Here we highlight two antibody papers, one from the past one more recent. The more recent one talks about developing an affinity maturation model. The older one is a refresher on the Developability Index — how to computationally harness hydrophobicity and accessible surface areas to predict aggregation.

Mouse antibody maturation model — the most expanded (common) clones might not be the ones with highest affinities here (van Kampen lab). The authors of the paper define a model of affinity maturation. The main take-home message of the paper is that the ‘most expanded’ clones might not be the ones with highest affinity — expanded clones are assumed to be the ones ‘responding’ to the antigenic challenge. The model is based on Ordinary Differential Equations, tracing cell fate in a germinal center. The model was compared to experimental expansion data from lymph nodes for accuracy. In each such model one needs to assume a lot of parameters, such as which day post-immunization do we start somatic hypermuatation? The paper is a very nice example of a model of maturation and a good starting point for tracing references citing germinal center biology and numbers for parameters used for models (also the general canon of construction of such models!).

Developability index here. (Trout lab at MIT). The authors touch on a very important subject of antibody developability: after you produced your ab binder, does it have physicochemical characteristics which are suitable to carry on with it as a therapeutic. Such characteristics include stability, expression yields and aggregation propensity. Aggregation propensity is one of the most important factors here as it affects the pharmacokinetics of the drug as well as shelf life. In this manuscript, authors address attempt to predict the aggregation propensity of antibodies. As background data, they use twelve antibodies whose long term stability has been measured over several years. To develop a computational method to predict antibody aggregation propensity, they use a score which combines hydrophobicity and electrostatic factors. The hydrophobicity is an adapted SAP score which the authors developed previously, and whose main parameters are the exposed residue area and hydrophobicity of the residue as defined by Black and Mould. The electrostatics are calculated using PROPKA. Since combining the scores into a predictive model involved parametrization, they use seven of the antibodies to adjust the coefficients. They use the rest to demonstrate that their model has predictive power. Calculation of their models requires a structure of an antibody which they obtain using WAM. Take home messages? It is a nice dataset to play with aggregation prediction and it demonstrates how to calculate electrostatics and hydrophobicity of a molecule.

This time round, one older paper, one recent paper. The older one talks about estimating how many H3s can there be in a human body based on sequencing of two individuals (they cap it at 9 million — not that much!). The more recent one is an attempt to define what makes a good antibody in terms of its developability properties (a battery of biophys assays on 150 therapeutic antibodies- amazing dataset to work with).

High resolution description of antibody heavy chain repertoires in (two) humans (Koralov lab at NYU). Here. Two individuals were sequenced and their VDJ frequencies measured. It is widely believed that the VDJ recombination events are largely independent and random. Here however they demonstrate some biases/interplay between the D and J regions. Since H3 falls on the VDJ junction, it might suggest that it affects the total choice of H3. Another quite important point is that they compared the productive vs nonproductive sequences (out of frame or with stop codons). If there were significant differences between the VDJ frequencies of productive vs nonproductive sequences, it would suggest selection at the VDJ frequency stage. However they do not see any significant differences here suggesting that VDJ combinations have little bearing on this initial selection step. Finally, they estimate the number of H3 in repertoire. The technique is interesting — they sample 1000 H3s from their set and see how many unique sequences it contributes. Each next sample contributes less and less unique sequences which leads to a log-decay curve. By doing so they get a rough estimate of when there will be no more new sequences added and hence an estimate of diversity (think why do this rather than counting the number of uniques!). They allow themselves to extrapolate this estimate to the whole organism by multiplying their blood sample by the total human body volume — they motivate this extrapolation by the fact that there were precious little overlaps between the two human subjects.

