Assessing Antibody Neutralization of HIV-1 as an Initial Step in the

109 Pages·2009·20.79 MB·English

by  Fabian Müller

Saarland University
Center for Bioinformatics
Master’s Program in Bioinformatics
Master’s Thesis
Assessing Antibody Neutralization of
HIV-1 as an Initial Step in the Search for
gp160-based Immunogens
submitted by
Fabian Müller
on July 28, 2009
Supervisor:
Prof. Dr. Thomas Lengauer, Ph.D.
Advisor:
André Altmann
Reviewers:
Prof. Dr. Thomas Lengauer, Ph.D.
Prof. Dr. Hans-Peter Lenhof

Statement
I hereby confirm that this thesis is my own work and that I have documented all sources
used.
Saarbrücken, July 28, 2009

Abstract
Despite more than 25 years of research since the identification of
the human immunodeficiency virus (HIV) as the causative agent
fortheacquiredimmunedeficiencysyndrome(AIDS)aremedyfor
the worldwide pandemic remains elusive. Vaccination approaches
represent major breakthroughs in gaining control over other dis-
eases like smallpox, polio and measles. However, so far, the ex-
traordinary challenges posed by HIV have impeded the empirical
discovery of an AIDS vaccine.
A novel approach for finding variants of the HIV envelope glyco-
protein (gp160) that are capable of eliciting a broad and potent
antibody response relies on bioinformatics to provide initial im-
munogen candidates. In this work, we show that random forest
and linear support vector machine models are capable of accu-
rately assessing antibody neutralization of envelope glycoprotein
variants. These models are inferred from input data comprising
gp160sequencesandcorrespondingneutralizationdataforthean-
tibodies b12, 2F5 and 4E10. Moreover, importances of the input
features are derived which allow for the identification of gp160 se-
quence traits that are potential determinants of antibody neutral-
ization. High importances are assigned to variables corresponding
to residues in structural proximity to known antibody epitopes.
Inadditiontostatisticalevaluationmethods,weproposesequence
mutations that lead to a predicted change in neutralization based
on model knowledge. Experimental confirmation by neutraliza-
tion assays would represent strong evidence for the validity of our
models.
In future steps, knowledge drawn from our statistical models will
provide the basis for proposing potential HIV immunogen candi-
dates that will be subject to further evaluation in the laboratory.
v

Acknowledgements
There are many people from whose support this thesis has benefited greatly and who
deserve special acknowledgement here.
First of all, I would like to thank Thomas Lengauer for introducing me to the field of
statistical learning and giving me the opportunity to work on this extraordinary project.
Hislectureshavealwaysbeendelightfulandencouragedmetocontinuemybioinformatics
studies along this path.
Another person, who deserves my special thanks is Hans-Peter Lenhof, my second
reviewer. His guidance and advice throughout my studies have been invaluable.
The “main criminals” responsible for getting me started on this project are André
Altmann, Francisco Domingues and Oliver Sander. André, Francisco and – during the
initial phase – also Oliver continuously provided encouraging remarks, excellent ideas
and demanding challenges. Especially André’s experience in statistical learning and
Francisco’s broad biological knowledge have been vital foundations of this work. Thank
you for the fruitful meetings and always being receptive to suggestions and problems! It
has been a pleasure to work on this project with you!
André deserves special recognition for enthusing me about statistical learning in the
context of fighting HIV. After having such an excellent advisor as him for my bachelor’s
thesis, I was not able to let go of this exciting topic.
Klaus Überla and his group initiated the vaccine development project. The bioin-
formatics approach described in this thesis came into existence as a means to support
their experimental approach for developing and optimizing HIV immunogens. They will
also conduct the experiments for validation of the statistical models. Moreover, Klaus’s
remarks and suggestions maintained a constant link of theory to practical virology.
Johannes Seibert, Daniel Stöckel and Markus Rabe proofread my thesis. Thank you!
It means a lot to me!
I would also like to thank the entire Computational Biology and Applied Algorith-
mics Department at Max Planck Institute for Informatics for inspiring conversations and
interesting group seminars.
Furthermore, the Saarbrücken Graduate School of Computer Science deserves recog-
nition for financial and equipment support throughout my work.
My fellow students in the office provided helpful tips, a pleasurable atmosphere, joyful
coffee breaks and seemingly infinite supply of candy and humor. Thanks a lot for that!
Finally, I owe deep gratitude to my friends and family, especially my parents, my
sister and my grandparents. My studies would not have been possible without their
incomparable love and exceptional support.
vii

