by Vladimir Jojic A thesis submitted in conformity - Stanford AI Lab

114 Pages·2007·1.51 MB·English

ALGORITHMS FOR RATIONAL VACCINE DESIGN
by
Vladimir Jojic
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Graduate Department of Computer Science
University of Toronto
Copyright (cid:13)c 2007 by Vladimir Jojic
ii
Abstract
Algorithmsforrationalvaccinedesign
VladimirJojic
DoctorofPhilosophy
GraduateDepartmentofComputerScience
UniversityofToronto
2007
Design of an HIV vaccine has proven to be a difficult challenge. Vaccines designed by the
traditional approach based on using a single weakened virus or a portion of a virus have not
provided sufficient protection. The large variability of the HIV virus population is believed to
be the main cause of failure for the vaccine candidates. In this thesis, I introduce an immuno-
logically motivated vaccine score which accounts for the variability of the target HIV popula-
tion. The exact optimization of this score is an NP-hard problem. I introduce algorithms, both
approximate and exact, for designing a high scoring vaccine. The approximate methods are
based on expectation maximization methods and use approximate probabilistic inference. The
exact method is based on a branch-and-cut method for the asymmetric orienteering problem.
Thescoreandthealgorithmsformaximizingthescorearevalidatedbothin-silicoandin-vitro.
In-silico comparisons are made to other methods aimed at overcoming HIV variability. The
in-vitroElispotexperimentsdemonstrateanexpectedprotectionin∼ 90%ofinfections.
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Acknowledgements
My gratitude goes out to my advisor Brendan Frey. He has provided me with intellec-
tual, financial and editorial support before and during the writing of this thesis. His guidance
throughoutmygraduatestudieshasbeeninvaluabletome. Iamalsogratefultomycommittee
members, Quaid Morris, Ryan Lilien, Radford Neal, as well as my external examiner Simon
Mallal,whosecommentsmadesignificantimpactonthequalityofthisthesis.
Thank you to all the members of the PSI group for providing a lively and stimulating
atmosphere.
Interaction with Microsoft Research’s Machine Learning and Applied Statistics group has
shaped both this thesis and my understanding of the machine learning area. In particular, I
havebeenluckytoworktogetherwithDavidHeckerman,ChrisMeekandmybrotherNebojsa
Jojic. Nebojsa’shelphasbeeninvaluableinmanyrespects,scientificandotherwise.
I am thankful to the University of Toronto and Microsoft Research for the funding which
hasmadecompletingmygraduatestudiesthatmucheasier.
Contents
1 Introduction 1
2 ImmunesystemandHIVinfection 5
2.1 Theimmunesystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Viralinfectionandresponse . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Howthecellsignalsitsinfection . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Howinfectedcellsarerecognizedandkilled . . . . . . . . . . . . . . . . . . . 10
2.4.1 HowadiverseT-cellrepertoireisshaped . . . . . . . . . . . . . . . . 10
2.4.2 HowaT-cellbecomesakillerT-cell . . . . . . . . . . . . . . . . . . . 11
2.5 HIVstructureandinfectioncycle . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5.1 HIVearlyphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5.2 HIVlatephase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 Backgroundonvaccinedesign 15
3.1 HIVvariability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 HIVvaccinedelivery,targetsandconstraints . . . . . . . . . . . . . . . . . . . 17
3.3 Syntheticvaccinedesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 Cocktailsofsyntheticvaccines . . . . . . . . . . . . . . . . . . . . . . 25
3.3.2 UnderlyingassumptionsaboutHIVandimmunology . . . . . . . . . . 26
3.4 Predictioninvaccinedesign . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.1 Physicalinteractionmodelsversusmachinelearningapproaches . . . . 30
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CONTENTS v
3.4.2 Probabilisticmodelsofprocessingandpresentation . . . . . . . . . . . 31
3.5 InvitroexperimentsforassayingpeptidebindingandT-cellrecognition . . . . 32
4 Computationalvaccineoptimization 34
4.1 Maximumcoverageformulationofvaccinedesign . . . . . . . . . . . . . . . . 36
