HLA MHCs are the gatekeepers of the immune system. 1.) LOCATE: Present peptides that may be viral. 2.) ACTIVATE: Activate immune defense mechanisms.

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Presentation transcript:

HLA

MHCs are the gatekeepers of the immune system. 1.) LOCATE: Present peptides that may be viral. 2.) ACTIVATE: Activate immune defense mechanisms.

HLA Class AA AA Class 2 # ~80# ~40 ClosedOpen

HLA Understanding the HLA: Structural chemistry (x-ray crystallography), biological processes, role within the immune system, binding behavior, statistical distribution. 2 ways of looking at this: 1.) Descriptive 2.) Functional

HLA Understanding the HLA: Structural chemistry (x-ray crystallography), biological processes, role within the immune system, binding behavior, statistical distribution. 2 ways of looking at this: 1.) Descriptive 2.) Functional

What fits in an HLA?

Find out experimentally? # Class I HLA’s≃ 80

What fits in an HLA? Find out experimentally? # Class I HLA’s≃ 80 # Possible 9-mers: 20^9= 512,000,000,000 ≃ 10^11

What fits in an HLA? Calculate theoretically? Binding Motifs Quantitative Matrices Molecular Artificial Neural Network Hidden Markov Models

What fits in an HLA? Binding Motifs: Hypothesis: HLA binding entirely determined by a few AA (1-3) on the peptide. Approach: Check peptide for anchor residue. Calculate theoretically?

What fits in an HLA? Binding Motifs: i.e A1 Serotype:X X [D/E] X X X [Y] X X [D/E] X X X X [Y] X X [D/E] X X X X X [Y] Binds peptides:BK[D]LGGSD[Y] AC[D]SWIH[Y] Calculate theoretically?

What fits in an HLA? Quantitative Matrices: Hypothesis: Binding is determined by AA on the specific HLA. Approach: Construct a virtual matrix and determine a threshold value. Check if product of AA values in virtual matrix exceed threshold. Calculate theoretically?

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant 0.002

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE

What fits in an HLA? Quantitative Matrices: Calculate theoretically? 9-mer Coefficient Table for HLA_A3 Amino Acid Type Position 1st 2n 3rd 4th 5th 6th 7th 8th 9th A C D E F G V W Y final constant ADCGTVMCE 1.0 x 0.1 x 1.0 x 1.5 x 3.0 x 3.0 x 3.0 x 1.0 x 10.0 x = 0.081

What fits in an HLA? Quantitative Matrices: Calculate theoretically? If > threshold, then ADCGTVMCE is bound by class I HLA A3.

Vaccination

1.) Determine a ‘good’ viral peptide sequence. 2.) Create and inject these peptides. - Candidate peptide bound and presented on HLA - Stimulate immune response - Subject is protected from virus

Vaccination What is a ‘good’ viral peptide sequence? 0.) Fits HLA 1.) Minimal overlap with self-peptides 2.) Preserved through genetic mutations 3.) Binds strongly to HLA

Vaccination What is a ‘good’ viral peptide sequence? 0.) Fits HLA 1.) Minimal overlap with self-peptides 2.) Preserved through genetic mutations 3.) Binds strongly to HLA Ideally: Fits all HLA (of type I or II)

Vaccination What is a ‘good’ viral peptide sequence? 0.) Fits HLA 1.) Minimal overlap with self-peptides 2.) Preserved through genetic mutations 3.) Binds strongly to HLA Ideally: No overlap with self-peptides

Vaccination What is a ‘good’ viral peptide sequence? 0.) Fits HLA 1.) Minimal overlap with self-peptides 2.) Preserved through genetic mutations 3.) Binds strongly to HLA Ideally: Sequence is perfectly conserved

Vaccination What is a ‘good’ viral peptide sequence? 0.) Fits HLA 1.) Minimal overlap with self-peptides 2.) Preserved through genetic mutations 3.) Binds strongly to HLA Ideally: Binds optimally to HLA

HIV1-B

Total AA length of proteins ≃ 3000 AA

HIV1-B Peptides considered from protein segments. Considered within class 1 HLAs only. Quantitative matrix of 9-mers. Parker, K. C., M. A. Bednarek, and J. E. Coligan Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J. Immunol. 152:163

1. Self Of peptides bound by HLAs, which viral 9-mer peptides are not in self? Self 9-mers bound by HLAs AAAAAAAIAAAAAAAALAAAAAAAAVAAAAAAAGV AAAAAAAHLAAAAAAAKMAAAAAAALVAAAAAAANI AAAAAAANLAAAAAAAPVAAAAAAASLAAAAAAAVI AAAAAAAVVAAAAAADKLAAAAAADKWAAAAAAFKL AAAAAAGELAAAAAAGGLAAAAAAGGVAAAAAAGKL AAAAAAGQIAAAAAAGRVAAAAAAGSLAAAAAAIGI AAAAAALALAAAAAALCVAAAAAALDLAAAAAALTL...