Biophysical landscape of clinical stage antibodies [here]. Paper from Adimab. Designing an antibody which binding its target is only a first step on the way to bring the drug on the market. The molecule needs to fulfill a variety of characteristics such as colloidal stability (does not aggregate or ‘clump up’), does not instantly clear from the organism (which is usually down to off target binding), is stable and can be expressed in reasonable quantities. In an effort to delineate what makes a good antibody, the authors take inspiration from earlier work on small molecules, namely the Lipinski Rules of Five. This set of rules describes what makes a ‘good’ small molecule drug, which was assessed by looking at ~2000 therapeutic drugs. The rules came down to certain numbers of hydrogen bond donors, acceptors, molecular weight & lipophilicity. Therefore, Jain et al would like a similar methodology, but for antibodies: give me an antibody and using methodology/rules we define, we will tell you either to carry on with development or maybe not. Since antibodies are far more complex and the data on therapeutic abs orders of magnitude smaller (around 50 therapeutic abs to date) Jain et al, had to devise a more nuanced approach than simply counting hb donors/acceptors mass etc. The underlying ‘good’ molecule data though is similar: they picked therapeutic antibodies and those in late clinical testing stages (2,3). This resulted in ~150 antibodies. So as to devise the benchmark ‘rules/methodology’, they went for a battery of assays to serve as a benchmark — if your ab raises too many red flags according to these assays, it’s not great (what constitutes a red flag to be defined). These assays were supposed to not be obscure and relatively easy to use as the point was that an arbitrary antibody can be relatively easy checked against them. The assays are a range of expression, cross reactivity, self reactivity, thermal stability etc. To define red flags, they run their therapeutic/clinical antibodies through the tests. To their surprise quite a lot of these molecules turn out to have quite ‘undesirable characteristics’. Following the Lipinski Rules, they define a red flag as being in the bottom 10th percentile of the assay values as evaluated on the therapeutic abs. They show that the antibodies which are approved or in more advanced clinical trials stages have less red flags. Therefore, the take-home messages from this paper: very nice dataset for any computational work, raising red flags does not disqualify you from being a therapeutic.

Hints how broadly neutralizing antibodies arise (paper here). (Haynes lab here) Antibodies can be developed to bind virtually any antigen. There is a stark difference however between the ‘binding’ antibodies and ‘neutralizing’ antibodies. Binding antibodies are those that make contact with the antigen and perhaps flag it for elimination. This is in contrast to neutralizing antibodies, whose binding eliminates the biological activity of the antigen. A special class of such neutralizing antibodies are ‘broad neutralizing antibodies’. These are molecules which are capable of neutralizing multiple strains of the antigen. Such broadly neutralizing antibodies are very important in the fight against highly malleable diseases such as Influenza or HIV.

The process how such antibodies arise is still poorly understood. In the manuscript of Williams et al., they make a link between the memory and plasma B cells of broadly neutralizing antibodies and find their common ancestor. The common ancestor turned out to be auto-reactive, which might suggest that some degree of tolerance is necessary to allow for broadly neutralizing abs (‘hit a lot of targets fatally’). From a more engineering perspective, they create chimeras of the plasma and memory b cells and demonstrate that they are much more powerful in neutralizing HIV.

Ineresting data: their crystal structures are different broadly neutralizing abs co-crystallized with the same antigen (altought small…). Good set for ab-specific docking or epitope prediction — beyond the other case like that in the PDB (lysozyme)! At the time of writing the structures were still on hold in the PDB so watch this space…

Below are two somewhat recent papers that are quite relevant to those doing ab-engineering. The first one takes a look at antibodies as a collection — software which better estimates a diversity of an antibody repertoire. The second one looks at each residue in more detail — it maps the mutational landscape of an entire antibody, showing a possible modulating switch for VL-CL interface.

Estimating the diversity of an antibody repertoire. (Arnaout Lab) paper here. High Throughput Sequencing (or next generation sequencing…) of antibody repertoires allows us to get snapshots of the overall antibody population. Since the antibody population ‘diversity’ is key to their ability to find a binder to virtually any antigen, it is desirable to quantify how ‘diverse’ the sample is as a way to see how broad you need to cast the net. Firstly however, we need to know what we mean by ‘diversity’. One way of looking at it is akin to considering ‘species diversity’, studied extensively in ecology. For example, you estimate the ‘richness’ of species in a sample of 100 rabbits, 10 wolves and 20 sheep. Diversity measures such as Simpson’s index or entropy were used to calculate how biased the diversity is towards one species. Here the sample is quite biased towards rabbits, however if instead we had 10 rabbits, 10 wolves and 10 sheep, the ‘diversity’ would be quite uniform. Back to antibodies: it is desirable to know if a given species of an antibody is more represented than others or if one is very underrepresented. This might indicate healthy vs unhealthy immune system, indicate antibodies carrying out an immune response (when there is more of a type of antibody which is directing the immune response). Problem: in an arbitrary sample of antibody sequences/reads tell me how diverse they are. We should be able to do this by estimating the number of cell clones that gave rise to the antibodies (referred to as clonality). People have been doing this by grouping sequences by CDR3 similarity. For example, sequences with CDR3 identical or more than >95% identity, are treated as the same cell — which is tantamount to being the same ‘species’. However since the number of diverse B cells in a human organism is huge, HTS only provides a sample of these. Therefore some rarer clones might be underrepresented or missing altogether. To address this issue, Arnaout and Kaplinsky developed a methodology called Recon which estimates the antibody sample diversity. It is based on the expectation-maximization algorithm: given a list of species and their numbers, iterate adding parameters until they have a good agreement between the fitted distributions and the given data. They have validated this methodology firstly on the simulated data and then on the DeKosky dataset. The code is available from here subject to their license agreement.

