Computational Design of Enzymes

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

Artificial enzymes bringing together computational design and directed evolution

Enzymes are proteins that catalyse chemical reactions and, as such, have been widely used to facilitate a variety of natural and industrial processes, dating back to ancient times. In fact, the global enzymes market is projected to reach $10.5 billion in 2024. The development of computational and DNA editing tools boosted the creation of artificial enzymes (de novo enzymes) - synthetic or organic molecules created to present abiological catalytic functions. These novel catalysts seek to expand the catalytic power offered by nature through new functions and properties. In this manuscript, we discuss the advantages of combining computational design with directed evolution for the development of artificial enzymes and how this strategy allows to fill in the gaps that these methods present individually by providing key insights about the sequence-function relationship. We also review examples, and respective strategies, where this approach has enabled the creation of artificial enzymes with promising catalytic activity. Such key enabling technologies are opening new windows of opportunity in a variety of industries, including pharmaceutical, chemical, biofuels, and food, contributing towards a more sustainable development.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Engineering a Histone Reader Protein by Combining Directed Evolution, Sequencing, and Neural Network Based Ordinal Regression

Directed evolution is a powerful approach for engineering proteins with enhanced affinity or specificity for a ligand of interest but typically requires many rounds of screening/library mutagenesis to obtain mutants with desired properties. Furthermore, mutant libraries generally only cover a small fraction of the available sequence space. Here, for the first time, we use ordinal regression to model protein sequence data generated through successive rounds of sorting and amplification of a protein-ligand system. We show that the ordinal regression model trained on only two sorts successfully predicts chromodomain CBX1 mutants that would have stronger binding affinity with the H3K9me3 peptide. Furthermore, we can extract the predictive features using contextual regression, a method to interpret nonlinear models, which successfully guides identification of strong binders not even present in the original library. We have demonstrated the power of this approach by experimentally confirming that we were able to achieve the same improvement in binding affinity previously achieved through a more laborious directed evolution process. This study presents an approach that reduces the number of rounds of selection required to isolate strong binders and facilitates the identification of strong binders not present in the original library.

Co-evolution of β-glucosidase activity and product tolerance for increasing cellulosic ethanol yield

β-Glucosidase (BGL) plays a key role in cellulose hydrolysis. However, it is still a great challenge to enhance product tolerance and enzyme activity of BGL simultaneously. Here, we utilized one round error-prone PCR to engineer the Penicillium oxalicum 16 BGL (16BGL) for improving the cellulosic ethanol yield. We identified a new variant (L-6C), a triple mutant (M280T/V484L/D589E), with enhanced catalytic efficiency ([Formula: see text]) for hydrolyzing pNPG and reduced strength of inhibition ([Formula: see text]) by glucose. To be specific, L-6C achieved a [Formula: see text] of 0.35 at a glucose concentration of 20 mM, which was 3.63 times lower than that attained by 16BGL. The catalytic efficiency for L-6C to hydrolyze pNPG was determined to be 983.68 mM^-1 s^-1, which was 22% higher than that for 16BGL. However, experiments showed that L-6C had reduced binding affinity (2.88 mM) to pNGP compared with 16BGL (1.69 mM). L-6C produced 6.15 g/L ethanol whose yield increased by about 10% than 16BGL. We performed molecular docking and molecular dynamics (MD) simulation, and binding free energy calculation using the Molecular Mechanics/Poisson Boltzmann surface area (MM/PBSA) method. MD simulation together with the MM/PBSA calculation suggested that L-6C had reduced binding free energy to pNPG, which was consistent with the experimental data.

Rational molecular design for improving digestive enzyme resistance of beta-glucosidase from Trichoderma viride based on inhibition of bound state formation

Beta-glucosidase (BGL1) is widely used in animal feed industries. However, degradation caused by digestive enzymes in the intestine hampers its application. Improving the resistance of feed enzymes against proteases is crucial in livestock farming. To improve the resistance of beta-glucosidase against pepsin and trypsin, a rational molecular design based on the inhibition of bound-state formation and secondary design was developed. The strategy includes: (1) prediction of the interaction surface of the pepsin-BGL1 complex structure, (2) prediction of key amino acids affecting the formation of the complex, (3) optimization of pepsin-resistant mutants by structural evaluation, (4) secondary molecular design based on pepsin-resistant mutants, and optimization of pepsin and trypsin-resistant mutants. Two BGL1 protein mutants (BGL1^Q627C and BGL1^{Q627C/R543H/R646W}) were constructed, and then mutated and wild-type BGL1s were expressed in Pichia pastoris. The half-life of BGL1^Q627C and BGL1^{Q627C/R543H/R646W} were 1.36 and 1.51 times that of the wild type upon pepsin exposure, respectively. For trypsin resistance, the half-life were 0.93 and 1.53 times that of the wild type, respectively. Compare to those of the wild type, most of the basic enzymatic properties of both mutants were not significantly changed except for increased Michaelis constants. The rational design method can be used as a guide for modifying other feed enzymes.

