AB-Bind: Antibody binding mutational database for computational affinity predictions.

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 (ΔΔG) 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 ΔΔG values were low (r = 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 |ΔΔG| > 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 ΔΔG < -1.0 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.

Physics-based enzyme design: predicting binding affinity and catalytic activity.

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.

Mechanistic and computational studies of the reductive half-reaction of tyrosine to phenylalanine active site variants of D-arginine dehydrogenase.

The flavin-mediated enzymatic oxidation of a CN bond in amino acids can occur through hydride transfer, carbanion, or polar nucleophilic mechanisms. Previous results with D-arginine dehydrogenase from Pseudomonas aeruginosa (PaDADH) using multiple deuterium kinetic isotope effects (KIEs) and computational studies established preferred binding of the substrate protonated on the α-amino group, with cleavages of the NH and CH bonds occurring in asynchronous fashion, consistent with the three possible mechanisms. The hydroxyl groups of Y53 and Y249 are ≤4 Å from the imino and carboxylate groups of the reaction product iminoarginine, suggesting participation in binding and catalysis. In this study, we have investigated the reductive half-reactions of the Y53F and Y249F variants of PaDADH using substrate and solvent deuterium KIEs, solvent viscosity and pH effects, and quantum mechanical/molecular mechanical computational approaches to gain insights into the catalytic roles of the tyrosines and evaluate whether their mutations affect the transition state for substrate oxidation. Both Y53F and Y249F enzymes oxidized D-arginine with steady-state kinetic parameters similar to those of the wild-type enzyme. Rate constants for flavin reduction (k(red)) with D-leucine, a slow substrate amenable to rapid kinetics, were 3-fold smaller than the wild-type value with similar pKa values for an unprotonated group of ∼10.0. Similar pKa values were observed for (app)Kd in the variant and wild-type enzymes. However, cleavage of the substrate NH and CH bonds in the enzyme variants occurred in synchronous fashion, as suggested by multiple deuterium KIEs on k(red). These data can be reconciled with a hydride transfer mechanism, but not with carbanion and polar nucleophilic mechanisms.

A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring.

Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective ω-aminotransferase (ω-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several ω-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.