Protein Engineering for Industrial Biocatalysis: Principles, Approaches, and Lessons from Engineered PETases

Abstract

Protein engineering has emerged as a transformative field in industrial biotechnology, enabling the optimization of enzymes to meet stringent industrial demands for stability, specificity, and efficiency. This review explores the principles and methodologies of protein engineering, emphasizing rational design, directed evolution, semi-rational approaches, and the recent integration of machine learning. These strategies have significantly enhanced enzyme performance, even rendering engineered PETase industrially relevant. Insights from engineered PETases underscore the potential of protein engineering to tackle environmental challenges, such as advancing sustainable plastic recycling, paving the way for innovative solutions in industrial biocatalysis. Future directions point to interdisciplinary collaborations and the integration of emerging machine learning technologies to revolutionize enzyme design.

1. Introduction

For billions of years, enzymes have served as Nature’s catalysts, driving countless biochemical reactions essential for life. Archeological findings indicate that humans have exploited enzymatic processes for daily applications such as bread fermentation and beer brewing since prehistorical times [1,2].

Today, these biological catalysts are being harnessed and engineered to solve some of the most pressing challenges in industrial biotechnology. For industrial use, enzymes must not only be cost-effective but also exhibit high performance, high specificity or promiscuity, and stability under the specific conditions required for their application. These stringent requirements often expose the limitations of wild-type (WT) enzymes that frequently fail to meet industrial demands due to low catalytic rates [3], poor thermal [4] or pH stability [5], inadequate organic solvent tolerance [6], restricted substrate range [7], susceptibility to inhibition by their substrates or products [8,9,10], and incompatible optimal reaction pH [11]. Subsequently, advancements in biocatalytic performance across a wide range of operational conditions are crucial in meeting the demands of large-scale industrial applications.

To overcome these challenges, protein engineering seeks to ameliorate these issues by introducing novel enzymatic activities, enhancing catalytic efficiencies, broadening or changing substrate specificities, and optimizing enzymatic stability under harsh operational conditions, such as high temperatures or diverse pH environments [8]. Protein engineering strategies are predominantly divided into three main categories: (i) rational design, which relies on detailed structural and mechanistic knowledge of enzymes to either introduce targeted modifications or to design entirely novel catalysts; (ii) directed evolution, which mimics natural selection by iteratively mutating and screening enzymes for improved properties; and (iii) semi-rational design, which prioritizes the design of smart libraries through evolutionary insights from homologous proteins. In addition, (iv) machine learning (ML) and deep learning (DL) methods have recently emerged as promising alternative strategies, leveraging vast amounts of genomic, structural, and functional data to predict mutations that enhance enzymatic properties [12,13].

The transformative potential of protein engineering is highlighted by the example of engineered industrial polyethylene terephthalate (PET) hydrolases (PETases). PET accounts for around 5% of total global plastic production [14] and is the most recycled plastic worldwide [15], although traditional thermomechanical recycling methods downgrade PET and produce inferior recycled products [16]. The discovery of novel plastic-degrading enzymes has significantly advanced enzymatic recycling technologies, offering advantages such as selective recycling from plastic mixtures and producing final products with virgin PET quality [3,17]. Despite this progress, naturally occurring PETases exhibit limitations in their efficiency and stability, restricting their use in industrial settings; therefore, recent advances in enzymatic recycling of PET have been driven not only by the discovery of new PETases but also by significant protein engineering efforts. These advancements culminated in engineered enzymes, such as the leaf and branch compost cutinase (LCC) variant LCC^ICCG, which is the first PETase to be industrialized for PET bio-recycling [3,4,18], an example that highlights the capability of protein engineering to improve enzyme efficiency and also expand industrial enzymatic applications beyond what Nature alone provides.

This review aims to provide a guide for exploring protein engineering concepts and commonly employed strategies. It explores the structure-function interplay behind enzymatic activity, substrate specificity, and stability, along with the latest and most relevant approaches employed to optimize or create industrially relevant biocatalysts, categorized into rational design, directed evolution, semi-rational design, and ML approaches. Highlighting recent advancements and proposing future research directions, this review seeks to contribute to the development of innovative, efficient, and industrially relevant enzymes and protein engineering solutions while inspiring and equipping future scientists with the knowledge and tools needed to innovate in this emerging field during its transformative prime.

2. Fundamental Principles for Engineering Protein Activity, Specificity, and Stability

Enzymes function as biological catalysts, exhibiting remarkable specificity and catalytic efficiency [19], expediting chemical reactions by lowering the activation energy barrier required for the conversion of reactants into products, primarily through the stabilization of the reaction’s transition state(s) [20]. A comprehensive understanding of the primary principles governing enzyme activity, specificity, and stability is essential for functional enzyme engineering. Key factors influencing enzymatic performance include the reaction conditions, predominantly temperature and pH, their affinity to the substrate, the employed catalytic mechanism, and the thermodynamic stability of the enzyme under reaction conditions. An industrially relevant enzyme may need to exhibit high activity that ensures efficient, rapid, and economical catalysis, high substrate specificity or promiscuity, depending on the application, and high stability, which allows for higher temperatures, thus enhancing reaction rates, reactant solubility, and reducing microbial contamination risks [21]. This section discusses critical physicochemical parameters underlying enzymatic performance.

2.1. Dependence on Temperature

Temperature is one of the most critical factors affecting enzyme activity, stability, and overall performance. As biological catalysts, enzymes exhibit a delicate balance between enhanced activity at higher temperatures and the risk of denaturation. Understanding how temperature influences enzymatic function is essential for designing robust enzymes that balance optimum activity and thermal stability under industrial conditions.

2.1.1. Optimum Temperature ($T_{opt}$)

At low temperatures, enzymatic reactions proceed slowly due to reduced molecular motion and limited substrate-enzyme collisions. Increasing the temperature accelerates molecular movement, facilitating faster reactions up to the enzyme’s optimal temperature, $T_{opt}$, where the rate of reaction reaches its peak [22,23]. The traditional approach describes this temperature dependence as a factor that accelerates reaction rates until the enzyme denatures, according to the Arrhenius equation (Equation (1)):

$$k_{cat}\left(T\right)=A_{cat}{e}^{-{\displaystyle \frac{E_a}{RT}}}$$

(1)

where $k_{cat}$ is the rate constant, the units of which depend on the reaction order, $A_{cat}$ is the pre-exponential factor, $E_a$ is the activation energy, $R$ is the universal gas constant, and $T$ is the temperature in K.

As the temperature increases, enzyme activity rises exponentially until irreversible thermal denaturation dominates, converting the active enzyme ($E_{act}$) into an irreversibly inactivated form ($X$) (Equation (2)):

$$E_{act}\stackrel{k_{inact}}{\to }{E}_{inact}$$

(2)

The reaction rate constant for this inactivation, $k_{inact}$, also follows the Arrhenius equation (Equation (1)), with $A_{inact}$ being the pre-exponential factor and $E_{inact}$ being the activation energy for denaturation. In this case, as this is a first-order reaction, the half-life of the enzyme can also be calculated as such (Equation (3)):

$$t_{1/2}={\displaystyle \frac{ln2}{k_{inact}}}$$

(3)

However, many experimental observations have shown that the relationship between temperature, catalytic rates, and enzyme stability is more nuanced than a simple gain in rate that is offset by irreversible thermal denaturation [24,25].

