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− | Combining the bioinformatics and machine learning, we | + | Combining the bioinformatics and machine learning, we presented an algorithm to pre-processing our big mutation library. The flow chart of the pre-processing algorithm is shown below. |
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− | <div align="center"><p >Fig.14 | + | <div align="center"><p >Fig.14 The flow chart of the pre-processing algorithm </p></div> |
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− | The SVM model | + | The SVM model was training well and the results were shown below. |
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− | After training the SVM model, we | + | After training the SVM model, we used it to evaluate the hairpin mutants. We selected the hairpin mutants with high ranks to check the four key points. Finally, we determined the five hairpin mutants below. The following chart shows the DR-Score which is the evaluated results from the SVM model. |
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Revision as of 15:57, 17 October 2018
Overview
The aim of our project is to develop a better post-transcriptional regulation strategy and use it in monocistron and polycistron. Here we built models to design and predict our work.
miniToe —— a better transcriptional regulate strategy
To achieve a better post-transcriptional regulation strategy, we designed a system which is composed of an RNA endoribonuclease (Csy4) and an RNA module named miniToe. We modeled to describe the dynamics of the miniToe system and found a way to achieve different regulation level. The ODEs and molecular dynamics were two main tools to explore it. We used the ODEs to describe the reaction curve and the molecular dynamics in order to give some explanations to experimental data.
Below you can follow the several questions we point out to have a better understanding of model work and the miniToe system. We will discuss some structures of Csy4 in different stages (Q1), some structures of miniToe system in different stages (Q2), the reaction order and some key points of miniToe system (Q3), the simulation of ODEs model (Q4), some significant symbols in molecular dynamics (Q5) and the ways to different regulation levels (Q6).
Q1: What is the structure of Csy4?
Fig.1 The structure of Csy4 without the hairpin bound. (PDB ID: 4AL5, resolution 2.0 A)
Fig.2 The structure of Csy4 with the hairpin bound. (PDB ID: 4AL5, resolution 2.0 A)
Q2: What is the structure of miniToe?
1. A cis-repressive RNA (crRNA) serves as a translation suppressor by pairing with RBS, and therefore constitutes the critical part of the miniToe structure.
2. A Csy4 site serves as a linker between cis-repressive RNA and RBS, which can be specifically cleaved by Csy4 enzyme.
3. Csy4 enzyme --- A CRISPR endoribonuclease.
Fig.3 The structure of miniToe.
Fig.4 The precursor complex of wild-type Csy4.
Fig.5 The product complex of wild-type Csy4.
Q3: What is the reaction process and key points of miniToe system?
Fig.6 The working process of miniToe system.
(1) The miniToe is produced and accumulated.
(2) Csy4 is produced after induced by IPTG.
(3) Csy4 binds to the miniToe structure and forms the Csy4-miniToe complex.
(4) Csy4 cleaves the special site and divides the miniToe structure into two parts: the Csy4-crRNA complex and the mRNA of sfGFP.
(5) sfGFP is produced.
From the description above, we can get four key problems in our system to make sure whether our system can work successfully:
(1) Can Csy4 dock correctly with the miniToe structure (hairpin)?
(2) How about the ability of binding between the Csy4 and miniToe structure (hairpin)?
(3) How about the ability of cleavage between the Csy4 and miniToe structure (hairpin)?
(4) Can cis-repressive RNA be released from the RBS successfully?
Q4: How about the simulation results of the ODEs model?
According to the work process, we built an ODEs model and simulated our miniToe system for 30h, the result can be seen in the Fig.7.
Fig.7 The dynamics of sfGFP by model prediction
Fig.8 The comparison between experimental data and simulation data.
Q5: How about simulation result of the molecular dynamics?
For the first key point, we have the interaction matrix to describe the molecular docking, and the heatmap of the matrix can be seen in Fig.9.
Fig.9 The heatmap of interaction matrix for wild-type Csy4.
For the second key point, we calculated the binding free Energy of Csy4/RNA complex. The result of binding free energy for wild-type Csy4 is .
For the third key point, we checked the distance of Ser151(OG)-G20(N2’), which is a key interaction in the active site of Csy4 to describe the ability of cleavage. The distance curve of Ser151(OG)-G20(N2’) for wild-type Csy4 can be seem in Fig.10.
Fig.10. The distance of Ser151(OG)-G20(N2’) in wild-type Csy4.
For the last key point, we used the RMSD of product to describe the release of crRNA. The result can be seen in the Fig.11.
Fig.11. The distance of Ser151(OG)-G20(N2') in wild-type Csy4
To see more details
Q6: How to achieve the goal of different regulate level?
