Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPBSA and MMGBSA.

Abstract

Accurate and efficient in silico ranking of protein-protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with few ([≤]4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5-10 replicas of short (4 ns) MD simulations may be sufficient for reproducible and accurate ranking of TCR variants. The protocols developed here can be applied towards in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.