Molecular modelling of antibody combining sites

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Molecular recognition studies by nanoESI mass spectrometry

Analytical methods that characterize antibody-antigen interactions are certainly worth developing as they illuminate our understanding of molecular mechanisms of certain disease processes, and ultimately provide opportunities to improve existing treatment and management of such diseases by designing novel therapeutics. In this thesis, the challenges associated with the existing methods have been surpassed by developing simple but accurate nanoESI mass spectrometry methods which can be used to characterize antibody specificities as well as estimation of their binding affinities

A New Definition And Classification Of Antibody Complementarity Determining Regions: Unsupervised Learning Of Protein Backbone Conformations Informs Antibody Structural Bioinformatics And Design

One of the main challenges in modern molecular biology is to establish general, robust, and precise descriptions of the relationship between structural features of molecules (DNA, RNA, proteins, and glycans) and the sequence of their constituent chemical building blocks (nucleotides, amino acids, monosachharides). In his 1951 Nobel lecture, Linus Pauling predicted that chemistry of the future would rely upon these descriptions to solve problems in biological medicine relevant to human health. As of July 8, 2021, X-ray crystallography, NMR, and Cryo-EM have solved 179,842 molecular structures, which have been deposited in the Protein Data Bank (PDB) along with their associated sequences. Antibodies are the largest such family of deposited protein structures in the PDB, and their importance to human health and research in molecular biology is widely acknowledged. In this work, I first show the development and validation of unsupervised learning software to cluster protein backbone conformations (clustering of backbones for Ramachandran analysis, or COBRA). I then describe the application of this software to the wealth of antibody data in the PDB to provide a novel, electron density validated classification of the antibody complementarity determining regions (CDRs). I compare this new classification to previous classifications of the CDRs to show the improvement of the association between the sequences and structures of the CDRs, the ability to robustly separate various CDR families, and the ability to assess the confidence in the quality of CDR families using electron density as support. In addition to providing a new classification of the antibody CDRs by clustering their backbone conformations, I provide an expanded definition of the antibody binding region by defining, naming, and classifying an antibody V-region segment named the “DE loop”, which resembles the other six CDRs in sequence and structural variability, ability to bind antigen, and ability to stabilize antibodies, but has no current recognition as a canonical member of the CDRs. Finally, I show examples implementing these analyses in RosettaAntibodyDesign (RAbD) software to design antibodies towards SARS-COV-2 Spike Protein Type 1 (S1) Receptor Binding Domain (RBD), and show the experimental data for the generated antibody designs

The Role Of Antibody Subclass In The Pathogenesis Of Pemphigus Vulgaris

A marvel of evolution, the adaptive immune system has the capacity to respond to almost any foreign antigen in a highly specific manner. Antibodies, Y-shaped glycoproteins containing both diverse variable regions responsible for antigen binding and constant regions responsible for effector function, are a key part of this capacity. However, this vast diversity comes with several drawbacks, one of which is the fact that the immune system can deleteriously respond to self-antigens. The focus of this thesis is to characterize the role of class-switching (the changing of antibody constant regions) in the pathogenesis of autoimmune disease, and in particular to trace the lineage of antigen-specific autoreactive B cells by analyzing clonal relationships between antibodies of different constant regions. Analyzing such lineages has the potential to shed light on mechanisms of autoantibody-mediated disease pathogenesis, leading to better understanding of autoimmunity and better therapeutics. The work presented in this thesis focuses on pemphigus vulgaris, or PV, a model antibody-mediated autoimmune disease characterized by a response to the cell adhesion protein desmoglein (Dsg) 3, which holds keratinocytes together in the epidermis. An enigmatic feature of this disease is the predominance of antibodies from the IgG4 subclass during active disease, which ordinarily appears to have few effector functions and may serve as a “brake” on the immune system in the setting of continuous stimulation by antigen. PV patients also display autoantibodies of the IgG1 subclass during disease and remission, but the relationship between IgG1 and IgG4 in the disease in unclear. Because the majority of cases of PV also harbor anti-Dsg antibodies of the IgA1 and IgA2 subclasses, we sought to determine the relationships between autoantibodies belonging to each of these subclasses. First, we address whether the same anti-Dsg variable region, grafted onto either IgG1 or IgG4 constant regions, can show differing affinity or pathogenicity, in order to determine whether antibody subclass is directly modulating pathogenic effect (chapter 2). Finding that the subclass has very little effect on antibody affinity, pathogenicity, or epitope preference, we then sought to determine whether B cells expressing autoantibodies of different subclasses share lineages, indicating common pathways of development (chapter 3). Using a combination of antigen-specific antibody cloning through phage display, and next-generation sequencing of subclass specific repertoires in a panel of PV patients, we managed to trace 80 lineages of anti-Dsg B cells across all four subclasses tested. In particular, we found that anti-Dsg IgG4 B cells, which are believed to be central to disease pathogenesis, tended to not share lineages with other subclasses, and in in general do not appear to share a precursor-product relationship with anti-Dsg IgG1 B cells. We have also found that anti-Dsg IgA1 and IgA2 were tightly related and often arose directly from IgG precursors. These findings are key to understanding the role of class-switching in the pathogenesis of PV, and may shed light on the class-switch mechanisms driving other autoimmune diseases and states of chronic antigen stimulation

