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A Predictive Model of Antibody Binding in the Presence of Igg-Interacting Bacterial Surface Proteins

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A Predictive Model of Antibody Binding in the Presence of Igg-Interacting Bacterial Surface Proteins bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347781; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. A predictive model of antibody binding in the presence of IgG-interacting bacterial surface proteins Vibha Kumra Ahnlide1, Therese de Neergaard1, Martin Sundwall1, Tobias Ambjörnsson2, and Pontus Nordenfelt1 1Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden 2Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden Many bacteria can interfere with how antibodies bind to their surfaces. This bacterial antibody targeting makes it challeng- According to our previous findings (22), the IgGFc-binding ing to predict the immunological function of bacteria-associated of M and M-like proteins is more prominent in low IgG antibodies. The M and M-like proteins of group A streptococci concentration environments, corresponding to that of the exhibit IgGFc-binding regions, which they use to reverse IgG human throat milieu. At the higher IgG concentrations in binding orientation depending on the host environment. Unrav- plasma, IgG is mostly bound to the surface proteins via eling the mechanism behind these binding characteristics may identify conditions under which bound IgG can drive an effi- Fab. This suggests that the antibody binding orientation is cient immune response. Here, we have developed a biophysi- dependent on the IgG concentration. The mechanism for this cal model for describing these complex protein-antibody inter- behaviour is however unclear. actions. We show how the model can be used as a tool for study- ing various IgG samples’ behaviour by performing in silico sim- Streptococcus pyogenes causes more than 700 million ulations and correlating this data with experimental measure- uncomplicated throat and skin infections annually and can ments. Besides its use for mechanistic understanding, this model be at the root of rare but very serious invasive infections could potentially be used as a tool to predict the effect, as well as such as sepsis (4). It is estimated that group A streptococcal aid in the development of antibody treatments. We illustrate this infections lead to more than 0.5 million deaths annually (4). by simulating how IgG binding in serum is altered as specified Monoclonal antibody treatments have shown potential for a amounts of monoclonal or pooled IgG is added. Phagocytosis wide spectrum of disease. Despite this, there are very few experiments link this altered antibody binding to a physiolog- ical function and demonstrate that it is possible to predict the monoclonal antibodies (mAbs) clinically available for treat- effect of an IgG treatment with our model. Our study gives a ment of bacterial infections (20) (19). Furthermore, pooled mechanistic understanding of bacterial antibody targeting and human IgG is used clinically for treatment of certain invasive provides a tool for predicting the effect of antibody treatments. GAS infections, but studies show no clear effect of this treat- ment (13) (15) (5) (3). A better understanding of the binding antibody binding | antibody interaction modelling | M protein | group A strep- tococci | antibody treatment characteristics of M protein can help identify conditions un- Correspondence: [email protected] der which bound IgG can drive an efficient immune response. While there exist models of antibody binding to antigens that Introduction build on receptor-ligand kinetics and structural data (24) (23) Many bacteria can interfere with how antibodies bind to their (12), there is no model that describes the complex interaction surfaces. Opsonisation, the process in which antibodies tar- of multiple antibody clones with varying affinities and multi- get pathogens through binding to facilitate phagocytosis, is valency. Moreover, no model takes Fc binding into account. essential for the defense against infections. However, several The binding of IgG to M protein in the host environment significant bacterial pathogens express surface proteins that depends on total concentration of IgG, concentration of interfere with this process by binding antibodies outside the the different IgG clones, concentration of bacterial surface antigen-binding region. A common target is the Fragment proteins, number of Fab-binding sites, location of epitopes crystallizable (Fc) region on Immunoglobulin G (IgG) and and binding energies to these locations (Fig. 1a). proteins expressing IgGFc-binding regions include protein G from group C and G streptococci (2), protein A from Here, we have developed a biophysical model for describing Staphylococcus aureus (10) and M and M-like proteins from all of the effects described above. Our implementation of the group A streptococci (GAS) (11) (29) (30). By binding