Enzyme dynamics: Looking beyond a single structure

Abstract

Conventional understanding of how enzymes function strongly emphasizes the role of structure. However, increasing evidence clearly indicates that enzymes do not remain fixed or operate exclusively in or close to their native structure. Different parts of the enzyme (from individual residues to full domains) undergo concerted motions on a wide range of time-scales, including that of the catalyzed reaction. Information obtained on these internal motions and conformational fluctuations has so far uncovered and explained many aspects of enzyme mechanisms, which could not have been understood from a single structure alone. Although there is wide interest in understanding enzyme dynamics and its role in catalysis, several challenges remain. In addition to technical difficulties, the vast majority of investigations are performed in dilute aqueous solutions, where conditions are significantly different than the cellular milieu where a large number of enzymes operate. In this review, we discuss recent developments, several challenges as well as opportunities related to this topic. The benefits of considering dynamics as an integral part of the enzyme function can also enable new means of biocatalysis, engineering enzymes for industrial and medicinal applications.

Keywords: Biocatalysis; conformational sub-states; directed evolution; enzyme engineering; protein dynamics.

Figure 1.. Schematic representation of conformational landscape associated with steps of enzyme catalysis.

(A) Enzymes do not stay fixed in their ‘native’ structures, but instead undergo conformational motions. Conformational changes experienced by the protein on several time-scales facilitate the different steps of substrate (and/or cofactor) binding, reactant ground state stabilization, chemical step of conversion, and product release along the catalytic cycle of an enzyme. The green arrows over the barriers in the conformational landscape correspond to the rate of transitions between the distinct sub-states. The rates of these conformational transitions are intrinsic properties of an enzyme topology and can be reproducibly measured with appropriate experimental techniques (2, 4). (B) Interconversion between two conformational states A and B. A is the lower energy state compared to state B (excited state), therefore, the conformational populations and associated probability p_A will be higher than p_B.

Figure 2.. Schematic depiction of the millisecond time frame sampled by TRESI-HDX.

A) The experiment allows comparative analysis of changes in conformational dynamics experienced by a working enzyme during catalysis. The enzyme is first subjected to deuterium incorporation in absence (top) and presence (bottom) of a ligand of interest. To isolate unique dynamic modes associated to specific states of a catalytic cycle, broad ligand diversity can be a significant comparative asset (good or bad substrate affinity, inhibitor, analog, product, etc.). As the protein samples different dynamic modes in its working state, labile/exposed ¹H on the structure are replaced by deuterium from the solvent. Deuterium incorporation is further stopped by acid quenching after a given period of time (in this case, milliseconds) and the protein is subjected to proteolysis before peptide ESI-MS analysis. Differences in deuterium uptake effectively allows extraction and comparison of the conformational landscape sampled by the enzyme under the influence of different ligands along the reaction. Figure adapted from ref. (50). B) Catalytic mechanism of deacylation in TEM-1 β-lactamase, whereby Glu166 abstracts a proton from a strictly conserved water molecule to initiate breakdown of the acyl-enzyme intermediate and regenerate the free enzyme. The typical benzylpenicillin (BZ) substrate is illustrated as example, with labeling and mapping of catalytic residues S70, K73, S130 and E166 (yellow carbon coloring) on the crystal structure of the acylated form of TEM-1 with BZ (green carbon coloring). Long-range rigidification of the S4/S5 loop (residues 250–257, in blue) and C-terminal alpha helix of TEM-1 (residues 273–284) have been shown to affect deacylation in TEM-1, possibly through a previously uncharacterized allosteric mechanism (50).

Figure 3:. Higher-order statistical methods are required to correctly identify slower motions and conformational sub-states in proteins.

(A) Motions can be harmonic (indicated by blue curve and marked by H), quasi-harmonic (green curve, Q) or anharmonic (pink, A). Second order methods such as principal component analysis and normal mode analysis are based on fitting a quadratic equation such as the one shown by black dashed line. Good fits for H and Q can be obtained with second-order methods. However, for anharmonic motions, the second-order methods provide poor approximations. (B) Higher-order methods such as quasi-anharmonic analysis (QAA)(54b) equate to fitting higher-order polynomials (indicated by dashed black line) to the protein anharmonic motions. (C) Conformational heterogeneity in protein conformational sub-states as identified by higher-order methods provides homogeneous separation, while second-order methods fail to provide this separation. Each dot represents a single protein conformation, which is colored by internal energy (the conformations can also be classified by any other property such as distance or angles). Note that the QAA-based method correctly identifies the lower energy (IV) and higher energy (III) states relative to the other two sub-states (I and II) with mixed energy conformations.

Figure 4:. Impact of non-aqueous conditions and cellular milieu on enzyme dynamics and activity.

Laboratory experiments are usually performed in dilute aqueous conditions, considerably different than the complex environment of the cellular milieu. The effects of such environment on enzyme dynamics and activity remains elusive. Miscible organic solvents and crowding agents (including inert proteins) have been used as non-aqueous conditions. The effects of altered solvation have already been shown to change the conformational energy landscape and how the enzyme samples the functionally relevant motions, in turn altering the enzyme activity.

Figure 5:. Evolving new enzyme activity by controlling dynamics.

(A) Multiple factors promoting enzyme function are conserved as a part of the enzyme fold. In addition to the active-site residues for structural role, distal residues are also preserved as part of the enzyme architecture. Recent study of ribonucleases (81) has provided insights into how the dynamics of a common fold shared by the super-family is fine-tuned for different biological activity of the sub-families. (B) Schematic overview of the approach used by Jackson and coworkers for developing new enzyme activity on an existing enzyme fold (86).

Figure 6:. Changes in enzyme dynamics and function over evolutionary trajectory.

Jackson and coworkers (86) investigated development of arylesterase activity (AE) by an enzyme which had native phosphotriesterase (PTE) activity. They reported increase in dynamics of loops L4 and L5, while decrease in dynamics of L7 during the evolution of new function (from R0 with native PTE activity to R22 with primarily AE activity). While in the reverse evolution trajectory, the dynamics of L7 increases while dynamics of L4 and L5 is reduced. R0 and Rev12 showed ~10⁴ higher catalytic efficiency (k_cat/K_M) for PTE, while R22 shoed s ~10⁴ higher k_cat/K_M for AE. Bifunctional intermediates R6 and Rev6 have equivalent efficiency for both functions, and sample conformations similar to both start and end points of the evolutionary trajectory. Increasing B-factor of individual residue is represented as width of the cartoon putty. RMSF of C_α atom of each residue is plotted, green peaks represent increase in dynamics while red peaks represent decrease in dynamics as compared to R0. k_cat/K_M ratio of PTE to AE activity is reported. Figure adapted from (86).