Fig.12 The curve of sfGFP with the changing cleavage rate.
miniToe Family —— The way to fifferent regulate level
In the miniToe family, the protein and hairpin were mutated to meet the goal of the different regulation level. In this part, the model can help us design mutants. Importantly, we used different strategies to design the feature of Cys4 and the hairpin. For example, molecular dynamics played an important role in designing protein mutants, and the bioinformatics and machine learning supported us to find the hairpin mutants of our interest.
So here comes the another four key questions: how to design our Csy4 mutants (Q7), how does the method work in design and the result (Q8), the problem different from Csy4 designing when design the hairpin mutants and how to solve it (Q9) and the results of the mutants designing (10).
Q7: How to design the Csy4 mutants?
click to see more
click to see less
We found four important sites in wet lab, Gln104, Tyr176, Phe155 and His29, which play important roles in binding and cleavage in Csy4 structure.(Fig.13) Considering the existing 20 amino acids in nature, there were 80 mutants to be explored if we had only one site to be mutated.
Fig.13 The four important sites in Csy4.
In Q3, we have discussed four key points which can directly influence our miniToe system. In addition, according to the molecular dynamics results in Q5, we can describe the four key points through four significant symbols.
Now we are going to construct a logic line to show how to use the three main information above to design Csy4 mutants:
What we have proved through the experiment is that the wild-type Csy4 can work well with the miniToe system, which means that all the key points we have discussed before didn't affect the wild-type Csy4. The wild-type Csy4 can dock correctly with the miniToe structure and had a good ability to bind and cleave the miniToe structure. Finally, the crRNA can be released from the RBS. So we choose the wild-type Csy4 as a standard, and all Csy4 mutants can check the four points by comparing with wild-type Csy4.
Now for the four points in Q3 we have discussed the mathematical forms in Q5. And the most important thing is how to make a comparison between the mutant and wild-type Csy4 enzymes, which will be discussed in Q8.
B
Q8: How does the design methods work?
click to see more
click to see less
In Q7, we have discussed the full logic lines about how to design the Csy4 mutants. Here we will give the comparison method for the four key points in miniToe system. And we did this comparison between the mutant and wild-type Csy4 enzymes.
Now we have four mathematical forms including two curves, a numerical value and a matrix. Four things can be divided into two kinds of data: the matrix and the numerical value. The interaction matrix and the curve can be regarded as a matrix because the curve is discrete, and the binding free energy is a numerical value.
We used Euclidean distance to describe the difference between the two matric:
We used the formula below to calculate the difference of binding free energy between the wild type and mutants:
According to description above, we defined four value to compare the four key points between the mutant and wild-type Cys4 enzymes:
,
,
,
.
By using the four values, five Csy4 mutants were designed and shown in table below.
Csy4
WT
0
0
0
0
Q104A
0.483
2483
9.48
30.82
Y176F
0.592
-382
11.61
40.62
F155A
0.233
-1627
13.41
35.71
H29A
0.173
833
15.29
316.22
Q9: How to design the hairpin mutant?
click to see more
click to see less
The design of hairpin mutants is quite different from the Csy4 mutants due to the large library. In theory, except for the two cleaved sites, G20 and C21, we can generate 420 mutants totally.
Combining the bioinformatics and machine learning, we presented an algorithm to pre-processing our big mutation library. The flow chart of the pre-processing algorithm is shown below.
Fig.14 The flow chart of the pre-processing algorithm
The SVM model was training well and the results were shown below.
Fig.15 The training result
After training the SVM model, we used it to evaluate the hairpin mutants. We selected the hairpin mutants with high ranks to check the four key points. Finally, we determined the five hairpin mutants below. The following chart shows the DR-Score which is the evaluated results from the SVM model.
Hairpin-Mutant
miniToe1
76.6306
miniToe2
65.6278
miniToe3
66.7160
miniToe4
62.5537
miniToe5
52.9794
Q10: How about the result of mutant designinig result?
click to see more
click to see less
After designing the protein mutant and hairpin mutants, the wet lab members test the all the Csy4 mutants and hairpin mutants. The result can see in the Fig.16.
Fig.16 The experimental result of mutants
And we try to give a comparison between the special value we used before for evaluating the mutant and experimental result to check our model.
For the protein mutants, we give a comparison between D3 and experimental result. Fig.17 is the result.
Fig.17 The comparison between model and experiment for protein mutant
As we can see in the Fig.3-2, we can find the inner relationship between D3 and experiment result: the D3 value describe the difference in the ability of cleavage between the wild-type and mutant. The higher D3 value means that it will have an big weaker than the wild-type Csy4 in it.
For the hairpin mutants, we give a comparison between DR-Score and experimental result. Fig.18 is the result.