In the early 20th century, the formulation of Transition State Theory by Eyring, Polanyi, and others culminated in the derivation of the Eyring equation (Equation (4)) for rate constants. For a first-order rate constant, the equation can be expressed as:

$$k_{cat}\left(T\right)=\kappa {\displaystyle \frac{k_{B}T}{h}}{e}^{{\displaystyle \frac{-\mathsf{\Delta }{G}^{‡}}{RT}}}$$

(4)

where $\mathsf{\Delta }{G}^{‡}$ represents the Gibbs free energy difference between the reactants and the transition state, $k_B$ and $h$ are the Boltzmann’s and Planck’s constants, respectively, and $\kappa$ is the transmission coefficient, which accounts for the probability of successful passage through the transition state, often assumed to be 1, reflecting a system where every trajectory crossing the transition state proceeds to product formation [26,27].

The “Equilibrium Model” introduced the idea that even before irreversible denaturation at higher temperatures, enzymes can adopt a reversible equilibrium between a fully active form, $E_{act}$, and an inactive form, $E_{inact}$, that can undergo thermal inactivation to the denatured state, $X$ [28,29]:

$$E_{act}\rightleftarrows E_{inact}\to \mathrm{X}$$

(5)

By accounting for a reversible conformational change, the equilibrium model provided markedly improved the fit to enzymatic activity data across a range of temperatures compared to older approaches, thereby reaffirming the validity of an optimal temperature that is not merely a point before catastrophic thermal denaturation but, rather, a meaningful characteristic intrinsic to the enzyme’s dynamic structural landscape [30].

More recently, Macromolecular Rate Theory (MMRT) described the temperature dependence of enzyme-catalyzed reactions independent of stability or regulatory processes, purely based on thermodynamics and the role of changing heat capacity ($\mathsf{\Delta }{C}_p^{‡}$) between the enzyme-substrate complex ($ES$) and the enzyme-transition state complex ($ET{S}^{‡}$), where the heat capacity ($C_p$) for $ES$ is generally larger. This negative $\mathsf{\Delta }{C}_p^{‡}$ reflects a reduction in low-frequency vibrational modes in the transition state. Based on experimental observations, $\mathsf{\Delta }{C}_p^{‡}$ is assumed to be constant, independent of temperature and $\kappa =1,$ for simplicity [26]. Thus, given Equation (4), if [31,32,33]:

$$\mathsf{\Delta }{G}^{‡}\left(T\right)=\mathsf{\Delta }{H}^{‡}\left(T\right)-T\mathsf{\Delta }{S}^{‡}\left(T\right)$$

(6)

$$\mathsf{\Delta }H\left(T\right)=\mathsf{\Delta }H\left(T_o\right)+{\int }_{T_o}^T\mathsf{\Delta }{C}_{p}d{T}^{\prime }$$

(7)

$$S\left(T\right)=\mathsf{\Delta }S\left(T_o\right)+{\int }_{T_o}^T{\displaystyle \frac{\mathsf{\Delta }{C}_p}{T^{\prime }}}d{T}^{\prime }$$

(8)

then the reaction rate constant, $k_{cat}$, is given by (Equation (9)):

$$k_{cat}\left(T\right)={\displaystyle \frac{k_{B}T}{h}}{e}^{{\displaystyle \frac{-\mathsf{\Delta }{H}^{‡}\left(T_o\right)-\mathsf{\Delta }{C}_p^{‡}(T-T_o)}{RT}}+{\displaystyle \frac{\mathsf{\Delta }{S}^{‡}\left(T_o\right)+\mathsf{\Delta }{C}_p^{‡}\left(\ln \left(T\right)-\ln \left(T_o\right)\right)}{R}}}$$

(9)

Due to the increasing dominance of the entropic contribution ($\mathsf{\Delta }{S}^{‡}\left(T\right)/R$) over the enthalpic term ($-\mathsf{\Delta }{H}^{‡}\left(T\right)/RT$), the reaction rate declines above the optimum temperature ($T_{opt}$), even in the absence of enzyme denaturation. In thermophilic enzymes, as $T_{opt}$ approaches 100 °C, $\mathsf{\Delta }{C}_p^{‡}$ diminishes to 0, causing the temperature dependence to approximate Arrhenius behavior as the enthalpic and entropic terms become less temperature-sensitive. Conversely, for mesophilic and psychrophilic enzymes that maintain relatively high unfolding temperatures, a negative $\mathsf{\Delta }{C}_p^{‡}$ leads to a curved temperature dependence in their catalytic rates [26].

By incorporating a simple correction term accounting for enzymatic denaturation ($k_{inact}$), MMRT can accurately deconvolute the intrinsic thermodynamic effects arising from $\mathsf{\Delta }{C}_p^{‡}$ on the reaction rate constant ($k_{cat}$) from those due to unfolding, thereby providing a comprehensive theoretical framework for understanding the temperature dependence of enzymatic reactions [26]$.$

2.1.2. Melting Temperature ($T_m$)

Enzyme inactivation theory proposes an initial equilibrium phase where the $E_{act}$ undergoes unfolding to the reversibly denatured and inactive state $E_{inact}$, which retains the potential for either refolding to its native conformation or progressing to irreversible inactivation state X (Equation (5)) [29].

Melting Temperature ($T_m$) is the temperature at which 50% of the enzyme population transitions from its active to its inactivated, but still reversibly denatured, state [34]. Close to or above the $T_m$, reversibility of inactivation rapidly decreases [29]. $T_m$ is typically measured with differential scanning calorimetry [35], optical methods such as circular dichroism [36], dynamic light scattering [37], nano-differential scanning fluorimetry (nanoDSF) based on either intrinsic fluorescence or utilizing dyes, such as SYPRO Orange [38], or directly from enzymatic activity assays at different temperatures [29]. Typically, $T_m$ falls between $5°\mathrm{C}$ and $15°\mathrm{C}$ above the $T_{opt}$ [39]. $T_m$ serves as a crucial benchmark for assessing an enzyme’s thermal resilience, as enzymes with higher $T_m$ values are generally more stable under elevated temperatures, making them more suitable for industrial processes that operate under harsh conditions [40].

2.2. Dependence on pH

Similarly to temperature, pH also influences enzymatic activity and stability by affecting the ionization states of titrating residues. As pH affects the overall protein conformation, the enzyme–substrate interactions and the catalytic residues, enzymes exhibit specific pH optima and working ranges that must be carefully considered to maintain optimal performance.

2.2.1. Optimum pH (${pH}_{opt}$)

The activity of enzymes is intrinsically linked to the pH of their environment, as the protonation states of catalytic residues and substrates are critical for enzymatic function. The intrinsic $p{K}_a$ values of ionizable groups such as carboxyls, amines, hydroxyls, thiols, and imidazoles determine their protonation state, as shown in Figure 1 [41].

However, these intrinsic values can be significantly perturbed in the active site of enzymes, markedly affecting catalysis, as titratable amino acids adopt a functional or apparent $p{K}_a$ within the protein environment. This transformation arises from electrostatic interactions with charged or partially charged proximal groups, which generate a localized microenvironment that modulates their behavior. The polarity and dielectrocharacteristics of the surrounding environment also critically influence the magnitude of this $p{K}_a$ perturbation by impacting the stability of the associated charged states. All these factors combined fine-tune the $p{K}_a$s of the titratable amino acids of a protein [41].