Contents
List of Figures xii
List of Tables xiii
1 Introduction 1
1.1 Human Immunodeficiency Virus (HIV) . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 HIV-1 Envelope Glycoproteins . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Cell Entry and Replication Cycle . . . . . . . . . . . . . . . . . . . 6
1.1.4 Pathogenesis, AIDS and Treatment . . . . . . . . . . . . . . . . . . 7
1.2 Vaccination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.1 Principles of Vaccination . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.2 Vaccination Challenges Posed by HIV . . . . . . . . . . . . . . . . 10
1.2.3 Previous HIV Vaccination Trials . . . . . . . . . . . . . . . . . . . 10
1.2.4 The (Broadly) Neutralizing Antibody Problem . . . . . . . . . . . 11
1.3 ABioinformaticsGuidedApproachforSearchingandEvaluatingPotential
HIV-1 Immunogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Materials and Methods 15
2.1 Data Acquisition and Preparation . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.1 Neutralization Data . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.2 Sequence Data and Alignments . . . . . . . . . . . . . . . . . . . . 19
2.2 Feature Encodings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.1 Alignment Position Variables . . . . . . . . . . . . . . . . . . . . . 24
2.2.2 Clade Membership . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.3 Generic Variable Loop Features . . . . . . . . . . . . . . . . . . . . 24
2.2.4 N-linked Glycosylation . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Statistical Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 Random Forests (RFs) . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.2 Support Vector Machines (SVMs) . . . . . . . . . . . . . . . . . . . 28
2.3.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 Statistical Importance Measures . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.1 Statistical Learning Model Derived Importance Measures . . . . . 31
2.4.2 Univariate Importance Measures . . . . . . . . . . . . . . . . . . . 32
2.4.3 P-Values Correction of Importance (PIMP) . . . . . . . . . . . . . 34
2.5 Correlation and Similarity Coefficients . . . . . . . . . . . . . . . . . . . . 34
2.6 Proposing Sequences for Additional Neutralization Tests . . . . . . . . . . 37
2.7 Applied Software Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
ix
3 Results and Discussion 39
3.1 Predicting Antibody Neutralization . . . . . . . . . . . . . . . . . . . . . . 39
3.1.1 Predictingb12NeutralizationBasedonAlignmentPositionVariables 39
3.1.2 Substitution Correlation Analysis . . . . . . . . . . . . . . . . . . . 55
3.1.3 Clade Prediction Analysis . . . . . . . . . . . . . . . . . . . . . . . 57
3.1.4 Variable Loop Features. . . . . . . . . . . . . . . . . . . . . . . . . 59
3.1.5 N-Linked Glycosylation Variables . . . . . . . . . . . . . . . . . . . 61
3.1.6 Predicting Antibody Neutralization Targeted at gp41 . . . . . . . . 63
3.1.7 Prediction Beyond the Training Set . . . . . . . . . . . . . . . . . . 66
3.2 Proposing Sequences for Additional Neutralization Tests . . . . . . . . . . 67
3.2.1 Predicted Changes in Antibody Neutralization . . . . . . . . . . . 67
3.2.2 Proposed Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4 Summary and Outlook 73
A Tables 75
A.1 Amino Acid Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
A.2 The PubSeqs Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
A.3 Alignment Position Mapping and Annotation . . . . . . . . . . . . . . . . 78
A.4 Predictive Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
A.5 Bochum Base Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
B Variable Importances 81
B.1 SVMWeightsandZ-scoresforBinaryPositionVariablesinPredictingb12
Neutralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
C Glossary 85
List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
References 91
x