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A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods. 

The 2014 Ebola virus (EBOV) outbreak in West Africa is the largest in recorded history and resulted in over 11,000 deaths. It is essential that strategies for treatment and containment be developed to avoid future epidemics of this magnitude. With the development of vaccines and antibody-based therapies using the envelope glycoprotein (GP) of the 1976 Mayinga strain, one important strategy is to anticipate how the evolution of EBOV might compromise these efforts. In this study we have initiated a watch list of potential antibody escape mutations of EBOV by modeling interactions between GP and the antibody KZ52. The watch list was generated using molecular modeling to estimate stability changes due to mutation. Every possible mutation of GP was considered and the list was generated from those that are predicted to disrupt GP-KZ52 binding but not to disrupt the ability of GP to fold and to form trimers. The resulting watch list contains 34 mutations (one of which has already been seen in humans) at six sites in the GP2 subunit. Should mutations from the watch list appear and spread during an epidemic, it warrants attention as these mutations may reflect an evolutionary response from the virus that could reduce the effectiveness of interventions such as vaccination. However, this watch list is incomplete and emphasizes the need for more experimental structures of EBOV interacting with antibodies in order to expand the watch list to other epitopes. We hope that this work provokes experimental research on evolutionary escape in both Ebola and other viral pathogens.