For example, in the serine protease mechanism, peptide bond hydrolysis is catalyzed by a catalytic triad composed of serine, histidine, and aspartate, as well as an oxyanion hole that stabilizes the intermediate complex (Figure 2). Initially, the substrate’s scissile peptide bond is oriented so that its carbonyl carbon is positioned adjacent to the nucleophilic serine residue. When the reaction begins, the histidine ring nitrogen, which has a $p{K}_a$ near 7.5 in the free enzyme [44], acts as a general-base, converting the serine into a strongly nucleophilic alkoxide-like species by deprotonating its hydroxyl, thus increasing its nucleophilicity and enabling it to attack the peptide carbonyl (nucleophilic attack). This generates a tetrahedral intermediate, stabilized in the oxyanion hole, and the histidine’s $p{K}_a$ is shifted significantly upward to between 10 and 12. Indicative $p{K}_a$s are revealed by nuclear magnetic resonance studies of chymotrypsin complexes with peptidyl trifluoroketones, analogs of the tetrahedral intermediate [44]. Subsequently, the histidine acts as a general acid, being the proton donor, thus facilitating the departure of the cleaved amine segment and shifting its $p{K}_a$ back to near 7.5. Water then enters the active site, once again activated by histidine’s elevated basicity, and performs a nucleophilic attack on the carbonyl carbon of the acyl-enzyme intermediate, generating another tetrahedral intermediate, which subsequently collapses by donating the hydrogen from the protonated histidine back to the serine, releasing the cleaved peptide C-terminus and regenerating the free enzyme [41].

Throughout these steps, the substantial shifts in histidine’s $p{K}_a$ are critical for modulating its dual role as acid and base catalyst (Figure 2). The enzyme’s optimal activity within a pH range of 8 to 9 [45,46,47] can be attributed to the ionization states of the histidine residue, which, at this pH, is mostly deprotonated, facilitating its role as a general base in deprotonating the serine and, thereafter, as a general acid that protonates the departing amine [47].

Over the years, many tools have been created to predict protein $p{K}_a$ values based on their structures using empirical rules from experiments [48], the Poisson–Boltzmann (PB) equation [49,50], Density Functional Theory (DFT) [51], and ML [52,53], among others [54,55,56]. Based on these methods, the discrete constant pH framework combines molecular dynamics (MD) with Monte Carlo simulations, allowing a more accurate approximation of the $p{K}_a$s. In this approach, an MD simulation, which provides enzyme conformational sampling, is occasionally paused to allow resampling of the residues’ protonation states [57]. By engineering the $p{K}_a$s of the catalytic amino acids, it is possible to change an enzyme’s $p{H}_{opt}$ [58,59].

2.2.2. pH Stability

Under extreme pH conditions, the primary mechanism driving protein unfolding is the electrostatic repulsion between like-charged groups within the protein structure, which may subsequently lead to aggregation or irreversible denaturation that is distinctly different from that of thermal denaturation [60]. From an engineering perspective, by modeling these unfolding events with MD simulations or experimental techniques and identifying strategies to mitigate destabilizing interactions, it is possible to rationally engineer pH-dependent stability. Such observation was seen in the stabilization of the 37-residue α/β protein CHABII by a rational single-point mutation (H21F), in which the protonation of the histidine at low pH induced unfolding occurred by destabilizing the hydrophobic core [61]. In this context, however, directed evolution often provides a more practical approach, bypassing the need for exhaustive computational predictions. This is exemplified by the engineered xylanase XynHBN188A, where two amino acid substitutions led to increased specific activity and pH stability [62].

2.3. Structure-Function Relationships

2.3.1. Substrate Affinity and Specificity

Substrate-enzyme complementarity, akin to an “induced-fit” [63,64], or the more recent but not mutually exclusive “conformational-selection” model [65] determines binding efficiency and substrate specificity. The binding energy of the enzyme-ligand complex is also utilized for catalytic turnover [66].

Engineering enzyme-substrate affinity involves tailoring enzymes’ interactions with their substrates and products, thereby enhancing enzyme specificity, promiscuity [67], or product release, even altering the final product’s composition [68]. Key determinants of substrate recognition are the shape of the active site and binding pocket, the conformations they can adopt, the combination of electrostatic, hydrophobic, hydrogen bonding, and van der Waals interactions [69,70,71], and the entrance tunnels [72].

A comparative analysis of homologous proteins with varying substrate specificities facilitates the identification of these key structural determinants [66]. In addition, energy-based docking solutions, such as AutoDock 4 (slower, more interpretable) [73] and AutoDock Vina (faster, superior results) [74], quantify protein-ligand binding affinities in silico, relatively accurately. The Molecular Mechanics Generalized Born Surface Area (MMGBSA), or the more rigorous Molecular Mechanics PB Surface Area (MMPBSA), approach provides an even better prediction of binding energy and can dissect interactions using per-residue free energy decomposition or alanine scanning [75,76,77]. The CAVER, CAVER Analyst and CaverDock suites provide powerful tools for analyzing enzymes with buried active sites to identify bottlenecks in substrate binding or product release and quantify binding energy between the bound and surface state in static and dynamic protein structures [78,79,80].

2.3.2. Stabilizing Mutations

Thermodynamics provides a useful framework for interpreting the stabilizing effects of mutations. Stabilizing mutations aim to maximize the Gibbs free energy difference ($\mathsf{\Delta }{G}_f$) between folded and unfolded states of a protein, thereby favoring the more thermodynamically stable folded conformation [21,81]. Equation (10) stipulates that protein stabilization can be principally achieved through adjustments to either the enthalpic ($\mathsf{\Delta }{H}_f$) or the entropic ($\mathsf{\Delta }{S}_{conf}$) components of the system, although deconvoluting the effect of a structural modification on each term is difficult [21]:

$$\mathsf{\Delta }{G}_f=\mathsf{\Delta }{H}_f-T\mathsf{\Delta }{S}_{conf}$$

(10)

For instance, mutations such as serine to proline in surface loops limit the conformational entropy of the unfolded state, thus increasing $\mathsf{\Delta }{S}_{conf}$, and are unlikely to be interpreted as adding or removing any atomic interactions that contribute to $\mathsf{\Delta }{H}_f$ [21,81]. Notwithstanding, by measuring the effect on $\mathsf{\Delta }{H}_f$ experimentally, the results may differ from the oversimplified interpretation [21].

Advances in biomolecular force fields, thermodynamic cycle analyses, and ML have facilitated the comprehensive assessment of mutational impacts by employing computational tools to systematically evaluate the effects of single amino acid substitutions. Such tools are Rosetta ddg_monomer [82], FoldX [83], ERIS [84], or DeepDDG [85] which predict the $\mathsf{\Delta }\mathsf{\Delta }{G}_f$ of a protein induced by a point mutation, either given by the difference in energy between the WT structure and the point mutant structure, calculated by a forcefield, or, in the last case, via DL. By leveraging these predictors and implementing in silico site-saturated mutagenesis, FireProt’s energy-based approach scores suggest single-point mutations by the predicted $\mathsf{\Delta }\mathsf{\Delta }{G}_f$ from Rosetta and FoldX with 100% precision and 0% false-positive rate on experimental datasets (after filtering with conservation analysis by the Rate4Site tool [86]), albeit at the expense of omitting some [87].

2.3.3. Flexibility

Enzymes are inherently dynamic and flexible macromolecules, characterized by internal conformational motions essential for substrate binding, product release, and potentially the catalytic mechanism itself [20,88,89]. Flexibility is often evaluated using B-factors (Debye-Waller factors, temperature factors, or atomic displacement parameters) derived from X-ray crystallography [90]. These factors quantify atomic displacement or mobility within the crystalline structure, providing a detailed view of the enzyme’s dynamic properties at an atomic scale. Regions characterized by lower B-factors exhibit structural rigidity, whereas higher B-factors indicate flexible domains that are often integral to substrate binding and conformational transitions [91]. B-factors can be visualized by PyMOL’s B-factor putty mode (Figure 3), which illustrates atomic mobility by varying thickness and color based on flexibility [92,93]. Variability in B-factors across structures of the same molecule often arises from external influences unrelated to intrinsic molecular properties, such as experimental conditions or computational methodologies, and, therefore, careful consideration when drawing comparisons between different structures is necessary [90]. Aside from X-ray structures, B-factors can be computationally estimated using MD simulations [94], normal mode analysis [95,96], elastic network models [97], and ML models [98].