4.2 Greedyalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Anexactmethodforcoverageoptimization . . . . . . . . . . . . . . . . . . . 40
4.3.1 Thevaccinedesignproblemasaninstanceofanasymmetricorienteer-
ingproblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3.2 Branch-and-cutmethodfortheasymmetricorienteeringproblem . . . . 43
4.4 Generativemodelsofpeptides . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.5 Favoringuniqueepitopepositionsanddistinctvaccines . . . . . . . . . . . . . 48
4.5.1 DerivationofthevariationalE-step . . . . . . . . . . . . . . . . . . . 50
4.5.2 DerivationofthevariationalMstep . . . . . . . . . . . . . . . . . . . 52
4.6 Garbagemodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5 Results 57
5.1 Comparisonofvaccinedesignmethodsusingcoverage . . . . . . . . . . . . . 57
5.2 Wet-labvalidationofvaccinebasedonoptimizingcoverage . . . . . . . . . . . 67
6 Possibleextensionstothiswork 73
6.1 Incorporatingmodelsofimmunologicalprocesses . . . . . . . . . . . . . . . . 73
6.1.1 Samplingbiasandmodelsofimmunologicalprocesses . . . . . . . . . 74
6.1.2 Utilizingamodelofimmunogenicity . . . . . . . . . . . . . . . . . . 75
6.2 Simplecross-reactivitymodel . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.3 HIVevolutioninpresenceofvaccine . . . . . . . . . . . . . . . . . . . . . . . 78
6.4 Usingmultipleframesforavaccine . . . . . . . . . . . . . . . . . . . . . . . 81
6.4.1 Multipleframevaccineoptimization . . . . . . . . . . . . . . . . . . . 81
6.4.2 Multiframevaccinein-silicoexamination . . . . . . . . . . . . . . . . 84
CONTENTS vi
6.5 Challengestocoverageoptimization . . . . . . . . . . . . . . . . . . . . . . . 90
6.5.1 Negativedesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.5.2 Immunodominance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7 Conclusion 94
Bibliography 97
Chapter 1
Introduction
This thesis deals with the design of a synthetic preventative vaccine for the human immunod-
eficiency virus (HIV). I formulate the problem of vaccine design as an optimization problem.
The essential part of this problem formulation is a vaccine score based on the biology of HIV
andhumanimmunesystem,specificallyT-cellresponse. Ialsoproposemethods,someapprox-
imate and some exact, for producing vaccines with a high score. The biological experiments
indicate higher levels of protection by vaccines designed using my methods than the vaccines
producedbythepreviouslyproposedmethods.
Previous attempts at HIV vaccine design have failed in providing the desired level of pro-
tectionagainsttheinfection. OneofthechallengesfacingHIVvaccinedevelopmentliesinthe
enormous variability of the HIV virus [30, 9, 75, 34, 61], which stems from the ability of the
virus to mutate rapidly. The diversity of human immune types further increases the challenge
of designing a single universal vaccine, as the different immune system types detect different
signalsofinfection. Iproposevaccinedesignmethodologythataddressesbothofthesesources
ofvariability.
Inthisthesis,IfocusonthemethodologyofdevelopingaT-cellvaccineagainstHIV.Avital
way in which T-cells contribute to the human immune response is by destruction of infected
cells. The T-cells recognize infected cells by detecting short viral peptides exhibited on the
1
CHAPTER 1. INTRODUCTION 2
surface of the cell. These viral peptides are short contiguous fragments of the viral protein
of length of 8-10 amino acids. The viral peptides can originate from any position in a viral
protein; hence the number of viral peptides from a single viral protein is only slightly smaller
than the length of viral protein. Since the virus mutates, many variants of the protein exist
and the set of viral peptides is quite large. There are two further characteristics of the viral
peptide that play a role in the methods developed in the thesis. First, not all viral peptides can
be recognized by all of the immune system types. The set of viral peptides provided by the
vaccine should contain suitable peptides for each of the immune system types. Second, there
is a significant amount of overlap between peptides, e.g., the amino acids at the end of one
peptidearethebeginningaminoacidsofanotherpeptide.