Immunogenetics

This open access book explores techniques for working in the field of immunogenetics, i.e. fundamental and translational research into the adaptive immune receptor repertoire. Many chapters are dedicated to lab protocols, bioinformatics, and immunoinformatics analysis of high-resolution immunome analysis, exemplified by numerous applications. Additionally, the newest technological variations on these protocols are discussed, including non-amplicon, single-cell, and cell-free strategies. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Immunogenetics: Methods and Protocols covers a broad spectrum of methodologies for applications in research and clinical diagnostics to illustrate the impact that immunogenetics has achieved and will further expand in all fields of medicine, from infection and (auto)immunity, to vaccination, to lymphoid malignancy and tumor immunity

Increasing the breadth and potency of a neutralising single domain antibody against the influenza haemagglutinin stem

Among influenza A group 1 viruses, those with the haemagglutinin (HA) subtypes H1, H2, H5 and H9 are considered to pose the greatest pandemic risk. The alpaca-derived single domain antibody R1a-B6 binds to a conserved epitope on the influenza HA stem, resulting in neutralisation by membrane fusion inhibition. R1a-B6 has demonstrated in vivo efficacy against H1 and H5 viruses but lacks potency against H2 and H9 viruses, tested in vitro. We created a rationally designed library of millions of R1a-B6 variants displayed on the surface of yeast by incorporating amino acid variation from clonally related antibodies. These cells were sorted by flow cytometry in successive rounds according to their binding to H9 HA, resulting in enrichment of high-affinity H9 binding variants. R1a-B6 variant WC21 demonstrated dramatic improvements to affinity, ELISA EC50 and pseudovirus neutralisation titre against H9 and H2 antigens and maintained effectiveness against H1 and H5 antigens. The improvements were mediated by one CDR1 substitution (R31I) and two CDR3 substitutions (D95N and Y102S). R1a-B6 single substitution variants R31I, D95N and Y102S each slowed dissociation from H2 and H9 antigens and these three substitutions in combination demonstrated the same affinity improvements as WC21. We used saturation mutagenesis and deep mutational scanning to identify HA substitutions which led to loss of antibody binding. We demonstrated that WC21 has a more compact epitope footprint than R1a-B6 and speculate that this may represent a higher genetic barrier for viral escape from WC21. Furthermore, this experiment highlighted an H2-specific HA polymorphism, HA2 I45F, which abrogated R1a-B6 binding but not WC21 binding. Overcoming the barrier to binding posed by HA2 I45F is suggested to be a primary reason for the dramatic increase in H2 potency. Our approach informs strategies which may be broadly applicable to the molecular evolution of monoclonal antibodies against antigenically variable pathogens

In vitro expanded human CD4+CD25+ regulatory T cells suppress effector T cell proliferation.

