Adam J. T. Smith

Beyond Picomolar Affinities: Quantitative Aspects of Noncovalent and Covalent Binding of Drugs to Proteins

More than half a century ago, Linus Pauling wrote: “enzymes are molecules that are complementary in structure to the activated complexes of the reactions that they catalyze, ···, [rather than] entering into reactions.”1 This model has had a profound impact on drug design. Structure-based drug design usually involves the conceptualization and synthesis of molecules that have shapes and binding surfaces that are highly complementary to a protein receptor or enzyme binding site.2, 3 The goal is to achieve high binding affinity and selectivity. A drug must also have appropriate ADMET properties, but affinity is the first step. Affinity and selectivity are generally improved by ensuring more perfect geometric and noncovalent interactions with a binding site. Crystal structures of a protein-ligand complex suggest structural modifications to better occupy a hydrophobic pocket. Such modifications can improve potency from the millimolar to the nanomolar range,4 and have helped lead to clinically approved compounds such as the HIV protease inhibitor nelfinavir.5, 6 Kuntz et al. have shown that small molecule affinity for protein binding sites resulting from noncovalent interactions generally peaks at 10 picomolar (10−11 M), corresponding to ΔGbinding of 15 kcal/mol.7 In contrast, binding constants for enzymes with transition states correspond to average Δ Gbinding of 22 kcal/mol, and up to 38 kcal/mol—many orders of magnitude stronger than can be attributed to noncovalent factors alone.8, 9 Earlier studies in our group have shown how these and other data point to the generality of covalent and partial covalent bonding in transition states of enzyme-catalyzed reactions.8, 9 This strength of binding may be achieved by fully covalent bonding such as Schiff base or acylenzyme intermediate formation, but partial covalent bonds that take place in general acid/base catalysis and interactions with metal cofactors can partially share electrons with the substrate or other reactants such as water molecules in the transition state.9 The harnessing of such strong covalent interactions could help provide the high potency that is needed at early stages of drug development. There has been a tendency to avoid covalent drugs, going back to studies in the early 1970s demonstrating hepatotoxicity as a result of covalent binding of compounds such as [14C]bromobenzene and acetaminophen.10, 11 At the same time, however, there are many examples of highly successful covalently acting drugs on the market, from proton pump inhibitors omeprazole and related compounds,12, 13 to the entire class of β-lactam antibiotics.14 The toxicity attributed to covalent binding is not an inherent feature of these interactions, per se, but rather a result of the specific spectrum of off-target modifications that may be made by the drug or even by metabolites of drugs whose primary mechanism of action is noncovalent.15, 16 Advances in chemical biology, along with bioinformatics data analysis methods, are increasingly able to unravel which covalent modifications are tolerable and which are toxic, suggesting a reevaluation of the role of covalent binding in drugs and drug leads.16–19 In this Perspective, the limits achievable by noncovalent binding in enzyme–inhibitor complexes, and the greater affinity achieved by covalent bonding of the drug to the receptor, are discussed. Selected examples of covalently acting drugs will be presented (for more comprehensive reviews see references 20, 21), as well as opportunities for their future structure-based design with advances in molecular modeling or screening with target-specific libraries.

Computational Design of Catalytic Dyads and Oxyanion Holes for Ester Hydrolysis

Nucleophilic catalysis is a general strategy for accelerating ester and amide hydrolysis. In natural active sites, nucleophilic elements such as catalytic dyads and triads are usually paired with oxyanion holes for substrate activation, but it is difficult to parse out the independent contributions of these elements or to understand how they emerged in the course of evolution. Here we explore the minimal requirements for esterase activity by computationally designing artificial catalysts using catalytic dyads and oxyanion holes. We found much higher success rates using designed oxyanion holes formed by backbone NH groups rather than by side chains or bridging water molecules and obtained four active designs in different scaffolds by combining this motif with a Cys-His dyad. Following active site optimization, the most active of the variants exhibited a catalytic efficiency (k(cat)/K(M)) of 400 M(-1) s(-1) for the cleavage of a p-nitrophenyl ester. Kinetic experiments indicate that the active site cysteines are rapidly acylated as programmed by design, but the subsequent slow hydrolysis of the acyl-enzyme intermediate limits overall catalytic efficiency. Moreover, the Cys-His dyads are not properly formed in crystal structures of the designed enzymes. These results highlight the challenges that computational design must overcome to achieve high levels of activity.

