AB ‐Bind: Antibody binding mutational database for computational affinity predictions

Abstract

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (DDG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed DDG values were low (r 5 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |DDG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with DDG < 21.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.Abbreviation and Symbols: DDG, change in free energy of binding; Ab, antibody; mAbs, monoclonal antibodies; Fab, fragment antigen binding; CDR, complementarity determining region; MD, molecular dynamics; KIC, kinematic closure; ROC, receiver operator characteristic; AUC, area under the curve; SPM, single point mutation; SPR, surface plasmon resonance; Yeast Disp. Flow Cyt, yeast surface display analyzed using flow cytometry; ELISA, enzyme-linked immunosorbent assay; phage ELISA, phage display ELISA; KinExA, kinetic exclusion assay; ITC, isothermal titration calorimetry; ASA, accessible surface area; SASA, solvent-accessible surface area; bASA, buried accessible surface area; VdW, van der Waals; CI, confidence interval; D. Studio, Discovery Studio.Additional Supporting Information may be found in the online version of this article.Short Statement: We report a data set of 1101 antibody and antibody-like interface mutations with experimentally determined free energies of binding and at least one experimental structure that enables structure-based modeling. The database, AB-Bind, was used to benchmark computational scoring potentials for their ability to predict observed changes in binding free energies. Although there was a clear signal in tests discriminating mutations that improved/reduced binding, the prediction performance of all methods was modest, indicating a continued need to i...

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“…As has been reported before, individual score functions perform differently on different types of predictions. 63,71 Despite the different prediction methods, these methods appear to be somewhat complementary as there is an increased predictive power when all 3 make the same categorization. This enhancement based upon the agreement of the methods is probably due to a combination of factors, including the identification of residue positions that are easier to classify and those that are not associated with inaccurate biases in any particular method.…”

“…To understand how well these methods performed at this classification, we evaluated all 3 affinity prediction methods and the single stability method on larger data sets of experimentally determined DDG of mutations with corresponding Xray crystal structures. For affinity measurements we utilized AbBind, 63 a large dataset that contains >1000 antibody:antigen interactions. To determine the predictive power of each method, we calculated their performance at correctly identifying affinity reducing mutations (DDG >D1.0 kcal/mol).…”

“…[64][65][66] We generated DDG predictions for the Prime and MacroModel methods on this data set and utilized predictions using the Discovery Studio method previously determined. 63 The results of these DDG predictions are shown in Supplementary Table 6. We analyzed these predictions for how well each method could identify an affinity reducing mutation, and whether the combination of methods added to the predictive performance.…”

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