at room temperature as deltaG became more negative upon fusing proteins.
