Bobak Seddighzadeh Week 9: Difference between revisions

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(New page: Procedure and Results ===Question=== The question that my partner and I decided to tackle is: '''Within the variable regions of the V3 domain of HIV-1, what are the amino acid differences...)
 
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Procedure and Results
Procedure and Results
===Powerpoint===
Final Presentation:
[[Image:Protein Structure Final Powerpoint-Alex and Bobby.ppt| Final Structure Presentation]]


===Question===
===Question===
The question that my partner and I decided to tackle is: '''Within the variable regions of the V3 domain of HIV-1, what are the amino acid differences for progressors and nonprogressors alter its structure significantly enough to affect its function?
The question that my partner and I decided to tackle is: '''Within the variable regions of the V3 domain of HIV-1, what are the amino acid differences for progressors and nonprogressors alter its structure significantly enough to affect its function?


#Convert your DNA sequences into protein sequences. Firstly, we had to choose what DNA sequences we would use. To approach this question properly, we need a set of data from subjects, visits, and clones in the env region of the V3 domain that possesses variable rates of divergence and diversity that can be classified as either a rapid or non progrogressor. For the rapid progressors, we have chosen 10-V4, 10-V5, 10-V6, 4-V5, 4-V6, 4-V7, 11-V7, 11-V8, 11-V9. Subjects 4, 10, and 11 were chosen because they had the highest rates of annual CD4 T cell decline. Having a high annual rate of CD4 T cell decline which is a potential good lead into specific nucleotide sequences that cause increased rates of diversity and divergence. We chose the visit based upon the biggest drop in T cell counts. For the group of non-progressors, we have chosen 2-V3, 2-V4, 2-V5, 12-V3, 12-V5, 12-V6, 13-V5, 13-V6, 13-V10. These subjects were chosen because they were the only recorded nonprogressors in the experiment. These visits were chosen because they had the highest CD4 T cell count among all visits. These clones will be analyzed for each visit to find a difference in the nucleotide sequence. To convert our DNA sequences into protein sequences, we first had to obtain our amino acid sequences from www.bioquest.org/bedrock. My partner and I decided to use the same subjects, visits, and clones from our previous two weeks ago because this data best fits our question for this week. you can run on a multiple sequence alignment using the protein tools for the Biology Workbench 3.2.
#Convert your DNA sequences into protein sequences. Firstly, we had to choose DNA sequences that would best fit our data. We need a set of data from subjects, visits, and clones in the env region of the V3 domain that possesses variable rates of divergence and diversity so that the possibility of significant sequences changes amongst the clones exists that can correlate into changes in function. We decided to use the same set of rapid and non progressors from the project that Chris Chin and I performed two weeks ago. For the rapid progressors, we have chosen subject 11, 10, and 4 because they had the highest rates of annual CD4 cell decline. For each subject we chose visits that had the biggest drop in CD4 T cell count. For subject 11 we chose visits 4,3, and 2. For subject 10 we chose visits 6,5,4  and for subject 4 we chose visits 4,3,2. We screened for the clones that had the greatest divergence amongst each other. For the group of non-progressors, we have chosen 2-V3, 2-V4, 2-V5, 12-V3, 12-V5, 12-V6, 13-V5, 13-V6, 13-V10. These subjects were chosen because they were the only recorded nonprogressors in the experiment. These visits were chosen because they had the highest CD4 T cell count among all visits. These clones will be analyzed for each visit to find a difference in the nucleotide sequence. To convert our DNA sequences into protein sequences, we first had to obtain our amino acid sequences from each subject at www.bioquest.org/bedrock. We know that the amino acid sequences obtained are correct because it came from the data generated during the study of these subjects by scientists.  
How will you do this?
#Perform a multiple sequence alignment on the protein sequences. There are fewer differences in the protein sequences than the DNA sequences because the amino acid code is degenerate which means that each amino acid has more than one codon that correlates to it. Are there more or fewer differences between the sequences when you look at the DNA sequences versus the protein sequences?
How will you know that it was done correctly?
 
Results of Clustal W:
Results of Clustal W:
#Amino acid change S to F at position four Subject 13 V5 clone 1. Serine --> to phenylalanine goes from polar uncharged to hydrophobic side chain
#Amino acid change S to F at position four Subject 13 V5 clone 1. Serine --> to phenylalanine goes from polar uncharged to hydrophobic side chain
Line 20: Line 25:
#E to G: negative to glycine * look at this change also
#E to G: negative to glycine * look at this change also
#E to V: negative to phobic
#E to V: negative to phobic
{{BobakS}}

Latest revision as of 21:24, 22 March 2010

Procedure and Results

Powerpoint

Final Presentation: File:Protein Structure Final Powerpoint-Alex and Bobby.ppt


Question

The question that my partner and I decided to tackle is: Within the variable regions of the V3 domain of HIV-1, what are the amino acid differences for progressors and nonprogressors alter its structure significantly enough to affect its function?

