Part I Single Cell

Our project is about the system involving various enzymes, mostly the series enzymes, combining into certain area. This area can be more efficient when it comes to synthesizing or degrading chemicals. So the first question is, whether this system can be so useful when distributing multiple similar areas in a single cell.

   Four Types of Distribution

• Type 1: Membrane & Random
  The position of enzyme is distributed randomly in the cell membrane.
  
• Type 2: Membrane & Polymerization
  The polymerization of certain enzymes, based on MembRing, is distributed randomly inside the cell.
• Type 3: Matrix & Random
  The position of enzyme is distributed randomly inside the cell.
• Type 4: Matrix & Random
  The polymerization of certain enzymes, based on MembRing, is distributed randomly inside the cell.

   Hypothesis of Simulation

1. Metabolism
   [图片一]
   Enzymes: E0, E1,E2
   Substrates:S0,S1,S2,S3
2. Initial Distribution of Substrates
   All substrates are randomly distributed OUTSIDE the cell in all four simulations.
3. Movement of Substrates
   The motion of molecules is random, including the rate and orientation.
4. Catalytic reaction
   The time period of reaction is neglected. When the type of chemical match the type of enzyme, distance is less than threshold, then the enzyme reaction is recognized and recorded.
5. Other Hypothesis
   Other physical and chemical parameters are under the scaling rule. The whole modeling combined with periodic boundary condition(PBC) to show the real performance of substrates and enzyme system.

   Results:

• All Results
• Type 1
• Type 2
• Type 3
• Type 4

Part II Docking

   Why do we need Docking?

Biobrick designers and users want to understand the characteristics of a particular biobrick, especially the performance and accuracy. Because they need to answer a question, that is, were there to be a certain mutation, whether a huge change would happen to the protein function. We hope to introduce evaluation methods of bioinformatics, to evaluate binding of protein and DNA.

   Materials




TAL (transcription activator-like) effectors, secreted by phytopathogenic bacteria, recognize host DNA sequences through a central domain of tandem repeats. Each repeat consists of 33 to 35 conserved amino acids and targets a specific base pair by using two hypervariable residues [known as repeat variable diresidues (RVDs)] at positions 12 and 13.

• PDB:3V6T


   Mutations




We designed fifteen sequences derived from raw sequence. These mutated sequences contain different mutations, ranging from one to five. Through a series of calculations, we obtained scores to represent the binding of TAL effectors and DNA.




• mutation-1
• Score：1164.128



• mutation-2
• Score:1170.910



• mutation-3
• Score:1153.537



• mutation-4
• Score:1377.231



• mutation-5
• Score:1169.283



• mutation-6
• Score:1179.122



• mutation-7
• Score:1482.902



• mutation-8
• Score:1161.824



• mutation-9
• Score:1482.897



• mutation-10
• Score:1174.229



• mutation-11
• Score:1237.449



• mutation-12
• Score:1482.896



• mutation-13
• Score:1483.352



• mutation-14
• Score:1482.048



• mutation-15
• Score:1164.128




   Analysis



• Table

• Table


From the docking scores, we can see that in the event of single nucleotide mutation, binding of TAL effectors and DNA differs greatly from normal. However, when there are more than two mutation sites, the difference becomes less drastic.

From the PDB document, we can find that mutation at certain sites may lead to huge conformational distortions of TAL-DNA complex. With as many as five mutations, the binding site changes greatly.

In conclusion, we strongly recommend that TAL designers and users ensure the accuracy of DNA binding sequence. If not, the specificity of binding site will not be guaranteed.