the model is computationally efficient and we provide an easy Fc region on IgG the bacteria can reduce the ability of the to use, publicly available Matlab-based software. Computa- immune response to detect and eliminate the bacteria from tions using our new model shed light on the implications of the host organism through Fc-mediated phagocytosis (9) the IgGFc-binding affinity and demonstrate under which cir- (14). This makes it difficult to predict the immunological cumstances an effective antibody binding is prominent. We effect of bacteria-associated antibodies. show how our model can be used as a tool for studying the Kumra Ahnlide et al. | bioRχiv | October 5, 2020 | 1–14 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347781; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. behaviour of various IgG samples by performing in silico The computational time required for calculating the binding simulations in relation to experimentally measured binding probability curves such as the ones shown in Fig. 1 is less values. We use the model here to study the behaviour of spe- than 10 seconds on a standard laptop computer. Altogether cific Fragment antigen-binding (Fab) and Fc binding, pooled we define a system in which a number of antibodies, charac- IgG and IgG in serum from healthy individual donors. The terised by their site-dependent affinities, compete for binding model is also used to simulate the effect of antibody treat- to different sites via both Fab and Fc. All possible binding ment, by calculating how binding of IgG in serum is altered states are identified and the probability of Fab and Fc binding as specified amounts of monoclonal or pooled IgG is added. can be calculated using the transfer matrix method. Phagocytosis experiments link the altered antibody binding to a physiological function, and demonstrate that it is possi- To test the model, we show experimentally measured ble to predict the effect of an IgG treatment with our model. independent Fab and Fc binding of pooled human IgG with its corresponding theoretical fit calculated with the model Results (Fig. 1c). The affinities extracted from this model fitting were used to predict the competitive Fab and Fc binding Biophysical modeling of competitive antibody binding (Fig. 1c). Binding of pooled IgG to M protein-expressing to M protein. Here we introduce a a statistical-physics-based bacteria has been measured using flow cytometry. The theoretical model for the interaction between antibodies and pooled IgG has been cleaved at the hinge region using the bacterial surface proteins, and show in silico computations IgG-cleaving enzyme IdeS (28), separating the divalent with this model in relation to measured binding curves antibody fragments (F(ab’)2) from the Fc fragments prior to and known affinities. The model describes the binding of being added to the bacterial samples. The measured binding IgG to M protein, taking into consideration the binding is thus non-competitive between the Fab and Fc region. Inde- competition between the Fab and Fc regions as well between pendent fits with the model yield affinity estimates for both different IgG clones. Different IgG binding possibilities Fab and Fc binding and binding curves of the competitive on M protein can be described as statistical weights, binding have been calculated using these. According to this making it possible to derive the probability of binding to calculation, the Fc binding is out-competed at high total IgG occur at a specific site, and whether it is Fab- or Fc-mediated. concentrations, which is in line with published findings (22). We approached the modeling by implementing the com- By performing calculations with the model for monoclonal putationally efficient multi-ligand transfer matrix method antibody fragments with determined binding sites, we show for competitive binding as described by Nilsson et al and that the model predicts binding as expected. To study Fab implemented for dual ligand binding to DNA barcodes (21) and Fc binding independently, IdeS cleaved monoclonal an- (25) (26). M and M-like proteins are fairly straight and rigid tibodies are used. The Fab binding is studied using a specific (18). We therefore model M protein as a one dimensional antibody to M protein (αM, Bahnan et al, manuscript) and a lattice (Fig. 1b). For this linear-protein approximation, M human IgG clone with no specificity to M protein (Xolair) is protein is divided into equally large sites i. Each antibody used for the Fc-binding. is ascribed a size given in a number of sites representing the width it covers on M protein when bound. A polyclonal Binding curve fittings, which are performed with the model IgG sample is characterised by its number of clones, a range to values measured by flow cytometry as input data, yield of affinities to specific sites and the proportion of the bulk affinity estimates that are verified with an ideal binding curve concentration of each clone.
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