1. Self Of peptides bound by HLAs, which viral 9-mer peptides are not in self? Viral 9-mers bound by HLAs AACWWAGIKAACWWAGIKADDTVLEEMAELELAENR AETFYVDGAAETFYVDGAAETFYVDGAAETFYVDGA AETGQETAYAETGQETAYAEVIPAETGAEVIPAETG AFSPEVIPMAGDDCVASRAGERIVDIIAGERIVDII AGERIVDIIAGERIVDIIAGERIVDIIAGIKQEFGI AGIKQEFGIAGIKQEFGIAGIKQEFGIAGIRKVLFL AGIRKVLFLAGIRKVLFLAGIRKVLFLAGIRKVLFL...

1. Self Of peptides bound by HLAs, which viral 9-mer peptides are not in self? None! No viral 9-mer peptides are in self.

1. Self Of peptides bound by HLAs, which viral 9-mer peptides are not in self? # Possible 9-mers: 20^9= 512,000,000,000 ≃ 10^11 # Human 9-mers: = 2,981,644= 10^6 # Human / # Possible 9-mers = 10^-5 No autoimmune symptoms.

2. Conserved Which viral peptides sequences are preserved through genetic mutations?

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 800 -

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Align protein sequences: Match conserved segments to each other Virus 1:[A] [B] [C] [Y] [A] [B] [C] … Virus 1’:[A] [B] [C] [A] [Y] [A] [B] [C] …

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Align protein sequences: Match conserved segments to each other Virus 1:[A] [B] [C] --- [Y] [A] [B] [C] … Virus 1’:[A] [B] [C] [A] [Y] [A] [B] [C] …

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA:

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 800 -

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Considering only those that are completely conserved Total # sequences = 537

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Considering only those that are completely conserved Total # sequences > 8 AA = 76 1 : : 93 3 : 71 4 : 50 5 : 28 6 : 18 7 : 14 8 : 10 9 : 6 10 : 3 11 : 9 12 : 3 13 : 3 14 : 3 15 : 5 16 : 3 17 : 1 18 : 2 19 : 2 20 : 3 22 : 1 23 : 2 24 : 1 25 : 4 26 : 3 29 : 1 30 : 4 34 : 1 37 : 1 38 : 1 40 : 1 41 : 1 43 : 1 47 : 1 48 : 2 49 : 1 51 : 1 52 : 1 57 : 1 59 : 1 60 : 1 72 : 1 87 : 1

2. Conserved Which viral peptides sequences are preserved through genetic mutations? Which of these conserved sequences make ‘good’ candidate peptides? DDTVLEEHKAIGTTHLEG - conserved 19 AA sequence DDTVLEEHKAIGTTHLEG peptides to be tested DDTVLEEHKAIGTTHLEG

0. Fitting the HLA

Can we fit all HLAs?

0. Fitting the HLA Can we fit all HLAs? For certain proteins: env, pol (1,2), gag, tat

0. Fitting the HLA # f S HLA 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA Can we fit all HLAs? For certain proteins: Optimal set of candidate sequences for a given protein? env, pol (1,2), gag, tat

0. Fitting the HLA Can we fit all HLAs? For certain proteins: Optimal set of candidate sequences for a given protein? Classical network algorithm : Min-cost-Max-flow env, pol (1,2), gag, tat

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT CVKLTPLCV DNWRSELYK FLGAAGSTM GAAGSTMGA GCSGKLICT GFLGAAGST GIVQQQNNL IVQQQNNLL KLTPLCVTL LGFLGAAGS LTVWGIKQL NVSTVQCTH NWRSELYKY RSELYKYKV SGIVQQQNN STVQCTHGI VKLTPLCVT WGCSGKLIC WGIKQLQAR WRSELYKYK 24

Candidate sequences CVKLTPLCV // FLGAAGSTM // GAAGSTMGA // GIVQQQNNL // KLTPLCVTL // RSELYKYKV HIV1-B env Protein (all) HIV1-B tat Protein (1,2,15,16,17,19,27) EPWKHPGSQ // GISYGRKKR HIV1-B pol(1) Protein (all) 14 HIV1-B pol(2) Protein (all) 11 HIV1-B gag Protein (all but 15, 19, 21) 9

Implementation “Sequence signals for generation of antigenic peptides by the proteasome: implications for proteasomal cleavage mechanism.” Altuvia Y, Margalit H CVKLTPLCV // FLGAAGSTM // GAAGSTMGA // GIVQQQNNL // KLTPLCVTL // RSELYKYKV // = Cleave signal.