2.3.4. Activity-Stability Trade-Off

Enzyme flexibility is a critical determinant of substrate specificity, enabling the dynamic structural transitions required for substrate binding, catalytic turnover, and product release through the “induced-fit” [63] or the “conformational-selection” models [65]. Traditionally, excessive flexibility has been thought of as undermining structural stability and being a trade-off with protein activity [100]. Rational protein engineering strategies have introduced targeted modifications, e.g., disulfide bonds, proline residues, and hydrogen bonds, to reduce protein flexibility away from the active site and fine-tune the activity-stability balance, to great acclaim [21].

The hypothesized trade-off has remained a cornerstone in protein engineering with numerous studies documenting evidence supporting its existence [20,101,102,103]. However, this assumption consequently suggests that a more thermostable enzyme operates as a slower catalyst compared to a less stable homolog under low-temperature conditions; that does not typically occur, neither in engineered systems [20] nor when comparing WT thermophilic and mesophilic homologous enzymes [104]. In fact, thermal stability can be enhanced by increasing conformational entropy upon folding ($\mathsf{\Delta }{S}_{conf}$), thereby generating a more thermostable enzyme while preserving flexibility (Equation (10)). In this case, a more thermostable enzyme may exhibit greater flexibility compared to a less stable homolog, if the increase in $\mathsf{\Delta }{S}_{conf}$ results from an increase in the conformational entropy of the native state ($S_{con{f}_F}$) [20,105]:

$$\mathsf{\Delta }{S}_{conf}={S_{conf}}_F-S_{con{f}_U}$$

(11)

Directed evolution studies further illuminate this dynamic, as engineered enzymes frequently achieve enhanced stability or activity without corresponding losses, showcasing the possibility of decoupling these properties [20], as is clearly the case with all reported directed evolution studies of the PET-degrading PETase from Ideonella sakaiensis (IsPETase) [106]. Nevertheless, strategies such as directed evolution and rational design are employed to navigate this balance, aiming to enhance both stability and activity without compromising either [4,107,108].

2.3.5. Structure-Function Engineering Insights

To enhance the activity, specificity, and stability of enzymes, a variety of engineering approaches have been developed (see Section 3). Table 1 provides an overview of key modifications used in protein engineering—regardless of their discovery strategy—outlining their mechanisms and typical effects in terms of activity, substrate affinity, and stability while acknowledging that any modification inherently impacts multiple attributes. Figure 4 showcases specific applications of these modifications. Less common enhancements such as allosteric site engineering [109] and helix capping also contribute to enzyme optimization [110].

Table 1. Key modifications in protein engineering and approaches that enable them.

Modification	Mechanism	Primary Effects	Engineering Approach	Refs.
Single-point mutations	Selective single point mutations with energetically favorable residues (minimizing $\mathsf{\Delta }\mathsf{\Delta }{G}_f$), residues important for substrate binding (surface electrostatics/hydrophobicity) or enhancing activity.	activity ↑↓** affinity ↑↓ stability ↑↓	RD, SRD, DE, ML *	[4,12,21,106,111]
Disulfide bridges	Covalent linkage of cysteine residues to rigidify the protein backbone and constrain conformational freedom.	stability ↑ activity ↓ (typically)	RD, ML	[4,12,21,112]
$\mathrm{Shifting}p{K}_a$ values	Modifying electrostatic microenvironment with single-point mutations to fine-tune the $p{K}_a$s of catalytic residues.	activity ↑ (in different pH)	RD, DE	[11,58,59,113]
Hydrogen bond network optimization	Modification of hydrogen-bonding network to reinforce affinity with substrate and active site stability.	activity ↑ affinity ↑	RD	[114]
Salt bridges	Introducing salt bridges to reinforce protein structure.	stability ↑	RD	[12,21,115]
Glycosylation	Introducing sites for post translational modifications.	stability↑	RD	[116,117]
Surface loop engineering	Rational remodeling of surface loops via shortening or loop grafting.	activity ↑↓ affinity ↑↓ stability ↑↓	RD, SRD	[118,119]
Hydrophobic core packing	Optimized distribution of hydrophobic residues to eliminate internal cavities.	stability↑	RD	[21,120]

* DE: directed evolution, ML: machine learning, RD: rational design, SRD: semi-rational design. ** ↑: increased, ↓: decreased.

Table 1. Key modifications in protein engineering and approaches that enable them.

Modification	Mechanism	Primary Effects	Engineering Approach	Refs.
Single-point mutations	Selective single point mutations with energetically favorable residues (minimizing $\mathsf{\Delta }\mathsf{\Delta }{G}_f$), residues important for substrate binding (surface electrostatics/hydrophobicity) or enhancing activity.	activity ↑↓** affinity ↑↓ stability ↑↓	RD, SRD, DE, ML *	[4,12,21,106,111]
Disulfide bridges	Covalent linkage of cysteine residues to rigidify the protein backbone and constrain conformational freedom.	stability ↑ activity ↓ (typically)	RD, ML	[4,12,21,112]
$\mathrm{Shifting}p{K}_a$ values	Modifying electrostatic microenvironment with single-point mutations to fine-tune the $p{K}_a$s of catalytic residues.	activity ↑ (in different pH)	RD, DE	[11,58,59,113]
Hydrogen bond network optimization	Modification of hydrogen-bonding network to reinforce affinity with substrate and active site stability.	activity ↑ affinity ↑	RD	[114]
Salt bridges	Introducing salt bridges to reinforce protein structure.	stability ↑	RD	[12,21,115]
Glycosylation	Introducing sites for post translational modifications.	stability↑	RD	[116,117]
Surface loop engineering	Rational remodeling of surface loops via shortening or loop grafting.	activity ↑↓ affinity ↑↓ stability ↑↓	RD, SRD	[118,119]
Hydrophobic core packing	Optimized distribution of hydrophobic residues to eliminate internal cavities.	stability↑	RD	[21,120]

* DE: directed evolution, ML: machine learning, RD: rational design, SRD: semi-rational design. ** ↑: increased, ↓: decreased.

3. Protein Engineering Approaches and Strategies

Protein engineering strategies can be broadly categorized into rational design, directed evolution, semi-rational design, and, more recently, ML/DL approaches [12]. Figure 5 provides an overview of these schemes.

3.1. Rational Design

Rational design mainly refers to leveraging knowledge of a target enzyme’s structural and functional attributes and using computational modeling and simulation frameworks to predict mutations, insertions, or deletions aimed at augmenting enzymatic performance. Most recent studies on improving plastic-degrading enzymes primarily adopt a structure-based approach, exploiting the extensive structural and functional data available for these enzymes [12]. In return, these enzymes also function as benchmark platforms for advancing and refining protein engineering methodologies. Another domain of rational design involves the de novo synthesis of novel enzymes, by incorporating active sites and substrate-binding pockets predicted to catalyze a reaction of interest into geometrically compatible native scaffolds [126].

