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Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March 22, 2010

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Page 1: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Analyzing Differences in Protein Sequences Between Subjects with

Varying T Cell Counts

J’aime MoehlmanAmanda Wavrin

Loyola Marymount UniversityMarch 22, 2010

Page 2: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Outline

• Introduction• Results• Discussion• Further Research• References

Page 3: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Introduction to the HIV-1 Structure

• The site on gp120 that binds to the CD4 receptor is vulnerable to neutralizing antibodies.

• However, most antibodies that interact with the site cannot neutralize HIV-1.

• There are many features that help the gp120 protein escape the immune system of the host such as:– Variable Loops– N- Linked Glycosylation– Confomational Flexibility

• The functions of the gp120 protein are influenced by the structure within the V3 region.

Page 4: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

•Our Proposed Question: Will there be specific differences between the protein sequences in subject 10 and 12 that results in different protein structures, which changes the function of the virus?

•Hypothesis: There will be specific amino acids that cause differences in the protein structures between subjects 10 and 12.

Page 5: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Subject’s 10 and 12 were selected based on their Annual Rate of CD4 T cell decline

Page 6: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

The Phylip’s Drawtree shows that Subject 10 and 12 are not closely related.

Page 7: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

BOXSHADE Sequences for our Representative Visits

Page 8: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Predicting the Secondary Structure of Subject 10’s Protein Sequence Using PSIPRED

H: HelicalE: ExtendedC: Random Coil

Conf:947814567777306999858867895368998735651458871002107441055763 Pred:CEEEECCCCCCCCEEEEEECCCCEEEECCCCCCCCCCCCCCCCCCEEEECCCEECHHHHH AA: EVVIRSENFTDNAKTIIVQLNKAVEINCTRPNNNTRRRISMGPGRVLYTTGEIIGDIRQA

10 20 30 40 50 60

Conf: 20547877765799999999976189447760589 Pred: HHCCCHHHHHHHHHHHHHHHHHHHCCCEEEEECCC AA: HCNLSRTKWNDTLKQVVDKLREQFRNKTIIFNQSS 70 80 90

Page 9: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Graphical Representation of the Secondary Structure of Subject 10’s Protein Sequence

Page 10: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Predicting the Secondary Structure of Subject 12’s Protein Sequence Using PSIPRED

H: HelicalE: ExtendedC: Random Coil

Conf:94782456777730699985885789636899874565045887100210733105676Pred:CEEEECCCCCCCCEEEEEECCCCEEEECCCCCCCCCCCCCCCCCCHHEECCCEECHHHH AA:EVVIRSKNFTDNAKIIIVQLNETVEINCTRPNNNTRKSIPIGPGRAFYTTGEIIGDIRQA 10 20 30 40 50 60

Conf: 10557878765799999999976189447750689 Pred: HHCCCHHHHHHHHHHHHHHHHHHHCCCEEEECCCC AA: HCNLSGAKWNETLKQIVIKLKEQFRNKTIVFSPSS 70 80 90

Page 11: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Graphical Representation of the Secondary Structure of Subject 12’s Protein Sequence

Page 12: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Locating the V3 Loop of the gp120 Protein

EVVIRSVNFTDNAKTTIIVQLNTSVEINCTGAGHCNISRAKWNNTLKQIASKLREQFGNNKTIIFKQSSGGDPEIVTHSFNCGGEFFYCNSTQLFNS

• By using the Kwong et. al article, we were able to identify the V3 region of the gp120 protein using the two representative clones.

Page 13: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

The V3 Loop of the gp120 Protein

Page 14: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Boxshade of Subjects 10 and 12 to find differences within their sequences

Page 15: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Compared Our Secondary Structure’s with the Kwong et al. 3-D Structure

• In the Kwong Structure: – At location 15 there is a threonine that resulted in an

extended sheet.– At location 38 there is an arginine that resulted in a helical

structure.– At location 66 there is a glutamine that resulted in an

extended sheet.– At location 78 there is a serine that resulted in an

extended sheet. – At location 93 there is a leucine that resulted in a random

coil.

Page 16: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Comparison Continued with Subject 10

• In our secondary structures we found:– Subject 10:

• At amino acid 15, the predicted structure, an extended sheet, was found

• At amino acid 38, the predicted structure was a random coil, which is not consistent with the actual structure.

• At amino acid 66, the predicted structure was helical, which is not consistent with the actual structure.

• At amino acid 78, the predicted structure was helical, which is not consistent with the actual structure.

• At amino acid 93, the predicted structure, a random coil, was found.

