Amino Acid Sequences in V3 Loop Conformation

Slides:

Advertisements

Similar presentations

Changes In Protein Sequences Of the HIV-1 gp120 V3 Region In Non-Progressor Types Nicki S.Harmon Samantha M. Hurndon.

Advertisements

Genetic Similarities Between HIV-1 Viruses in the Onset of AIDS Isabel Gonzaga BIOL : Bioinformatics Laboratory Loyola Marymount University October.

Dual conformations for the HIV-1 gp120 V3 loop in complexes with different neutralizing Fabs RL Stanfield, E Cabezas, AC Satterthwait, EA Stura, AT Profy,

Materials and Methods Abstract Conclusions Introduction 1. Korber B, et al. Br Med Bull 2001; 58: Rambaut A, et al. Nat. Rev. Genet. 2004; 5:

An Introduction to the HIV Problem Space Oakwood University: Faculty Quantitative Institute Aug. 10–12, 2009.

Examination of Amino Acid Differences as a Means of Determining Functional Changes in HIV-1 Protein Sequences Chris Rhodes and Isaiah Castaneda Loyola.

DNA methylation as an epigenetic marker in HIV-2 disease in West Africa? Alberta Davis MRC Laboratories, Gambia 18th January 2013.

Analysis of HIV Evolution Bobak Seddighzadeh and Kristoffer Chin Department of Biology Loyola Marymount University Bio February 23, 2010.

The Influence of CCL3L1 Gene- Containing Segmental Duplications on HIV-1/AIDS Susceptibility Gonzalez et al. Mar 4, 2005 :307 Science Presenter: Braydon.

Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.

Unconserved Amino Acid Sequences in V3 Domain of gp120 Show No Significant Correlation to Altered Folding and Function Bobak Seddighzadeh Alex George.

1 Resistance and Tropism - Maraviroc Lisa K. Naeger, Ph.D. Division of Antiviral Products Food and Drug Administration April 24, 2007 FDA Antiviral Advisory.

STEPHANIE HINTZEN BIOL 471 SIV and HIV: Differences in Diversity and Divergence.

Re evaluating the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi & Ryan Willhite Loyola Marymount University.

A Mutational Investigation of an HIV Patient’s GP120 Glycoprotein and it’s Implications on CD4 Binding Salita Kaistha Usrinus College, Collegeville PA.

Predicting the Onset of AIDS Robert Arnold, Alex Cardenas, Zeb Russo LMU Biology Department 10/5/2011.

Supporting Scientific Collaboration Online SCOPE Workshop at San Diego Supercomputer Center March 19-22, 2008.

Patterns of selection for or against amino acid change among different CD4 T-cell count progressor groups Michael Pina, Salomon Garcia Journal Club Presentation.

Diversity and Divergence in HIV-1 Viral Variants between patients with high CD4+ T Cell Variability and Patients with Rapid CD4+ T Cell Decline Kevin Paiz-Ramirez.

Mutations on the V3 loop of gp120 may predict progression to AIDS Derese Getnet, Dr. Rebecca Roberts, Structural Biology, Ursinus College, Collegeville.

Amino Acid Sequences in V3 Loop Conformation Alex Cardenas, Bobby Arnold and Zeb Russo Loyola Marymount University Department of Biology BIO /02/11.

Project in BioInformatics Variability of Membrane proteins of different HIV strains By Emad Nimer Wisam Kadry.

Predicting the Onset of AIDS Robert Arnold, Alex Cardenas, Zeb Russo LMU Biology Department 10/5/2011.

Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.

Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.

Recurring conformation of the human immunodeficiency virus type 1 gp120 V3 loop Robyn L. Stanfield, Jayant B. Ghiara, Erica O. Saphire, Albert T. Profy,

Role of V3 in HIV Entry and Neutralization Chloe Jones, Isabel Gonzaga, and Nicole Anguiano BIOL398: Bioinformatics Laboratory Loyola Marymount University.

Recurring conformation of the human immunodeficiency virus type 1 gp120 V3 loop. Stanfield RL, Ghiara JB, Ollmann Saphire E, Profy AT, and Wilson IA.

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

Changes In Protein Sequences Of the HIV-1 gp120 V3 Region In Non-Progressor Types Nicki S.Harmon Samantha M. Hurndon LMU Department of Biology BIOL 368.

Examining the Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.

Examining Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano Bioinformatics Laboratory Loyola.

V3 Loop Binding to CCR5 and CXCR4 of Rapid and Non Progressor HIV Patients in Baltimore MD BackgroundCCR5 and CXCR4 Receptor Rapid-progressor Patient 10.

Structure of V3-containing HIV-1 gp120 core Huang, C. C., Tang, M., Zhang, M. Y., Majeed, S., Montabana, E., Stanfield, R. L., Dimitrov, D. S., Korber,

Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.

Examining Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.

Investigations of HIV-1 Env Evolution Evolutionary Bioinformatics Education: A BioQUEST Curriculum Consortium Approach Grand Valley State University August.

Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,

Journal Club Presentation BIOL368/F16: Bioinformatics Laboratory

Colin Wikholm William Fuchs

Amino Acid Sequences in V3 Loop Conformation

BIO 402 STUDY Education for Service-- bio402study.com.

The V3 Region Expresses More Diversity in Amino Acid Sequence in AIDS Diagnosed Patients than in Non-Trending and AIDS Progressing Patients HIV Structure.

Do HIV+ Rapid Progressors Show More Divergence than Non-Progressors?

Predicting the Onset of AIDS

Anindita Varshneya, Jordan Detamore, Colin Wikholm, Isai Lopez

Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,

FcRL6, a new ITIM-bearing receptor on cytolytic cells, is broadly expressed by lymphocytes following HIV-1 infection by Timothy J. Wilson, Rachel M. Presti,

Volume 37, Issue 5, Pages (March 2010)

Investigations of HIV-1 Env Evolution

Structure of V3-containing HIV-1 gp120 core

Loyola Marymount University

LMU Department of Biology

Dual conformations for the HIV-1 gp120 V3 loop in complexes with different neutralizing Fabs RL Stanfield, E Cabezas, AC Satterthwait, EA Stura, AT Profy,

Predicting the Onset of AIDS

Loyola Marymount University

Loyola Marymount University

Nicki Harmon, Samantha Hurndon, & Zeb Russo

Amino Acid Sequences in V3 Loop Conformation

Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,

Postoperative changes in blood monocyte subsets.

Sequence alignment of PHCCEx domains with secondary structure elements of the Tiam2 PHCCEx domain at the top. Sequence alignment of PHCCEx domains with.

Predicting the Onset of AIDS

Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.

Alignment of the deduced amino acid sequences of the myosin light chain 2 (MLC2) proteins. Alignment of the deduced amino acid sequences of the myosin.

Chloe Jones, Isabel Gonzaga, and Nicole Anguiano

PubMed Research Article

Loyola Marymount University

Presentation transcript:

Amino Acid Sequences in V3 Loop Conformation Alex Cardenas, Bobby Arnold and Zeb Russo Loyola Marymount University Department of Biology BIO 398 11/02/11

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

CD4 T cell count is an trait in developing AIDs ( >200 safe, <200 AIDs) Observations led us to conclude that CD4 T cell counts were crucial in developing AIDs. Rapid Progressors – 1, 3, 4, 10, 11, 15. Controls Moderate progressor – 6. Non Progessor – 13.

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

CD4 T cell count and relationship to V3 loop amino acid sequence We were interested in the amino acid sequences of AIDs subjects. Our question Is there a conserved region in the V3 loop sequence that led the subject to develop AIDs?

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

Usage of Star Biochem and to Answer our Question Star Biochem was used to determine the location and structure of the V3 loop region ProtParam was considered as a tool to use, however all the variable regions were determined to be coils so therefore was not necessary

gp120 Secondary Structure Using Star Biochem Kwong et. al V3 region

V3 region Secondary Structure Stanfield et. al 1NAK 312-323

Stanfield et. al 2ndary Structure not useful in our examination of Markham data Stanfield data composed of 12-14 amino acid chains Markham data composed of 94-95 amino acid chains

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

BioWorkbench used to create multiple sequence alignments Each rapid progressor subject had a clone randomly chosen from their first and last visit Control subjects had one clone randomly selected from last visit. Each rapid progressor had their two sequences aligned Control subjects were aligned with each other All rapid progressor last visits were aligned with and without a control sequence

ClustalW Multiple Sequence Alignment Results Single Subject Alignments Multiple Subject Alignments

Outline CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). Differing CD4 T cell count is correlated with conservation in amino acid sequences. Usage of Star Biochem to Answer our Question BioWorkbench used to create multiple sequence alignments Results obtained from multiple sequence were observed and analyzed. Discussion and thoughts of our findings are shared.

PubMed Research Article “Identification and structural characterization of novel genetic elements in the HIV-1 V3 loop regulation coreceptor usage” by Svicher et al. Studied the interaction between HIV-1gp120 and CCR5 N terminus

HIV-1 entry involves gp120, CD4 recepor and CCR5 or CXCR4 V3 loop is determining factor in coreceptor usage Composed of 3 regions Base Stem Hairpin crown V3 positions 11, 24 and 25 bind with different coreceptors but mechanism that differentiates is not know

V3 base interaction with CCR5 critical for infection Increased affinity of V3 for CCR5 Study population: 323 blood samples from 323 HIV-1 patients V3 mutation was tested for along with wild-type residues to see different coreceptor usage Structural analysis taken from Huang et al. CCR5 model with HIV-1 gp120

V3 sequence determines coreceptor usage 19 V3 sequence positions that correlated to CCR5 of CXCR4 usage Of 29 mutations, 6 are at the V3 positions of 11, 24 and 25 select for CCR5 23 other mutations at 15 V3 positions select for CXCR4 Mutations for CCR5 not seen in viruses that select for CXCR4 and vice versa Less that 1% of mutation in each case