3.1.1. Structure-Based Design

Structure-based rational design relies on computational tools that analyze and manipulate protein structures. These structures may be obtained through experimental methods (e.g., X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance), homology modeling (e.g., SWISS-MODEL and MODELLER) leveraging information from similar solved structures, or, lately, DL-based predictors (e.g., AlphaFold 2.0 and ESMFold), which achieve unprecedented accuracy in structural prediction while maintaining high computational efficiency [127,128,129]. Docking software, such as AutoDock Vina, predict protein-ligand binding modes [74]. Visualization platforms such as PyMOL [130] and ChimeraX [131] enable detailed analysis of enzymatic structures, facilitating the identification of protein regions critical for activity, substrate affinity, and stability. These tools help pinpoint key protein-ligand interactions (i.e., hydrogen bonds, hydrophobic contacts, electrostatic interactions, steric hindrances, etc.) and evaluate flexibility by analyzing B-factors, which highlight potential engineering hotspots [93]. Integrated mutagenesis tools allow for the visualization and evaluation of potential mutations. Energy-based methods (e.g., Rosetta ddg_monomer [82] and FoldX [83], both integrated into FireProt [87]) systematically evaluate the $\mathsf{\Delta }\mathsf{\Delta }{G}_f$ associated with point mutations, and specialized programs such as Disulfide by Design 2.0 enable the introduction of disulfide bonds [132].

Similarly, MD simulations using software such as GROMACS [133], AMBER [134], and CHARMM [135] enable the exploration of protein and protein-ligand dynamics. MD simulations can extract B-factors [94], estimate binding free energies with MMPBSA/MMGBSA, and decompose the contributions of each amino acid to these energies [75,77]. Additionally, they provide insights into the frequency and time-dependency of interactions, revealing how often and for how long contacts (e.g., hydrogen bonds) or conformational states occur [136]. They can also calculate the activation energy barrier of a reaction using quantum mechanics/molecular mechanics and umbrella sampling [137], as well as model reaction mechanisms at an atomic level [138]. The simulated trajectories can be analyzed to reveal conformational clusters representing accessible states [139], while Markov State Models may be used to characterize these states by their populations and transition kinetics [140]. In addition, Principal Component Analysis reduces the dimensionality of the configurational space by unveiling the dominant modes of motions [141] and also allows plotting of the free energy landscape projected onto principal components [142]. Furthermore, Constant-pH MD addresses the limitation of fixed protonation states by allowing titratable residues to dynamically switch between protonated and deprotonated forms, capturing pH-dependent behaviors and allowing the determination of their $p{K}_a$ values [57]. Moreover, MD simulations can explore the denaturation of proteins by simulating them under diverse thermal or environmental conditions [143,144].

3.1.2. De Novo Design

The overarching goal of protein engineering is the de novo development of enzymatic functions derived from first principles [66], once considered to be impossible [145]. Leveraging computational methods to create structures and functionalities absent in nature, “true” de novo design aims to construct entirely new proteins from scratch, relying purely on computationally generated backbones, while the “minimalist” approach utilizes known stable protein folds as the foundation for introducing new functional sites, aiming to establish the feasibility of a catalytic reaction [146]. Computational tools, such as Rosetta [147,148], play a pivotal role in designing scaffolds and optimizing sequences for functional and structural refinement [149]. Rosetta Match [150] and AsiteDesign [151] extend these capabilities by enabling the precise redesigning or grafting of active sites onto protein scaffolds and engineering the stability of the transition state [152].

Although de novo-designed enzymes frequently exhibit limited catalytic efficiencies, iterative optimization through directed evolution, discussed in the following section, or other engineering approaches can substantially enhance their performance [153]. Despite the inherent challenges, advances in computational frameworks continue to highlight the potential of this approach [154]. Prominent achievements include the development of catalysts for Diels-Alder reactions [155], ester hydrolysis [156], and retro-aldol reactions [157]. In the context of plastic-degrading enzymes, a polycarbonate hydrolase has been successfully designed de novo by introducing a catalytic site into a thermostable scaffold [158].

3.2. Directed Evolution

Directed evolution has accelerated the optimization of polymer-degrading enzymes, providing robust complementarity to rational design. In contrast to rational design, which demands detailed structural and functional insights to guide enzyme engineering, directed evolution operates independent of prior knowledge regarding structure-function relationships, establishing it as an exceptionally versatile approach, particularly valuable for enzymes with limited structural characterization [159].

By simulating natural selection, directed evolution iteratively generates extensive libraries of enzyme variants and screens them to identify enhanced traits. In vitro Polymerase Chain Reaction (PCR)-based methodologies, including error-prone PCR (epPCR), Site Saturation Mutagenesis (SSM) (focused mutagenesis), and recombination-driven DNA shuffling, represent the principal strategies for exploring the mutational landscape [160], although SSM could be considered part of all engineering approaches after identifying hotspots.

However, despite its transformative potential, directed evolution presents inherent challenges. The primary bottleneck in directed evolution experiments lies in identifying improved variants, which heavily depends on high-throughput screening platforms tailored to the specific enzymatic activity under investigation [160,161]. Furthermore, inherent biases introduced by the experimental methodologies (e.g., the preference of Taq polymerase for AT → GC transitions and AT → TA transversions in epPCR) further limit exploration of the mutational landscape. Lastly, even protein libraries containing millions of variants can only probe an infinitesimal fraction of the immense sequence space theoretically available for an average protein [160,162].

3.3. Semi-Rational Design

Semi-rational design leverages computational tools to extract evolutionary insights from homologous proteins based on conserved sequences, structures, and functional data with a focus on creating small, high-quality mutational libraries, allowing for more efficient sampling of sequence space [13]. Databases such as the Protein Data Bank (PDB), UniProt [163], CAZy (Carbohydrate-Active enZYmes) database [164], and PAZy (Plastics-Active enZYmes) database [165] catalog comprehensive protein data including sequences, structures, functional information, and the organism of origin. To capitalize on that, the Basic Local Alignment Search Tool (BLAST) [166] and Many-against-Many sequence searching (MMseqs2) [167] identify homologous proteins through sequence analysis. Recently, advancements in computational structure prediction, as illustrated by AlphaFold 2.0 [127], have enabled tools such as Foldseek to perform rapid and precise structural homolog searches across extensive protein databases [168], often achieving higher sensitivity than sequence-based methods since structural cores evolve slower than sequences [169].

Back-to-consensus design is a semi-rational protein engineering strategy that exploits evolutionary information to enhance protein stability [13,170]. By aligning homologous sequences, conserved residues are identified, reflecting evolutionary pressure for functional importance. Target proteins are mutated to match the consensus sequence, increasing melting temperatures by 10–32 °C. However, not all conserved residues contribute positively to stability, as approximately 50% are stabilizing, 10% are neutral, and 40% are destabilizing, thus requiring precise selection. Full-length de novo sequences using the most frequent residues can also be constructed with some success [13].

Ancestral Sequence Reconstruction (ASR) infers the sequences of ancient proteins by analyzing and comparing the sequences of their modern descendants using phylogenetic methods [13]. The functional and structural properties of ancient proteins often reveal enhanced stability, elevated promiscuity, or functionalities that have been lost or modified in contemporary proteins [171]. ASR has been successfully used by Pfizer Inc. to engineer ene-reductases with enhanced thermostability [172], utilizing the FireProt^ASR tool [173].

The persistence of specific amino acids within proteins of the same family across evolutionary timelines illustrates the need to maintain the protein’s biological activity. ConSurf analyses evolutionary conservation at the residue level, identifying sites under intense selective pressure that are likely important for maintaining structural or functional integrity. The results are presented as a color-coded visualization superimposed on the protein structure, ranging from variable to highly conserved residues [174]. Utilizing this concept, HotSpot Wizard identifies and evaluates evolutionary variable amino acids in the active site or along the access tunnels as key targets for mutagenesis [175]. Concurrently, residue-residue coevolution can be uncovered using GREMLIN [176] and EVcouplings [177] for uncovering epistatic interactions (i.e., where consequences of a mutation in one residue are dependent on the state of another) and has been proposed as a strategy to design smart mutational libraries [178].

