Antibodies are an invaluable class of therapeutic molecules — they have the capacity to bind any molecule (Martin and Thornton, 1996), and this comes from an antibody’s remarkable diversity (Georgiou et al., 2014). In particular, antibodies can bind their targets with high affinity, and most drug design campaigns seek to ‘mature’ an antibody, i.e. increase the antibody’s affinity for its target. However, the process of antibody maturation is, in experimental terms, time-consuming and expensive — if we had 6 CDRs (as in a typical antibody), with 10 residues each, and if you can have any of the 20 amino acids in the CDR positions, there are 20^60 mutants to test (and this is before considering any double or triple mutations!)
So hold on, what is affinity exactly? Affinity represents the strength of binding, and it’s calculated as either a ratio of concentrations, or as a ratio of rate constants, i.e.
In the simplest affinity maturation protocol, three steps are compulsory:
- Mutate the antibody’s structure correctly
- Assess the impact of mutation on KD
- Select and repeat.
For the past year, we have centralised around part 2 — affinity prediction. This is a fundamental aspect of the affinity maturation pipeline in order to rationalise ‘good’ and ‘bad’ mutations in the context of maturing an antibody. We developed a statistical potential, CAPTAIN; essentially the idea is to gather contact statistics that are represented in antibody-antigen complexes, and use this information to predict affinities.
But why use contact information? Does it provide anything useful? Based on analyses of the interfaces of antibody-antigen complexes in comparison to general protein-protein interfaces, we definitely see that antibodies rely on a different binding mode compared to general protein-protein complexes, and other studies have confirmed this notion (Ramaraj et al., 2012; Kunik and Ofran, 2013; Krawczyk et al., 2013).
For our methodology, we trained on general protein-protein complexes (as most scoring functions do!) and specifically on antibody-protein complexes from the PDB. For our test set of antibody-protein complexes, we outperformed 16 other published methods, though for our antibody-peptide test set, we were one of the worst performers. We found that other published methods predict antibody-protein affinities poorly, though they make better predictions for antibody-peptide binding affinities. Ultimately, we achieve stronger performance as we use a more appropriate training set (antibody-antigen complexes) for the problem in hand (predicting antibody-antigen affinities). Our correlations were by no means ideal, and we believe that there are other aspects of antibody structures that must be used for improving affinity prediction, such as conformational entropy (Haidar et al., 2013) and VH-VL domain orientation (Dunbar et al., 2013; Fera et al., 2014).
What’s clear though, is that using the right knowledge base is key to improving predictions for solving greater problems like affinity maturation. At present, most scoring functions are trained on general proteins, but this ‘one-fits-all’ approach has been subject to criticism (Ross et al., 2013). Our work supports the idea that scoring functions should be tailored specifically for the problem in hand.