  1. Convert your DNA sequences into protein sequences. Firstly, we had to choose DNA sequences that would best fit our data. We need a set of data from subjects, visits, and clones in the env region of the V3 domain that possesses variable rates of divergence and diversity so that the possibility of significant sequences changes amongst the clones exists that can correlate into changes in function. We decided to use the same set of rapid and non progressors from the project that Chris Chin and I performed two weeks ago. For the rapid progressors, we have chosen subject 11, 10, and 4 because they had the highest rates of annual CD4 cell decline. For each subject we chose visits that had the biggest drop in CD4 T cell count. For subject 11 we chose visits 4,3, and 2. For subject 10 we chose visits 6,5,4 and for subject 4 we chose visits 4,3,2. We screened for the clones that had the greatest divergence amongst each other. For the group of non-progressors, we have chosen 2-V3, 2-V4, 2-V5, 12-V3, 12-V5, 12-V6, 13-V5, 13-V6, 13-V10. These subjects were chosen because they were the only recorded nonprogressors in the experiment. These visits were chosen because they had the highest CD4 T cell count among all visits. These clones will be analyzed for each visit to find a difference in the nucleotide sequence. To convert our DNA sequences into protein sequences, we first had to obtain our amino acid sequences from each subject at www.bioquest.org/bedrock. We know that the amino acid sequences obtained are correct because it came from the data generated during the study of these subjects by scientists.
  2. Perform a multiple sequence alignment on the protein sequences. There are fewer differences in the protein sequences than the DNA sequences because the amino acid code is degenerate which means that each amino acid has more than one codon that correlates to it. Are there more or fewer differences between the sequences when you look at the DNA sequences versus the protein sequences?

Results of Clustal W:

  1. Amino acid change S to F at position four Subject 13 V5 clone 1. Serine --> to phenylalanine goes from polar uncharged to hydrophobic side chain
  2. T -> M polar to phobic
  3. T to S: polar polar
  4. I to T: phobic to polar
  5. I to N: phobic to polar
  6. L to P: phobic to proline which is a ring strucurre can alter bonding
  7. S to A: polar to phobic
  8. S to T: polar to polar look at this one S to T and T to S
  9. S to F: polar phobic
  10. E to G: negative to glycine * look at this change also
  11. E to V: negative to phobic
  • Electronic Journal
  1. Bobak Seddighzadeh Week 2
  2. Bobak Seddighzadeh Week 3
  3. Bobak Seddighzadeh Week 4
  4. Bobak Seddighzadeh Week 5
  5. Bobak Seddighzadeh Week 6
  6. Bobak Seddighzadeh Week 7
  7. Bobak Seddighzadeh Week 8
  8. Bobak Seddighzadeh Week 9
  9. Bobak Seddighzadeh Week 10
  10. Bobak Seddighzadeh Week 11
  11. Bobak Seddighzadeh Week 12
  12. Bobak Seddighzadeh Week 13
  • Shared Journal
  1. BIOL398-01/S10:Class Journal Week 2
  2. BIOL398-01/S10:Class Journal Week 3
  3. BIOL398-01/S10:Class Journal Week 4
  4. BIOL398-01/S10:Class Journal Week 5
  5. BIOL398-01/S10:Class Journal Week 6
  6. BIOL398-01/S10:Class Journal Week 7
  7. BIOL398-01/S10:Class Journal Week 8
  8. BIOL398-01/S10:Class Journal Week 9
  9. BIOL398-01/S10:Class Journal Week 10
  10. BIOL398-01/S10:Class Journal Week 11
  11. BIOL398-01/S10:Class Journal Week 12
  12. BIOL398-01/S10:Class Journal Week 13
  • Assignments
  1. BIOL398-01/S10:Week 2
  2. BIOL398-01/S10:Week 3
  3. BIOL398-01/S10:Week 4
  4. BIOL398-01/S10:Week 5
  5. BIOL398-01/S10:Week 6
  6. BIOL398-01/S10:Week 7
  7. BIOL398-01/S10:Week 8
  8. BIOL398-01/S10:Week 9
  9. BIOL398-01/S10:Week 10
  10. BIOL398-01/S10:Week 11
  11. BIOL398-01/S10:Week 12
  12. BIOL398-01/S10:Week 13

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