Structure-based recombination approaches, such as SCHEMA [179], decompose proteins into structurally compatible fragments, facilitating the generation of chimeric libraries that preserve core folds and minimize misfolding by choosing the least disruptive crossover locations, generating functional chimeric proteins [180]. Similarly, LoopGrafter has demonstrated remarkable efficacy, achieving a 40,000-fold enhancement in the bioluminescence efficiency of the grafted variant relative to the scaffold enzyme of an ancestral dehalogenase-luciferase [118,181].

3.4. Machine Learning and Deep Learning

The integration of ML into protein engineering represents a revolutionary shift in the field, offering an unprecedented capacity to predict structures from sequences with exceptional speed and accuracy, navigate high-dimensional sequence-function landscapes, and generate optimized protein variants that surpass the limitations of traditional protein engineering approaches. Expansive databases, modern model architectures, and present-day hardware have culminated in successful uses of this technology in enzyme engineering, showcasing the transformative potential of ML in streamlining the development of more efficient and stable enzymes [182]. ML encompasses a diverse set of algorithms and techniques for predictive tasks and pattern recognition, including decision trees, support vector machines, k-nearest neighbors, linear regression models, and neural networks (NNs) [183,184].

NNs, inspired by biological neural structures, become “deep” upon incorporating two or more hidden layers [183]. DL, a subset of ML, has transformed data science by enabling breakthroughs in areas such as computer vision, natural language processing (NLP), autonomous systems, and, of course, protein engineering [185]. Deep neural networks construct hierarchical feature representations, wherein layers proximal to the input data capture elementary patterns while successive, deeper layers deduce increasingly abstract and complex features. These architectures inherently demand access to extensive datasets and substantial computational resources to achieve effective model performance [183].

Advancements in accessible hardware and community-driven tools such as ColabFold [186], supported by Google Colab, have greatly enhanced the reach of ML applications. Hugging Face (huggingface.co) offers a comprehensive suite of DL resources, including a vast repository of pre-trained models and datasets spanning diverse domains, including emerging applications such as protein engineering. Their open-source libraries, such as Transformers [187], enable efficient integration with widely used DL frameworks, including PyTorch and TensorFlow, streamlining the construction, training, and deployment of ML models.

3.4.1. ML Paradigms

Supervised learning involves predicting specific properties from labeled datasets by establishing a mapping between inputs (e.g., enzyme sequences or structures) and outputs (e.g., enzyme structures or properties, respectively). This is achieved by minimizing the error between the model’s predictions and the provided labels [188]. For example, a training dataset might include enzyme sequences (inputs), annotated/labeled with experimentally determined characteristics such as structures, or properties such as thermal stability, substrate specificity, or catalytic efficiency (outputs). AlphaFold 2.0 serves as a prominent example of a supervised learning-based approach by utilizing sequences as inputs and experimental structures as labels, with the data originating from PDB, and demonstrates exceptional accuracy in predicting protein structures [127].

Unsupervised learning enables the identification of hidden patterns and relationships in unlabeled data by generating outputs that replicate the given input data and using the errors in these outputs to refine the model. It is commonly applied in tasks such as clustering and representation learning. In representation learning, models are trained to encode data in a format that is beneficial for subsequent tasks by mapping high-dimensional inputs to low-dimensional spaces [189], often as part of pre-training [190]. Pre-trained models can then be adapted or fine-tuned for specific downstream applications through transfer learning. This approach is frequently used with large Protein Language Models (PLMs) to minimize further training requirements and effectively leverage smaller labeled datasets [191]. A prime example is the ESM model from Meta’s Fundamental AI Research Protein Team, which is trained to predict the identity of randomly masked amino acids in protein sequences [192]. These models can be fine-tuned/trained [193], modified [194], or used as feature extractors/encoders [128] for subsequent supervised learning tasks.

Semi-supervised learning is a hybrid approach that combines a small set of labeled data with a larger set of unlabeled data. By leveraging the structure of the unlabeled data, semi-supervised models improve prediction accuracy by learning a better representation of the input, while reducing reliance on extensive experimental annotations, particularly advantageous in protein engineering, where experimental datasets are often limited [184,195].

Reinforcement Learning (RL) emerges as a powerful tool for exploring protein sequence-function landscapes. In the RL setting, an agent proposes actions (e.g., amino acid substitutions or entire protein sequences) and observes feedback (a “reward”) that reflects how well those designs perform (e.g., measured experimentally or predicted by a model). RL methods continually update their strategy (policy) to propose better designs without necessitating a predefined labeled dataset, as the necessary annotations are generated dynamically throughout the experimental process [184]. This paradigm has been successful in simulating directed evolution, either in silico or in combination with in vitro experiments [196,197], and generating compounds predicted to interact effectively with biological targets [198].

3.4.2. Training Datasets

A robust foundation for ML training is provided by accurate and well-structured enzyme databases. PDB, UniProt [163], and other standardized datasets, such as CASP [199], provide sequence and structural data that can be used for representation learning [200], pre-training [192], or training structure predictors [127,128]. NCBI [201], JGI IMG [202], and BacDive [203] databases collectively provide taxonomic, sequence, and cultivation data that have been used in the training of ThermoProt [204] and Preoptem [205], enabling them to classify enzymes as thermophilic or mesophilic. CAZy and PAZy can be used to train classification models for natural and synthetic polymer-degrading enzymes or organisms based on their substrates [206,207]. BRENDA [208] and Sabio-RK [209] catalog kinetic parameters and are used for training kinetic predictors, such as DLKcat [210] and TurNuP [211], while EnzymeML provides a framework for standardizing kinetic data exchange [212]. For stability, SAPPHIRE [213], ProThermDB [214], and FireProtDB [215] offer sequence and mutation-specific thermostability data, enabling tools such as DeepDDG [85] and ThermoMPNN [216]. BindingDB [217] and PDBbind [218] catalog protein-ligand affinity data, SoluProtMutDB [219] curates mutational data on soluble expression, the SignalP 6.0 [220] dataset supports signal peptide detection, and MutaDescribe [221] provides rich textual annotations for the effects of mutations on proteins. Additionally, databases such as GotEnzymes [222] and AlphaFoldDB [223] have initiated the systematic organization of predictions derived from AI tools, thereby streamlining access to experimentalists and facilitating deeper engagement with the field.

3.4.3. Model Architectures

Traditional ML models, such as random forests [224] and gradient boosting [225], remain valuable for tasks where well-labeled and moderately sized datasets prevail. However, recent breakthroughs in DL model architectures have largely amplified the impact of such databases. Convolutional Neural Networks (CNNs) are a type of DL architecture characterized by hidden layers that are locally connected to subsequent layers through convolutional filters (also called kernels), traditionally used for computer vision. This local connectivity enables CNNs to efficiently extract local features, which are then hierarchically combined into more complex representations [226]. In the field of protein engineering, CNNs have demonstrated great predictive power and capability in learning the fitness landscape of proteins [227]. CNNs have been applied in binding site detection [228], optimal amino acid prediction [111], thermostability estimation [205], and de novo protein design [229], addressing the inverse folding problem.

