Exploiting non-covalent π interactions for catalyst design

Abstract

Molecular recognition, binding and catalysis are often mediated by non-covalent interactions involving aromatic functional groups. Although the relative complexity of these so-called π interactions has made them challenging to study, theory and modelling have now reached the stage at which we can explain their physical origins and obtain reliable insight into their effects on molecular binding and chemical transformations. This offers opportunities for the rational manipulation of these complex non-covalent interactions and their direct incorporation into the design of small-molecule catalysts and enzymes.

Figure 1. Overview of catalysis and non-covalent π interactions

a, Qualitative depiction of catalysis via transition-state (TS) stabilization, where C is catalyst, S is substrate, and P is product. G^o, Gibbs free energy in standard state; reaction coordinate indicates the progression of the reaction; C–S^‡, activated complex. b, Featured interactions (grey dashed lines) of π systems in this Review. X = B, C, N, O.

Figure 2. Overview of π-stacking geometries and evidence for the direct interaction model

a, π–π interaction geometries (see text for details). b, Hammett correlations (interaction energy versus σ_meta, the meta-Hammett substituent parameter) supporting the direct interaction model. Broken red line in insets indicates interaction. Adapted from ref. , American Chemical Society. c, Correlations demonstrating geometric consequences of the direct interaction model. Insets show Ar¹ (top) and Ar² (bottom); broken red lines in insets indicate interactions. On axes, E_int(Ar¹ × Ar²) denotes interaction energy between Ar¹ and Ar², and so on. Adapted from ref. , American Chemical Society. In b and c, regression lines and their equations are shown: R, correlation coefficient.

Figure 3. Experiments quantifying effects on π-stacking interactions

a, Substituent effects on π-stacking interactions detected using molecular torsion balance 3.1 (shown unfolded and folded); Δ G^o = –RTlnK_eq. b, Transition-state interaction energies (Δ G_TS) of rings 2 and 3 during conversion of pyridine of 3.2 (left) to methyl pyridinium (right) using methyl iodide (MeI).

Figure 4. Studies of XH–π interactions

a, Effect of X and arene identity on XH–π interactions. The structure that was computed to determine E_int in each case is shown boxed at right; S is a variable substituent. Adapted from ref. , American Chemical Society. b, Double mutant cycles in β-hairpin oligopeptide to quantify CH–π interactions. Left, the oligopeptide under evaluation, where X and Y are variable substituents. Previous studies support the CH–π interactions as shown in the box on the right. c, Chiral host (left) binds amino acid guests (right) selectively through a CH–π interaction, as measured by K_D/K_L. d, Rationalization of enantioselectivity in asymmetric transfer hydrogenation of aryl ketones. Top, reaction studied; bottom, calculated diastereomeric transition states (Si and Re isomers shown left and right, respectively). e.e., enantiomeric excess.

Figure 5. Examples of cation–π interactions

a, Left, depiction of the aromatic box in the HP1 chromodomain binding trimethyllysine (KMe₃) of histone 3 (H3: Y, tyrosine; W, tryptophan). Green, HP1 chromodomain carbon; blue, nitrogen; red, oxygen; black, H3 carbon. Right, binding of HP1 chromodomain to wild-type H3 (unmodified) and a series of mutants (tBuNle9, tert-butyl norleucine; K9Me₁, methyllysine; K9Me₂, dimethyllysine; K9Me₃ trimethyllysine). Reprinted from ref. , National Academy of Sciences, USA. b, Proposed mechanism for hopene synthesis from squalene. c, Nucleophilic ring opening of episulfonium ions is stabilized by a cation–π interaction: top, reaction; middle, transition state; bottom, effect of catalyst substituents on product enantiomeric excess.

Figure 6. Examples of anion–π interactions

a, Anion–π geometries exemplified in 6.1, 6.2 and 6.3. b, Effect of polarizability on anion–π interaction. Boxed structure, interaction geometry. Values of parameters are shown under the relevant structures: Q_zz, z² component of the quadrupole moment tensor; α, molecular polarizability; E, energy. c, Neutral receptors for anion binding. Top, depiction of bimolecular association between neutral receptors 6.4–6.6 and halide anions Cl⁻, Br⁻ and I⁻; bottom, the free energies of association (Δ G^o).

Figure 7. Substituent effects on anion–π interactions

a, Modulation of pK_a values of malonate moiety embedded within naphthalenediimide (NDI) scaffold (7.2, bottom) through anion–π interactions compared with diethylmalonate as a control (7.1, top). b, Top, depiction of differential stabilization of planar (leading to addition product 7.4) and non-planar (leading to decarboxylation product 7.5) tautomers of malonic acid half thioester (7.3) by NDI scaffold. Bottom, effect of modulation of NDI (7.7 and 7.9) electronic properties on product distribution (addition versus decarboxylation, a/d) and comparison with controls (7.6 and 7.8) incapable participating in anion–π interactions. Differential transition-state stabilization is tuned over a range of 1.3 kcal mol⁻¹.

Figure 8. Examples of lone pair–π interactions

a, Example of lone pair(lp)–π interaction in Z-DNA (broken red line in magnified inset at right). Adapted from ref. , PCCP Owner Societies, and refs and , American Chemical Society. b, Quantification of lp–π interaction in solution. Box at left, depiction of the dimeric solid-state structure of amino alcohol 8.1 implicating lp–π interactions as important structural elements. Middle, association between electronically diverse dimethylamino arylethylamines (8.2 and 8.3) and methanol in solution, and (right) correlation between enthalpy of association (Δ H_obs) and the Taft parameter (σ* ). c, Quantification of lp–π interactions using molecular torsion balances (anti and syn isomers of 8.4). Δ G^o = –RTlnK_eq, where K_eq = [syn]/[anti].