The immune response is greatly improved if the viral peptides have been “learned” by the
immune system prior to the infection. It is the role of a vaccine to provide examples of viral
peptides for the immune system to learn. The learning mechanism is essentially the same
mechanism used for detecting the presence of a virus in a cell. The fragments of the vaccine
are presented on the cell wall and detected by the immune system. It seems natural, then, that
creating vaccines containing a large number of viral peptides would provide good protection.
However,compressingthelargesetofpeptidesrequiredintoacompactvaccine,whichinitself
is a string of amino acids, seems to be a quite difficult task. The key observation is that many
ofthepeptideshavesignificantoverlap,sincetheystemfromneighboringpositionsintheviral
protein.
Previously proposed algorithms for designing synthetic vaccines [33, 64] were based on
the assumption that the diversity challenge can be overcome by utilizing a single “central” se-
quencesimilartoallofthevariantsofthevirus. Thissimilarityisdefinedintermsofposition-
wise distance between sequences: evolutionary distance was used in the case of the Center of
Tree(COT)andrelatedancestralmethods[64];adistancebasedonthenumberofmismatches
between sequences was used in case of consensus methods [66]. The underlying assumption
isthatthe“centrality”ofthesequenceentailsrecognitionofviralpeptidesintheviralproteins
CHAPTER 1. INTRODUCTION 3
by an immune system trained on viral peptides in the vaccine . Due to the high variability of
thevirus,thisassumptionneednotholdoutsideofhighlyconservedregions.
In this thesis, I propose a method founded on a vaccine score that takes into account the
nature of T-cell immune response and its basis in detection of viral peptides. The score of a
vaccine is the fraction of all viral peptides present in the vaccine. The set of viral peptides is
obtained from a dataset of viral sequences; in this thesis I use the set of sequences referred to
as the Perth dataset from [60]. The set of viral peptides may be restricted to peptides from a
particularviralregionorviralprotein. Hence,peptidesthatoccurinmanyviralsequences,the
more frequent peptides, will contribute more to the coverage score of a vaccine than the less
frequent ones. The larger the score, the more likely it is that a viral peptide occurred in both
the vaccine and an infecting virus. If the peptide had been learned by the immune system of a
vaccine recipient, then the infection can be efficiently cleared by the recipient. In this thesis, I
assumethatfrequencyofappearanceofpeptidesinthevaccinetranslatestoincreasedlearning
ofthepeptidebytheimmunesystem.
I propose algorithms for finding a vaccine with a high score. I show that the problem of
finding the optimal vaccine is NP-hard, necessitating use of approximate algorithms to solve
the problem. The essence of the algorithms lies in exploiting the overlap in the viral peptides
in order to produce a compact vaccine. The algorithms include a greedy algorithm, closely re-
latedtosequenceassemblyalgorithms[27],andanumberofalgorithmsbasedonprobabilistic
inference, inspired by the epitome model [39], and an exact algorithm, based on the branch-
and-bound ideas. Other challenges facing vaccine design such as leveraging cross-reactivity,
utilizing multiple coding frames, avoiding auto-immune response and immunodominance are
addressedattheendofthethesisandpointtowardspossibleextensionsofthemethodology.
The thesis starts off with a review of relevant immunology: in particular, the basics of
the formation of the CD8 T-cell repertoire, as well as the role of the human leukocyte antigen
types. AreviewoftheHIVlifecycleisprovidednext. Thelastportionofthereviewfocuseson
previously-proposedvaccinedesignmethodsandthestateoftheartinvaccinedesign. Anum-
CHAPTER 1. INTRODUCTION 4
ber of predictive methods which may be used to guide the vaccine design are also presented.
Readersfamiliarwiththematerialofthesereviewsectionsmaywishtoskipthemandproceed
to chapter 4 which introduces the contributions I have made: the vaccine score, algorithms for
optimization of the score and vaccine design comparisons. Section 5.2 contains experimental
resultscomparingthevaccinedesignedusingtheproposedalgorithmsandaconsensusvaccine
in ELISPOT experiments, which test whether peptides are indeed epitopes for a population of
patients. Chapter 6 discusses modifications to the methods aimed at overcoming some of the
additionalchallengestovaccinedesign.