Page 17: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Comparison Continued with Subject 12

• In our secondary structures we found:– Subject 12:

• At amino acid 15, the predicted structure, an extended sheet, was found.

• At amino acid 38, the predicted structure was a random coil, which is not consistent with the actual structure.

• At amino acid 66, the predicted structure was helical, which is not consistent with the actual structure.

• At amino acid 78, the predicted structure was helical, which is not consistent with the actual structure.

• At amino acid 93, the predicted structure, a random coil, was found.

Page 18: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

15 T: Threonine, polar, hydrophilic

I:Isoleucine, nonpolar-

hydrophobic

T: Threonine, polar, hydrophilic

38 R:Arginine, basic, polar, hydrophilic

S:Serine, polar,(uncharged), hydrophilic

R:Arginine, basic, polar, hydrophilic

66 R:Arginine, basic, polar, hydrophilic

G:Glycine, hydrophobic

Q: Glutamine, polar, hydrophilic

78 D: Aspartic acid, acidic, polar, hydrophilic

I:Isoleucine, nonpolar-

hydrophobic

S: Serine, Polar,(uncharged), hydrophilic

93 Q: Glutamine, polar, hydrophilic

P:Proline, hydrophobic

L: Leucine, hydrophobic

Page 19: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

15 T:Threonine, polar, hydrophilic

I:Isoleucine, nonpolar-

hydrophobic

T:Threonine, polar, hydrophilic

•The closest side chain on the blue domain is a glutamine, which is polar.

•Between the two subjects there is a change between a hydrophobic and hydrophilic amino acid.

Page 20: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

38 R: Arginine, basic, polar, hydrophilic

S: Serine, polar, hydrophilic

R: Arginine, basic, polar, hydrophilic

•Arginine is greater in size than Serine.

•They are all hydrophilic amino acids.

Page 21: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

66 R:Arginine, basic, polar, hydrophilic

G:Glycine, hydrophobic

Q:Glutamine, polar, hydrophilic

•In subjects 10 and 12; the amino acid changes from hydrophilic to hydrophobic causing a potential structural difference.•Glutamine is larger in size than Glycine, but is closer in size to Arginine.

Page 22: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

78 D: Aspartic acid, acidic, polar, hydrophilic

I: Isoleucine, nonpolar,

hydrophobic

S: Serine,Polar, hydrophilic

•There is a difference between nonpolar and polar properties.•Isoleucine is larger in size than the other two amino acids.•Within the two subjects there is a change based off of hydrophobic and hydrophilic properties which has the potential to cause a structural difference.

Page 23: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Position Subject 10 Subject12 V3 Loop

93 Q:Glutamine, polar, hydrophilic

P:Proline, hydrophobic

L: Leucine, hydrophobic

•Both Proline and Leucine are hydrophobic and Glutamine is hydrophilic, which can cause a change in the structure.•Structurally, Leucine and Glutamine are similar, while Proline has a cyclical structure.

Page 24: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

There will be specific amino acids that cause differences in the protein structures of subjects

10 and 12• Based off of our results, we accept our hypothesis for the

tertiary structures of the proteins.• There were specific amino acid changes between the

subjects, but they did not result in predicted secondary structural changes.

• There were differences in the amino acids between our secondary structure and that of the actual V3 structure (from Kwong et al).

• This resulted in structural differences between them.

Page 25: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

Opportunities for Further Research

• Analyze an individual subjects amino acid sequence and protein structure to potentially create a neutralizing antibody for the HIV-1 virus.

• There are many features that help the gp120 protein escape the immune system of the host such as:– Variable Loops– N- Linked Glycosylation– Confomational Flexibility

Page 26: Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March

References• Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A,

Faradegan H, and Yu XF. Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proc Natl Acad Sci U S A 1998 Oct 13; 95(21) 12568- 73.

• Kwong PD, Wyatt R, Robinson J, Sweet RW, Sodroski J, and Hedrickson WA. Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody. Nature 1998 Jun 18; 393(6686) 648-59.

• Chen L, Kwon YD, Zhou T, Wu X, O’Dell S, Cavacini L, Hessell AJ, Pancera M, Tang M, Xu L, Yang ZY, Zhang MY, Arthos J, Burton DR, Dimitrov DS, Nabel GJ, Posner MR, Sodroski J, Wyatt R, Mascola JR, Kwong PD. Structural basis of immune evasion at the site of CD4 attachment on HIV-1 gp120. Science. 2009 Nov 20;326(5956):1123-7.