Regulatory T cells (Tregs) have been shown to be critical in the balance between autoimmunity and tolerance and have been implicated in several human autoimmune diseases. However, the small number of Tregs in peripheral blood limits their therapeutic potential. Therefore, we developed a protocol that would allow for the expansion of Tregs while retaining their suppressive activity. We isolated CD4+CD25 hi cells from human peripheral blood and expanded them in vitro in the presence of anti-CD3 and anti-CD28 magnetic Xcyte Dynabeads and high concentrations of exogenous Interleukin (IL)-2. Tregs were effectively expanded up to 200-fold while maintaining surface expression of CD25 and other markers of Tregs: CD62L, HLA-DR, CCR6, and FOXP3. The expanded Tregs suppressed proliferation and cytokine secretion of responder PBMCs in co-cultures stimulated with anti-CD3 or alloantigen. Treg expansion is a critical first step before consideration of Tregs as a therapeutic intervention in patients with autoimmune or graft-versus-host disease

Antibody loop modeling methods and applications

This thesis describes improvements to our protein loop structure prediction algorithm and use of this algorithm to inform a computational investigation of anti-HIV antibodies. First, in Section I, we outline improvements to the Protein Local Optimization Program ("Plop") that allow us to reliably restore long loops containing secondary structure elements, and predict nativelike conformations for loops whose surroundings deviate from the native crystal structure context. Shifting to focus exclusively on antibody hypervariable loop prediction, we also benchmark our results in the community-wide Second Antibody Modeling Assessment. Plop can now be reliably deployed as a tool for understanding important biological systems. In Section II, we start from a system of interest - broadly neutralizing antibodies against HIV-1 - with the long-term goal of computationally identifying more potent VRC01-class anti-HIV antibodies. We show proof of concept results for predicting relative binding affinity upon mutation using free energy perturbation (FEP) simulations for the VRC01 antibody binding to glycoprotein gp120. Using the protocols developed in Section I, we provide case studies for using Plop to understand key FEP results and guide future experiments

Molecular display of synthetic oligonucleotide libraries and their analysis with high throughput DNA sequencing

Thesis (Ph. D. in Biomedical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 142-151).High throughput methods in molecular biology have changed the landscape of biomedical research. In particular, advances in massively parallel DNA sequencing and synthesis technologies are defining our genomes and the products they encode. In the first part of this thesis, we have constructed a rationally designed antibody library and analysis platform optimized for use with deep sequencing technologies. Libraries of fully defined oligonucleotides encode three complementarity determining regions (CDRs; L3 from the light chain, H2 and H3 from the heavy chain), and were combinatorially cloned into a synthetic single chain variable fragment (scFv) framework for molecular display. Our novel CDR sequence design utilized a hidden Markov model (HMM) that was trained on all antibody-antigen co-crystal complexes present in the Protein Data Bank. The resultant ~10¹² member library has been produced in ribosome display format, and was comprehensively analyzed over four rounds of antigen selections by multiplex paired-end Illumina sequencing. The HMM library generated multiple antibodies against an emerging cancer antigen and is the basis of a next generation antibody production platform. In a second application of these technologies, we have created a synthetic representation of the complete human proteome, which has been engineered for display on bacteriophage. We use this library together with deep DNA sequencing methods to profile the autoantibody repertoires of individuals with autoimmune disease in a procedure called phage immunoprecipitation sequencing (PhIP-Seq). In a proof-of-concept study, this method identified both known and novel autoantibodies contained in the spinal fluid of a control patient with paraneoplastic neurological syndrome. The study was then expanded to include a large scale automated screen of 289 independent antibody repertoires, including those from a large number of healthy donors, multiple sclerosis patients, rheumatoid arthritis patients, and type 1 diabetics. Our data describes each individual's unique "autoantibodyome", and defines a small set of recurrently targeted peptides in health and disease.by Harry Benjamin Larman.Ph.D.in Biomedical Engineerin