In recent years, borrowing techniques from NLP, pre-trained PLMs, such as ProtBERT [191] and ESM-2 [128], have leveraged transformer-based architectures with multi-head attention mechanisms. The attention mechanism enables these models to selectively focus on the most relevant aspects of the input by assigning varying levels of importance, or “weights”, to different parts of the sequence, thus allowing the models to effectively capture both local and global dependencies across the entire input sequence through the use of multiple attention heads [230]. By learning these patterns, PLMs have become highly effective for predicting protein structures, annotating functions, and assessing the effects of mutations. For example, ProtBERT has been applied in tools such as SignalP 6.0 [220] and BertThermo [231], ESM-2 powers applications such as ESMFold [128] and PepMLM [232], ProtGPT2 enables de novo protein design [233] and, while not technically a PLM, AlphaFold 2.0 is a prominent example of a model that applies attention mechanisms within its Evoformer block to accurately predict protein structures from their sequences [127]. Transformer-based PLMs have largely replaced CNNs in popularity due to their often superior performance. However, it is important to note that CNNs can still be competitive and, in specific contexts, outperform transformer-based architectures [234,235,236].

In parallel with these developments, diffusion models have recently emerged as promising generative approaches that iteratively refine random noise into structured and coherent outputs and were originally popularized for generating images from text prompts [237,238]. Their applications span docking [237], de novo design with RFdiffusion [238], NNPs [239], and structure prediction with AlphaFold 3 [240]. Graph Neural Networks (GNNs), on the other hand, treat proteins as graphs, where amino acid residues (or atoms) serve as nodes and edges represent interactions or spatial proximity [241]. As a result, GNN-based methods have shown promise in tasks such as predicting protein-protein interactions [241] and protein solubility [242] in NNPs [243] and de novo sequence design from structure in ProteinMPNN [126,244].

3.4.4. Interpreting ML Models

Interpreting the predictions of ML models in protein engineering is important for uncovering insights into fundamental protein sequence-function or structure-function relationships. Traditional ML models, such as decision trees, random forests, and linear regression, are inherently interpretable. These models clearly show how input features influence predictions, whether through detailed decision pathways, feature importance metrics, or coefficients, making them highly useful for exploratory research [245]. However, understanding the predictions of DL models, which are generally more accurate, can be difficult because of their large size and complex architecture.

CNNs process input data by extracting features using filters, focusing on key regions that significantly impact the output. During training, these filters identify patterns from the input dataset important to predictions, thereby elucidating the relationship between input features, such as structure, and predictive outcomes, such as function [246]. Likewise, attention mechanisms in transformer-based models also enable the identification of critical input regions influencing predictions, by analyzing the attention scores given to each input token, providing insights into the relationship between the input data and the prediction, thereby also enabling the visualization of sequence-function relationships [247]. Unsupervised techniques, such as Sparse Autoencoders, aid in extracting latent representations by compressing high-dimensional data into lower-dimensional, interpretable forms, uncovering patterns across datasets. For example, InterPLM extracted 143 biological concepts (e.g., functional domains and structural motifs) learned by the PLM ESM-2 from its unsupervised training [248]. Additionally, recent advancements and the incorporation of Chain-of-Thought (CoT), prompting strategies in systems such as MutaPLM [221], have demonstrated progress in providing human-readable step-by-step explanations for mutational effects.

4. Lessons from the Industrial Application of Engineered PETases

The escalating issue of plastic waste accumulation has driven significant research into enzymatic strategies for plastic degradation, as traditional waste management techniques, such as incineration and landfill disposal, not only fail to address the scale of this issue but also contribute to additional environmental concerns, including greenhouse gas emissions and microplastic soil contamination [249,250]. Furthermore, traditional thermomechanical recycling methods degrade plastics, such as PET, due to chain breakage, crystallinity increase, and chemical degradation of their building blocks [16]. In this context, enzymes capable of catalyzing the breakdown of polymeric materials offer a sustainable and eco-friendly alternative for addressing the plastic waste crisis [12]. PETases stand out as efficient and potentially transformative biocatalysts for tackling PET pollution and supporting sustainable material reuse in a circular economy [251], with an already industrialized application made possible by protein engineering of LCC [3,4,18].

4.1. Biocatalysis of PET

Enzymes responsible for PET depolymerization are part of the Enzyme Commission number (EC) 3.1.1 class of carboxylic ester hydrolases [208] and feature a catalytic triad (Ser-His-Asp) typical of the α/β hydrolase superfamily. PET degradation occurs in a hydrophobic cleft on the surface of PETases, which facilitates interaction with the polymer. The hydrolysis of ester bonds begins with the catalytic serine’s oxygen atom attacking the carbonyl carbon of the scissile ester bond, leading to bond cleavage (Figure 2) [18]. PETases, either alone or synergistically with mono(2-hydroxyethyl) terephthalate (MHET) hydrolases (MHETases), fully depolymerize PET to its monomers, i.e., ethylene glycol and terephthalic acid [252].

The first highly efficient PET depolymerase, Thermobifida fusca cutinase (TfCut), was reported in 2005 [18,253], bringing significant attention to cutinases and lipases for PET bio-recycling. Since then, other PET-degrading enzymes have been isolated from various taxonomic groups, with benchmark LCC [254] and IsPETase [252] being the most widely studied and cited in the field. LCC is recognized as the most effective WT PETase at temperatures exceeding the glass transition temperature of PET (~70 °C), while IsPETase demonstrates the best performance at moderate temperatures (<45 °C) [255], with melting temperatures ($T_m$) of ~84.7 °C and ~46.4 °C, respectively [4].

Protein engineering has played a pivotal role in transitioning PETases from an academic curiosity focus to industrially relevant targets. WT PETases, while effective at degrading PET under laboratory conditions, exhibited limitations such as suboptimal catalytic efficiency and low thermostability at industrially relevant temperatures, as well as poor activity on semi-crystalline PET, rendering them unsuited for industrial applications. Through iterative rounds of rational design, directed evolution, semi-rational design, and ML, these challenges are being systematically addressed [256].

4.2. Protein Engineering of PETases

Engineered variants of benchmark enzymes such as IsPETase and LCC have demonstrated outstanding performance in terms of catalytic activity and durability, making them viable candidates for industrial deployment, highlighting the transformative potential of the aforementioned protein engineering approaches in addressing diverse modern challenges through optimized or completely novel enzymatic systems [3]. Table 2 showcases notable applications of protein engineering in PETase optimization. In column 3 (targeted properties), the first property mentioned corresponds to the main target of each study, while the rest were of secondary consideration to the researchers; this emphasizes the synergistic integration of multiple protein engineering strategies for optimal results. Figure 6 highlights all residues of IsPETase and LCC modified in the studies presented in Table 2, demonstrating their diverse positions, not solely concentrated close to the active site but all over the enzyme, inside and outside.

Rational design examples are plentiful in PETase engineering, mostly aiming for increased stability and modulating PET binding affinity. The most influential PETase engineering study is the engineering of LCC to LCC^ICCG (Table 2, study 10), which was later industrialized for PET bio-recycling. In this study, a disulfide bond was introduced at sites D238C and S283C to increase stability, which resulted in a ${\mathsf{\Delta }T}_m$ = +10 °C, albeit with a 28% decreased activity (LCC^CC). Point mutation F243I on LCC^CC regained activity 22% higher than WT but decreased $\mathsf{\Delta }{T}_m$ to +6.6 °C (LCC^ICC). The latter was proposed by identifying hotspots through in-silico docking studies, SSM, and experimental screening. Finally, Y127G from the same docking and SSM study did not affect activity but increased melting temperature, resulting in LCC^ICCG and demonstrating ${\mathsf{\Delta }T}_m$ = +9.3 °C and 82% PET conversion in 20 h compared to 53% of WT at 72 °C [4]. Study 11 (LCC^ICCG-RIP) focused on (i) introducing proline residues, (ii) introducing hydrophilic interactions on the surface, and iii) increasing internal hydrophobic interactions to further increase the stability of LCC^ICCG [257]. The GRAPE strategy, which includes $\mathsf{\Delta }\mathsf{\Delta }{G}_f$ calculations with FoldX and Rosetta, among others, for identification of stabilizing mutations, was utilized to engineer DuraPETase (Table 2 study 6), an IsPETase variant, and TurboPETase (Table 2 study 16), a BhrPETase variant [258,259]. For engineering substrate affinity, Studies 8 (CombiPETase), 9 (TS-ΔIsPET), and 14 (LCC-A2) all employed docking methodologies to identify hotspot residues in the active site of the respective enzymes, significantly increasing activity in all cases [257,260,261].

