Experimentally guided computational antibody affinity maturation with de novo docking modelling and rational design.

Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed. Author summary The role of computational techniques in therapeutic protein development is multifaceted and includes structure prediction (homology modelling), interface identification (docking), and mutational energy change calculation. Success has been reported in the areas of protein structure prediction and interface prediction (see competition results such as Critical Assessment of Structure Prediction [CASP] and Critical Assessment of Predicted Interactions [CAPRI]), but perhaps one of the greatest challenges is the translation of in silico derived binding energy changes upon mutation into affinity matured antibody variants. In these applications, it is important to choose the correct structural models, or approximations, that make sense across all aspects of in silico protein design. The challenges are compounded when no antibody-antigen co-crystal structure is available and there is a high degree of uncertainty around the protein-protein interface. Although the field is arguably far from its goal of precisely correlating computational predictions with experimental data, we show that even in the absence of a co-crystal structure, it is possible to identify modest affinity-improving mutations by using in silico mutagenesis in combination with homology modelling, protein docking, and simple experimental checkpoints.