Computational analysis of off-rate selection experiments to optimize affinity maturation by directed evolution.

Directed evolution is a powerful approach for isolating high-affinity binders from complex libraries. In affinity maturation experiments, binders with the highest affinities in the library are typically isolated through selections for decreased off rate using a suitable selection platform (e.g. phage display or ribosome display). In such experiments, the library is initially exposed to biotinylated antigen and the binding reaction is allowed to proceed. A large excess of unbiotinylated antigen is then added as a competitor to capture the vast majority of rapidly dissociating molecules; the slowly dissociating library members can subsequently be rescued by capturing the biotin-carrying complexes. To optimize the parameters for such affinity maturation experiments, we performed both deterministic and stochastic simulations of off-rate selection experiments using different input libraries. Our results suggest that the most critical parameters for achieving the lowest off rates after selection are the ratio of competitor antigen to selectable antigen and the selection time. Furthermore, the selection time has an optimum that depends on the experimental setup and the nature of the library. Notably, if selections are carried out for times much longer than the optimum, equilibrium is reached and the selection pressure is weakened or lost. Comparison of different selection strategies revealed that sequential selection rounds with lower stringency are favored over high-stringency selection experiments due to enhanced diversity in the selected pools. Such simulations may be helpful in optimizing affinity maturation strategies and off-rate selection experiments.