Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening

Abstract

<div> <div> <div> <p>Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design. </p> </div> </div> </div>

Harnessing Conformational Plasticity to Generate Designer Enzymes

“…There has been substantial interest in understanding the evolution of enzymatic activity on existing enzymatic scaffolds, either through specialization toward one of a set of generalist functionalities, or through emergence of completely new activities in existing active sites (see e.g. refs ( 16 , 43 − 49 )). This focus on existing enzymes makes sense considering that at least 87% of all existing enzyme functions have been estimated to have either evolved from another pre-existing catalytic function, or evolved through specialization of a generalist enzyme.…”

“…Combined structural and computational analysis suggested that this was due to the increased rigidity of the evolved active sites, which could not adapt to bind the substrate and catalyze Kemp elimination with optimal electrostatic preorganization. Subsequently, we performed computationally focused ultra-low-throughput screening of variants of our most efficient lactamase predicted by FuncLib, 40 and were able to further enhance our most proficient lactamase from our earlier study 177 to k cat ≈ 10 2 s –1 and k cat / K M ≈ 2 × 10 4 M –1 s –1 , 49 bringing it to the range of the catalytic activities of naturally occurring enzymes. 178 We note that the catalytic base (D229) introduced into the de novo active site lies on the end of a flexible loop.…”

“… 200 , 201 , 234 , 235 Clearly, this suggests that conformational dynamics is therefore also a feature that can be manipulated in protein engineering, to generate new designer enzymes with targeted substrate specificities or improved catalytic activity, and there are a number of such success stories in the literature. 11 , 31 , 49 Computational approaches have played a big role in protein engineering, in particular in the context of designing de novo enzymes. 164 , 170 , 236 As an illustration, the topic of de novo enzyme design has been recently reviewed extensively by Korendovcyh and DeGrado, 237 who describe three key stages of de novo design: (1) manual protein design (based on work from the 1970s and 1980s), (2) computational design guided by fundamental physico-chemical principles (from the mid 1980s to the early 2000s), and (3) fragment-based and bioinformatically informed computational design (starting in the early 2000s).…”

“… 164 − 166 , 171 , 176 This is changing, as greater awareness of the importance of conformational dynamics, as well as the role of remote mutations in modulating activity, 239 − 245 means that both conformational dynamics and mutations of outer shell residues are starting to be incorporated into design approaches. 11 , 31 , 49 …”

“…To take just a few of the examples discussed in this work, in the case of chalcone isomerases (CHI), 25 the role of conformational dynamics is easy to assign, as all the key catalytic residues are already in place in the non-catalytic ancestor, and evolution appears to be primarily fine-tuning both side-chain conformational dynamics (through optimizing the position of the catalytic arginine, Figure 3 ) and substrate positioning (through elimination of non-productive substrate binding conformations in the evolved enzyme). In the case of the β-lactamases we have repurposed as Kemp eliminases, 49 , 177 once again, scaffold flexibility appears to play an important role both in the process of specialization from a generalist to a specialist β-lactamase, 151 and for whether the Precambrian vs modern enzymes are capable of accommodating our de novo active site for catalyzing Kemp elimination. 49 , 177 In the case of the designed Kemp eliminase, KE07, we observed that the introduction of remote mutations facilitates the stabilization of completely new active-site conformations through evolutionary conformational selection.…”