HLA MHCs are the gatekeepers of the immune system. 1.) LOCATE: Present peptides that may be viral....
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Transcript of HLA MHCs are the gatekeepers of the immune system. 1.) LOCATE: Present peptides that may be viral....
HLA
MHCs are the gatekeepers of the immune system.
1.) LOCATE: Present peptides that may be viral.
2.) ACTIVATE: Activate immune defense mechanisms.
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
# 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 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
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
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
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
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
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
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
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
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
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
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
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
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
What fits in an HLA?
Quantitative Matrices:
Calculate theoretically?
If 0.081 > 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
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
1. SelfOf peptides bound by HLAs, which viral 9-mer peptides are not in self?
Self 9-mers bound by HLAsAAAAAAAI AAAAAAAAL AAAAAAAAV AAAAAAAGV
AAAAAAAHL AAAAAAAKM AAAAAAALV AAAAAAANI
AAAAAAANL AAAAAAAPV AAAAAAASL AAAAAAAVI
AAAAAAAVV AAAAAADKL AAAAAADKW AAAAAAFKL
AAAAAAGEL AAAAAAGGL AAAAAAGGV AAAAAAGKL
AAAAAAGQI AAAAAAGRV AAAAAAGSL AAAAAAIGI
AAAAAALAL AAAAAALCV AAAAAALDL AAAAAALTL
. . .
1. SelfOf peptides bound by HLAs, which viral 9-mer peptides are not in self?
Viral 9-mers bound by HLAsAACWWAGIK AACWWAGIK ADDTVLEEM AELELAENR
AETFYVDGA AETFYVDGA AETFYVDGA AETFYVDGA
AETGQETAY AETGQETAY AEVIPAETG AEVIPAETG
AFSPEVIPM AGDDCVASR AGERIVDII AGERIVDII
AGERIVDII AGERIVDII AGERIVDII AGIKQEFGI
AGIKQEFGI AGIKQEFGI AGIKQEFGI AGIRKVLFL
AGIRKVLFL AGIRKVLFL AGIRKVLFL AGIRKVLFL
. . .
1. SelfOf peptides bound by HLAs, which viral 9-mer peptides are not in self?
None!
No viral 9-mer peptides are in self.
1. SelfOf 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^-5No autoimmune symptoms.
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 1 - 70
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 100 - 160
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 170 - 240
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 800 -
2. ConservedWhich 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. ConservedWhich 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. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 1 - 70
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 100 - 160
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 170 - 240
2. ConservedWhich viral peptides sequences are preserved through genetic mutations? Alignment Program: ClustalX
v1.8
Selection of env Proteins, AA: 800 -
2. ConservedWhich viral peptides sequences are preserved through genetic mutations?
Considering only those that are completely conservedTotal # sequences = 537
2. ConservedWhich viral peptides sequences are preserved through genetic mutations?
Considering only those that are completely conservedTotal # 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
2. ConservedWhich viral peptides sequences are preserved through genetic mutations?
Which of these conserved sequences make ‘good’ candidate peptides?
DDTVLEEHKAIGTTHLEG - conserved 19 AA sequenceDDTVLEEHKAIGTTHLEGDDTVLEEHKAIGTTHLEG
DDTVLEEHKAIGTTHLEG
DDTVLEEHKAIGTTHLEGDDTVLEEHKAIGTTHLEG
DDTVLEEHKAIGTTHLEGDDTVLEEHKAIGTTHLEGDDTVLEEHKAIGTTHLEGDDTVLEEHKAIGTTHLEG
peptides to be tested
DDTVLEEHKAIGTTHLEG
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
0. Fitting the HLACan 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 HLACan 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 HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
0. Fitting the HLA # f S HLA Least :
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
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