Insights from molecular dynamics simulations for computational protein design

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations was used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

Establishing catalytic activity on an artificial (βα)8-barrel protein designed from identical half-barrels

It has been postulated that the ubiquitous (βα)8-barrel enzyme fold has evolved by duplication and fusion of an ancestral (βα)4-half-barrel. We have previously reconstructed this process in the laboratory by fusing two copies of the C-terminal half-barrel HisF-C of imidazole glycerol phosphate synthase (HisF). The resulting construct HisF-CC was stepwise stabilized to Sym1 and Sym2, which are extremely robust but catalytically inert proteins. Here, we report on the generation of a circular permutant of Sym2 and the establishment of a sugar isomerization reaction on its scaffold. Our results demonstrate that duplication and mutagenesis of (βα)4-half-barrels can readily lead to a stable and catalytically active (βα)8-barrel enzyme.

The influence of protein dynamics on the success of computational enzyme design

We characterize the molecular dynamics of a previously described computational de novo designed enzyme optimized to perform a multistep retrol-aldol reaction when engineered into a TIM barrel protein scaffold. The molecular dynamics simulations show that the protein dynamics under physiological conditions of temperature and aqueous environment distorts the designed geometric factors of the substrate-enzyme reaction intermediates, such that catalysis is limited by the primary retrol-aldol step of proton abstraction from the covalently bound substrate and its interactions with a histidine-aspartate dyad. These results emphasize that computational enzyme designs will benefit from considerations of dynamical fluctuations when optimizing active site geometries.

Towards the automated engineering of a synthetic genome

The development of the technology to synthesize new genomes and to introduce them into hosts with inactivated wild-type chromosome opens the door to new horizons in synthetic biology. Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis. The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution. The biggest bottleneck lies on the use of an appropriate fitness function. This fitness function, usually cell growth, relies on the ability to quantitatively model the biochemical networks of the cell at the genome scale using parameters inferred from high-throughput data. Computational methods integrating such models in a common multilayer design platform can be used to automatically engineer synthetic genomes under physiological specifications. We describe the current state-of-the-art on automated methods for engineering or re-engineering synthetic genomes. We restrict ourselves to global models of metabolism, transcription and DNA structure. Although we are still far from the de novo computational genome design, it is important to collect all relevant work towards this goal. Finally, we discuss future perspectives about the practicability of an automated methodology for such computational design of synthetic genomes.

Designing new cellular signaling pathways

All cells respond to signals from the environment. Extracellular stimuli activate intracellular signal transduction pathways that make decisions about cell identity, behavior, and survival. A nascent field aims to design and construct new signaling pathways beyond those found in nature. Current strategies exploit the structural modularity of many signaling proteins, which makes them inherently amenable to domain-swapping tactics that exchange their input and output connections. The results reveal a remarkable degree of functional plasticity in signaling proteins and pathways, as well as regulatory logic that can be transported to new proteins. Modified adaptor and scaffold proteins can reroute signal traffic and adjust the response behavior of the pathway circuit. These synthetic biology approaches promise to deepen our understanding of existing signaling pathways and spur the development of new cellular tools for research, industry, and medicine.

TransCent: computational enzyme design by transferring active sites and considering constraints relevant for catalysis

Background

Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used.

Results

We have developed TransCent, a computational enzyme design method supporting the transfer of active sites from one enzyme to an alternative scaffold. In an optimization process, it balances requirements originating from four constraints. These are 1) protein stability, 2) ligand binding, 3) pK_a values of active site residues, and 4) structural features of the active site. Each constraint is handled by an individual software module. Modules processing the first three constraints are based on state-of-the-art concepts, i.e. RosettaDesign, DrugScore, and PROPKA. To account for the fourth constraint, knowledge-based potentials are utilized. The contribution of modules to the performance of TransCent was evaluated by means of a recapitulation test. The redesign of oxidoreductase cytochrome P450 was analyzed in detail. As a first application, we present and discuss models for the transfer of active sites in enzymes sharing the frequently encountered triosephosphate isomerase fold.

Conclusion

A recapitulation test on native enzymes showed that TransCent proposes active sites that resemble the native enzyme more than those generated by RosettaDesign alone. Additional tests demonstrated that each module contributes to the overall performance in a statistically significant manner.