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HLA
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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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HLA Class 1 8-10 AA 13-25 AA Class 2 # ~80# ~40 ClosedOpen
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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
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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
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What fits in an HLA?
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Find out experimentally? # Class I HLA’s≃ 80
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What fits in an HLA? Find out experimentally? # Class I HLA’s≃ 80 # Possible 9-mers: 20^9= 512,000,000,000 ≃ 10^11
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What fits in an HLA? Calculate theoretically? Binding Motifs Quantitative Matrices Molecular Artificial Neural Network Hidden Markov Models
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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?
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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?
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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?
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 ADCGTVMCE
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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 C 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 D 0.300 0.100 1.000 1.000 1.000 1.000 1.000 1.000 0.100 E 0.300 0.300 1.000 1.500 1.000 1.000 1.000 1.000 0.100 F 1.000 0.100 5.000 1.000 3.000 3.000 3.000 1.000 10.000 G 3.000 0.100 1.000 1.500 1.000 1.000 0.300 1.000 0.100... V 1.000 10.000 1.500 1.000 1.500 3.000 1.000 1.000 1.000 W 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 1.000 Y 1.000 0.100 5.000 1.000 3.000 1.000 3.000 1.000 20.000 final constant 0.002 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 0.002 = 0.081
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What fits in an HLA? Quantitative Matrices: Calculate theoretically? If 0.081 > threshold, then ADCGTVMCE is bound by class I HLA A3.
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Vaccination
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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
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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
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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)
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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
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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
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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
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HIV1-B
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Total AA length of proteins ≃ 3000 AA
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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. 1994. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J. Immunol. 152:163
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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...
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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...
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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.
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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.
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2. Conserved Which viral peptides sequences are preserved through genetic mutations?
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 1 - 70
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 100 - 160
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 170 - 240
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 800 -
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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] …
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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] …
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 1 - 70
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 100 - 160
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 170 - 240
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX v1.8 Selection of env Proteins, AA: 800 -
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Considering only those that are completely conserved Total # sequences = 537
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2. Conserved Which viral peptides sequences are preserved through genetic mutations? Considering only those that are completely conserved Total # sequences > 8 AA = 76 1 : 177 2 : 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
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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
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0. Fitting the HLA
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Can we fit all HLAs?
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0. Fitting the HLA Can we fit all HLAs? For certain proteins: env, pol (1,2), gag, tat
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0. Fitting the HLA # f S HLA 1 1 CSGKLICTT 4 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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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
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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
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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0. Fitting the HLA # f S HLALeast : 1 1 CSGKLICTT 4 15 10 8 5 4 2 2 2 CVKLTPLCV 10 1 3 1 DNWRSELYK 19 4 3 FLGAAGSTM 7 19 33 5 3 GAAGSTMGA 6 2 27 6 1 GCSGKLICT 10 7 1 GFLGAAGST 13 8 10 GIVQQQNNL 33 2 3 11 13 18 19 26 30 32 9 7 IVQQQNNLL 33 1 11 13 18 22 32 10 10 KLTPLCVTL 7 3 11 12 13 18 19 24 32 33 11 1 LGFLGAAGS 2 12 9 LTVWGIKQL 9 2 3 11 13 18 26 30 32 13 1 NVSTVQCTH 1 14 1 NWRSELYKY 13 15 2 RSELYKYKV 4 20 16 1 SGIVQQQNN 2 17 3 STVQCTHGI 3 2 27 18 1 VKLTPLCVT 26 19 1 WGCSGKLIC 5 20 1 WGIKQLQAR 16 21 2 WRSELYKYK 24
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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
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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.
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