Below are several antibody papers that should be of interest to those dealing with antibody engineering, be it computational or experimental. The running motif in this post will be humanization, or the process of engineering a mouse antibody sequence which binds to a target to look ‘more human’ so as to reduce the immune response (if you need an early citation on this issue, here it is).

We present two papers which talk about antibody humanization directly, one from structural point of view (Choi et al. 2015), the other one highlighting issues facing antibody engineers  mining for information (Martin & Rees, 2016). The third paper (Collins et al. 2015) takes a step back from the issues presented in the other papers and talks broadly about the nature of mouse sequences raised in the lab.

Humanization via structural means [here] (Bailey-Kellogg group). The authors introduce a novel methodology named CoDAH to facilitate humanization of antibodies. They design an approach which makes a tradeoff between sequence and structural humanization scores. The sequence score used is the Human String Content (Laza et al. 2007, Mol Immunol), which calculates how similar the query (murine) sequence is to short stretches of human sequences (mostly germilne). In line with the fact that T-Cells are one of main drivers of anti-biologics immunity, they define the sequences stretches to be 9-mer, as recognized by T-Cells. For the structural score, they use Rotameric energy as calculated by Amber. They demonstrate that constructs designed using their score express and retain affinity towards the target antigen, however they do not appear to prove that the new sequences are not immunogenic.

Extracting data from databases for humanization [here] (Martin group and Rees consulting). The main purpose of this manuscript is to warn potential antibody engineers of the pitfalls of species mis-annotations. They point out that in a routine ‘humanization’ pipeline where we aim to find human sequences given a mouse sequence, a great number of seemingly good ‘human’ templates are not human at all (sources as diverse as IMGT or PDB). This might lead to errors down the line if the engineer does not double check the annotations (unfortunate but true). Many of such annotations arise because the cells in which mouse antibodies are expressed are human cells or because the sequences are chimeric — in either case the annotation would not read mouse or chimeric, but erroneously ‘human’. NB. Another thing to watch in this publication is the fact that authors are working on a sequence database of their own: EMBLIG which is said to collect data from EMBL-ENA (nucleotide repository from EMBL). Hopefully in their database, authors will address the issues that they point out here.

What can we say about antibodies produces by laboratory mice? [here] (Collins group). Authors of this manuscript have addressed the issue that the now available High Throughput Sequencing (HTS) overlooked mouse repertoires. Different mice strains have different susceptibilities to diseases (Houpt, 2002, J Imunol; which might mean that you need to think twice which mice strain to choose for a given target). Currently known antibody repertoire of mice is based on the sequencing of two strains, BALB/c and C57BL/6. Here the authors apply HTS to two strains (BALB/c and C57BL/6) of laboratory mice (eight mice per strain) to get a better snapshot of antibody gene usage. Specifically, they pay close attention to the different genes combinations (VDJ) in the sequences that they obtain. Authors conclude that the repertoires between the two strains are strikingly different and quite restricted — which might mean that the laboratory mice were under very specific pressures (read inbred/overbred). All in all, the VDJ usage numbers that they produce in this publication are a useful reference to know which sequence combinations might be used by antibody engineers.

De Novo H3 prediction by C-terminal kink-biasing (Gray Lab) [here].

Authors introduce an improvement to the prediction of CDR-H3 in the form of a constraint for de-novo decoy generation. Working from the observation that 80% of CDR-H3 have kinked C-Terminal (Weitzner et al., 2015, Structure), they bias the loops to assume this conformation (they prove that it does not force ALL loops to do so!). The constraint is in the form of a pseudo bond angle between Ca for the three C-terminal residues and a pseudo dihedral angle for the three C-terminal residues and one adjacent residue in the framework. The bias takes the form of a penalty score if the generated angle falls outside mean +/- 1s. They use a quite stringent H3 loop benchmark of only 49 loops. Using this constraint on this dataset improves prediction for majority of the loops. They also demonstrate the utility of the score for full Fv homology modeling and Ab-Ag docking.

Therapeutic vs synthetic vs natural antibodies (Ofran Lab) [here].

The authors analyzed 137 Ab-Ag complexes from the PDB. Those from hybridoma and synthetic libraries were classified as ‘Natural’ and those coming from ‘synthetic’ libraries. They demonstrate that synthetic libraries overuse H3 in the number of contacts the antibody forms with the antigen, whereas natural constructs share the paratope with H1& H2 to a larger extent. This, together with their tool, CDRs analyzer (analysis of structural & biochemical properties of ab-ag complex) can be a useful method to inform the design of antibodies.

From the past: TABHU, tools for antibody humanization (Tramontano Lab) [here]. Authors have created a tool to aid antibody humanization. Given a sequence of an antibody, the system would look for the most suitable template from their extensive sequence databases (DIGIT) and germline sequences from IMGT. The templates are assessed on sequence similarity to the query and the similarity of the ‘binding’ mode which is assessed by their paratope prediction tool proABC. After the template had been chosen, the user can produce a structural model of the sequence.