Thorough analysis of the mutational landscape of the entire antibody. [here]. (Germaine Fuh from Affinta/Genentech/Roche). The authors aimed to see how malleable the variable antibody domains are to mutations by introducing all possible modifications at each site in an example antibody. As the subject molecule they have used high-affinity, very stable anti-VEGF antibody G6.31. They argue that this antibody is a good representative of human antibodies (commonly used genes Vh3, Vk1) and that its optimized CDRs might indicate well any beneficial distal mutations. They confirm that the positions most resistant to mutation are the core ones responsible for maintaining the structure of the molecule. Most notably here, they have identified that Kabat L83 position correlates with VL-CL packing. This position is most frequently a phenylalanine and less frequently valine or alanine. This residue is usually spatially close to isoleucine at position LC-106. They have defined two conformations of L83F — in and out:

1. Out: -50<X1-100 interface.
2. In: 50<X1<180

Being in either of these positions correlates with the orientation of LC-106 in the elbow region. This in turn affects how big the VL-CL interface is (large elbow angle=small  tight interface; small elbow angle=large interface). The L83 position often undergoes somatic hypermutation, as does the LC-106 with the most common mutation being valine.

Last week during the group meeting we talked about a pre-print publication from the Ippolito group in Austin TX (here). The authors were monitoring the antibody repertoire from Bone Marrow Plasma Cells (80% of circulating abs) over a period of 6.5 years. For comparison they have monitored another individual over the period of 2.3 years. In a nutshell, the paper is like Picture 1 — just with antibodies

This is what the paper talks about in a nutshell. How the antibody repertoire looks like taken at different timepoints in an individual’s lifetime.

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The main question that they aimed to answer was: ‘Is the Human Antibody repertoire stable over time‘? It is plausible to think that there should be some ‘ground distribution’ of antibodies that are present over time which act as a default safety net. However we know that the antibody makeup can change radically especially when challenged by antigen. Therefore, it is interesting to ask, does the immune repertoire maintain a fairly stable distribution or not?

Firstly, it is necessary to define what we consider a stable distribution of the human antibody repertoire. The antibodies undergo the VDJ recombination as well as Somatic Hypermutation, meaning that the >10^10 estimated antibodies that a human is capable of producing have a very wide possible variation. In this publication the authors mostly focused on addressing this question by looking at how the usage of possible V, D and J genes and their combinations changes over time.

Seven snapshots of the immune repertoire were taken from the individual monitored over 6.5 years and two from the individual monitored over 2.3 years. Looking at the usage of the V, D and J genes over time, it appears that the proportion in each of the seven time points appears quite stable (Pic 2). Authors claim similar result looking at the combinations.  This would suggest that our antibody repertoire is biased to sample ‘similar’ antibodies over time. These frequencies were compared to the individual who was sampled over the period of 2.3 years and it appears that the differences might not be great between the two.

How the frequencies of V, D and J genes change (not) over 6.5 years in a single individual

It is a very interesting study which hints that we (humans) might be sampling the antibodies from a biased distribution — meaning that our bodies might have developed a well-defined safety net which is capable of raising an antibody towards an arbitrary antigen. It is an interesting starting point and to further check this hypothesis, it would be necessary to carry out such a study on multiple individuals (as a minimum to see if there are really no differences between us — which would at the same time hint that the repertoire do not change over time).