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 on the cell membrane.

Type 2: Membrane & Polymerization Certain enzymes are polymerized on the cell membrane.

Type 3: Matrix & Random The position of enzyme is distributed randomly inside the cell.

Type 4: Matrix & Polymerization The polymerization of certain enzymes is distributed randomly inside the cell.

Hypothesis of Simulation

1. Metabolism

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

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Type 1

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Type 2

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Type 3

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Type 4

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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.

[The highlighted Letters represent the mutation site.]

[The white DNA sequences on the graph is the originated position and orange one represents the possible binding 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

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.

Part Ⅲ K-clique Problem

Problem Identification

Reference

1. Pierce, Brian G., Yuichiro Hourai, and Zhiping Weng. "Accelerating protein docking in ZDOCK using an advanced 3D convolution library." PloS one 6.9 (2011): e24657.
2. Mintseris, Julian, et al. "Integrating statistical pair potentials into protein complex prediction." Proteins: Structure, Function, and Bioinformatics 69.3 (2007): 511-520.