Fig.18 The comparison between model and experiment for hairpin mutant
As we can see in the Fig.18 we can also can find the inner relationship between DR-Score and experiment result except for the miniToe 1. It is reasonable because the machine learning is quite sensitive to the data amounts and the R2 is not 1 in our training result of SVM model.
After all, our wet lab member test 30 combinations of our Csy4 and hairpin. Fig.19 is the heatmap result of it.
Fig.19 The heatmap result of 30 combination
We found four important sites in wet lab, Gln104, Tyr176, Phe155 and His29, which play important roles in binding and cleavage in Csy4 structure.(Fig.13) Considering the existing 20 amino acids in nature, there were 80 mutants to be explored if we had only one site to be mutated.
Fig.13 The four important sites in Csy4.
In Q3, we have discussed four key points which can directly influence our miniToe system. In addition, according to the molecular dynamics results in Q5, we can describe the four key points through four significant symbols.
Now we are going to construct a logic line to show how to use the three main information above to design Csy4 mutants:
What we have proved through the experiment is that the wild-type Csy4 can work well with the miniToe system, which means that all the key points we have discussed before didn't affect the wild-type Csy4. The wild-type Csy4 can dock correctly with the miniToe structure and had a good ability to bind and cleave the miniToe structure. Finally, the crRNA can be released from the RBS. So we choose the wild-type Csy4 as a standard, and all Csy4 mutants can check the four points by comparing with wild-type Csy4.
Now for the four points in Q3 we have discussed the mathematical forms in Q5. And the most important thing is how to make a comparison between the mutant and wild-type Csy4 enzymes, which will be discussed in Q8. B
Now we have four mathematical forms including two curves, a numerical value and a matrix. Four things can be divided into two kinds of data: the matrix and the numerical value. The interaction matrix and the curve can be regarded as a matrix because the curve is discrete, and the binding free energy is a numerical value.
We used Euclidean distance to describe the difference between the two matric:
We used the formula below to calculate the difference of binding free energy between the wild type and mutants:
According to description above, we defined four value to compare the four key points between the mutant and wild-type Cys4 enzymes: , , , .
By using the four values, five Csy4 mutants were designed and shown in table below.
Csy4 | ||||
---|---|---|---|---|
WT | 0 | 0 | 0 | 0 |
Q104A | 0.483 | 2483 | 9.48 | 30.82 |
Y176F | 0.592 | -382 | 11.61 | 40.62 |
F155A | 0.233 | -1627 | 13.41 | 35.71 |
H29A | 0.173 | 833 | 15.29 | 316.22 |
Q9: How to design the hairpin mutant?
Combining the bioinformatics and machine learning, we presented an algorithm to pre-processing our big mutation library. The flow chart of the pre-processing algorithm is shown below.
Fig.14 The flow chart of the pre-processing algorithm
The SVM model was training well and the results were shown below.
Fig.15 The training result
After training the SVM model, we used it to evaluate the hairpin mutants. We selected the hairpin mutants with high ranks to check the four key points. Finally, we determined the five hairpin mutants below. The following chart shows the DR-Score which is the evaluated results from the SVM model.
Hairpin-Mutant | |
---|---|
miniToe1 | 76.6306 |
miniToe2 | 65.6278 |
miniToe3 | 66.7160 |
miniToe4 | 62.5537 |
miniToe5 | 52.9794 |
Q10: How about the result of mutant designinig result?
After designing the protein mutant and hairpin mutants, the wet lab members test the all the Csy4 mutants and hairpin mutants. The result can see in the Fig.16.
Fig.16 The experimental result of mutants
And we try to give a comparison between the special value we used before for evaluating the mutant and experimental result to check our model.
For the protein mutants, we give a comparison between D3 and experimental result. Fig.17 is the result.
Fig.17 The comparison between model and experiment for protein mutant
As we can see in the Fig.3-2, we can find the inner relationship between D3 and experiment result: the D3 value describe the difference in the ability of cleavage between the wild-type and mutant. The higher D3 value means that it will have an big weaker than the wild-type Csy4 in it.
For the hairpin mutants, we give a comparison between DR-Score and experimental result. Fig.18 is the result.
Fig.18 The comparison between model and experiment for hairpin mutant
As we can see in the Fig.18 we can also can find the inner relationship between DR-Score and experiment result except for the miniToe 1. It is reasonable because the machine learning is quite sensitive to the data amounts and the R2 is not 1 in our training result of SVM model.
After all, our wet lab member test 30 combinations of our Csy4 and hairpin. Fig.19 is the heatmap result of it.
Fig.19 The heatmap result of 30 combination