Quantum Mechanical Design of Enzyme Active Sites

The design of active sites has been carried out using quantum mechanical calculations to predict the rate-determining transition state of a desired reaction in presence of the optimal arrangement of catalytic functional groups (theozyme). Eleven versatile reaction targets were chosen, including hydrolysis, dehydration, isomerization, aldol, and Diels-Alder reactions. For each of the targets, the predicted mechanism and the rate-determining transition state (TS) of the uncatalyzed reaction in water is presented. For the rate-determining TS, a catalytic site was designed using naturalistic catalytic units followed by an estimation of the rate acceleration provided by a reoptimization of the catalytic site. Finally, the geometries of the sites were compared to the X-ray structures of related natural enzymes. Recent advances in computational algorithms and power, coupled with successes in computational protein design, have provided a powerful context for undertaking such an endeavor. We propose that theozymes are excellent candidates to serve as the active site models for design processes.

How similar are enzyme active site geometries derived from quantum mechanical theozymes to crystal structures of enzyme-inhibitor complexes? Implications for enzyme design

Quantum mechanical optimizations of theoretical enzymes (theozymes), which are predicted catalytic arrays of biological functionalities stabilizing a transition state, have been carried out for a set of nine diverse enzyme active sites. For each enzyme, the theozyme for the rate‐determining transition state plus the catalytic groups modeled by side‐chain mimics was optimized using B3LYP/6–31G(d) or, in one case, HF/3–21G(d) quantum mechanical calculations. To determine if the theozyme can reproduce the natural evolutionary catalytic geometry, the positions of optimized catalytic atoms, i.e., covalent, partial covalent, or stabilizing interactions with transition state atoms, are compared to the positions of the atoms in the X‐ray crystal structure with a bound inhibitor. These structure comparisons are contrasted to computed substrate–active site structures surrounded by the same theozyme residues. The theozyme/transition structure is shown to predict geometries of active sites with an average RMSD of 0.64 Å from the crystal structure, while the RMSD for the bound intermediate complexes are significantly higher at 1.42 Å. The implications for computational enzyme design are discussed.

SU‐E‐T‐703: Brain Dose From Gamma Knife Depends Primarily On the Treated Volume and Not On the Number, Shape Or Location of the Lesions

Purpose: To assess the hypothesis that the volume of brain parenchyma that receives a certain dose level in Gamma‐Knife is dependent on the treated volume and not on the number, shape or location of the lesions. This would help a physician validate the suitability of GammaKnife based stereotactic radiosurgery (GKSR) prior to treatment. Methods: Simulation studies were performed to establish the hypothesis for an oblong, a spherical lesion of various sizes and multiple spherical lesions. A similar study was performed on forty patients who underwent GKSR with mean age 54 (range 7–80), mean number of lesions 2.5 (range 1–6) and mean lesion volume at presentation 4.4cc (range 0.02cc–22.2cc). Following recommendations of QUANTEC, V12 of brain (VB12) was measured and a power‐law based relation is proposed here for estimating VB12 from the volume of target treated to 50% of maximum dose (VT50%). Results: In the simulation study, the volume of brain irradiated by 50% (VB50%), 10% (VB10%) and 1% (VB1%) of maximum dose was found to have linear, linear and exponentially increasing dependence on VT50%, respectively. In the retrospective study on forty GKSR patients, a similar relationship was found to predict the brain dose with a Spearman correlation coefficient >0.9 and the corresponding p‐value from Students T‐test <1*10‐4 for all the above 3 dependences. The relationships between clinical VT50% and the number and aspect ratio of lesions were statistically insignificant. The measured VB12 of brain agrees with the calculated value of VB12 to within 1.7%. Conclusion: The simulation and the retrospective clinical studies indicate that the volume of brain irradiated by a percentage of maximum dose is dependent on the treated volume and not on the number, shape or location of the lesions.