Directed evolution approaches for evolving IsPETase stability are represented by studies 1 and 2 of Table 2, which resulted in a significant increase in $T_m$ with multiple mutations [262,263]. In the case of DepoPETase, semi-rational design was also used to further identify mutation R260Y via focused SSM of positively charged amino acids on the opposite side of the substrate-binding pocket, and D186H and N233K were obtained from the literature, further refining the obtained variant [263].

Semi-rational design approaches were also used for engineering IsPETase (Table 2 studies 5, 7, 8, and 9) and LCC (Table 2 study 12). Study 3 identified mutations S121D/D186H for IsPETase based on structural comparison with TfCut, where these residues generate a hydrogen bond that stabilizes the β6-β7 loop [264]. In study 8, an ASR approach was used to identify IsPETase mutation K95N, which exhibited an increase in thermal stability and activity [260]. In study 5, eight hotspots were identified in the binding site of IsPETase by examining four homologs (smart library) and deeming them variable. After focused mutagenesis, a combinatorial approach and insights from the literature yielded the final variant that exhibited 58-fold increased activity at 37 °C compared to WT [265]. Studies 7 and 9 employed similar concepts, utilizing IsPETase’s homologs to calculate the likelihood and conservation of amino acids in specific positions, respectively. In the first case (study 7), the Premuse tool was developed to calculate position-specific amino acid probabilities from a library of homologs, guiding mutation selection and generating a variant with $\mathsf{\Delta }{T}_m=+10.4°\mathrm{C}$ and 40-fold activity increase at 40 °C in 24 h [266]. In the later study (study 9), conservation analysis was used to avoid the substitution of highly conserved residues using Rate4Site [86,261]. Additionally, study 12 identified hotspots on LCC^ICCG based on two approaches. First, a conservation scheme categorized 4203 homologous proteins into high and low-temperature datasets, based on scoring from the DL-based tool Preoptem, and determined the probability of amino acids at each position, identifying 18 candidate mutations that were not only conserved in the high-temperature dataset but also absent from the low-temperature dataset and the target protein. The second approach followed a coevolutionary scheme utilizing EVcouplings to identify hotspots, which were subsequently screened through EVmutation and Preoptem scoring functions, further identifying 18 additional mutations. Experimental validation independently screened six beneficial mutations and LCC^ICCG_I6M. Incorporating all six demonstrated an increased $T_{opt}$ from 65 °C to 75–80 °C for 39% crystalline PET [267].

Machine learning has been used to improve BhrPETase (Table 2, study 16), IsPETase (Table 2, studies 4, 6, and 7), and LCC (Table 2, study 12). In the case of BhrPETase, a PLM, trained with a masked language modeling objective on ~26,000 homologous sequences to predict the real amino acid at the masked position, was used to suggest mutations that improved activity but resulted in decreased thermal stability. After the rational design of stabilizing mutations, the final variant, with a $T_m=84°\mathrm{C}$ and a 3.4-fold improvement in specific activity towards PET films compared to WT, was obtained [259]. In the case of IsPETase variant FAST-PETase (study 4), the CNN-based MūtCompute, trained to predict the masked amino acid at the center of a chemical environment extracted from a protein structure [268], was used to obtain a discrete probability distribution for the structural fit of all 20 canonical amino acids at every position, identifying mutations S121E, T140D, R224Q, and N233K and combinatorically assembling them across IsPETase, Thermo-PETase, and DuraPETase to obtain the best variant containing N233K and R224Q on top of Thermo-PETase [111].

Other non-typical approaches include: (i) study 15, in which LCC was expressed in Pichia pastoris, increasing thermal stability and activity through the introduction of N-glycosylation [117], (ii) study 18, in which an active site loop from LCC was grafted to Mors1, resulting in a shift in optimal temperature from 25 °C to 45 °C and a 5-fold increase in PET hydrolysis compared with WT at 25 °C [122], (iii) study 19, in which a PETase was designed de novo [269], and (iv) study 20, in which IsPETase was fused with IsMHETase, separated with a linker, improving turnover relative to the free enzymes [270]. An unconventional but notable filter in study 14 (LCC-YGA) incorporated a correlation-based accumulated mutagenesis (CAM) strategy that accounts for the amino acids exhibiting highly correlated or anti-correlated motions. Through MD simulations, the correlation is quantified by the covariance between the fluctuations of two atoms. Mutations are then introduced in regions with little cross-correlated dynamics [271].

Table 2. Protein engineering examples for enhancing PETases.

#	Enzyme	Targeted Properties	Engineering Strategies	Modifications	Results	Ref
1	HotPETase	stability, activity	DE	IsPETase variant: S121E, D186H, R280A, P181V, S207R, S214Y, Q119K, S213E, N233C, S282C, R90T, Q182M, N212K, R224L, S58A, S61V, K95N, M154G, N241C, K252M, T270Q	${\mathsf{\Delta }T}_m$ = +35.5 °C	[262]
2	DepoPETase	stability, affinity, activity	DE, SRD (focused surface charge mutations with SSM), literature	IsPETase variant: T88I, D186H, D220N, N233K, N246D, R260Y, S290P	${\mathsf{\Delta }T}_m$ = +23.3 °C 1407-fold more products towards amorphous PET film at 50 °C	[263]
3	Thermo-PETase	stability, activity	RD (structure-based approach), SRD (adopting features from homolog TfCUT), literature	IsPETase variant: S121E, D186H, R280A	${\mathsf{\Delta }T}_m$ = +8.81 °C activity enhanced by 14-fold at 40 °C	[264]
4	FAST-PETase	stability	ML	Thermo-PETase variant: N233K, R224Q	2.4- and 38- fold higher activity at 40 and 50 °C, respectively, compared to ThermoPETase	[111]
5	IsPETase variant	affinity, stability	SRD (smart libraries from homologs), literature	IsPETase variant: S121E, D186H, S242T, N246D (based on Thermo-PETase)	${\mathsf{\Delta }T}_m$ = +12 °C 58-fold increased activity at 37 °C	[265]
6	DuraPETase	stability, activity	$\mathrm{RD}(\mathsf{\Delta }\mathsf{\Delta }{G}_f$)	IsPETase variant: S214H, I168R, W159H, S188Q, R280A, A180I, G165A, Q119Y, L117F, T140D	Enhanced degradation performance (300-fold) on semicrystalline PET films at 40 °C	[258]
7	IsPETase variant	stability, activity	SRD (position-specific amino acid probabilities)	IsPETase variant: W159H, F229Y	${\mathsf{\Delta }T}_m$ = +10.4 °C 40-fold activity increase at 40 °C in 24 h	[266]
8	CombiPETase	affinity, stability, activity	RD (MD, engineering flexibility engineering, disulfides, hydrophobic core packing, hydrogen bond breaking), SRD (ASR), literature	IsPETase variant: K95N, S136E, A179C, D186A, S214T, N233C, S282C	${\mathsf{\Delta }T}_m$ = +27.2 °C 4.25-fold increased activity when compared to WT at their respective $T_{opt}$ 24.6-fold increased protein yield	[260]
9	TS-ΔIsPET	activity, affinity, stability	RD (identifying hotspots through protein-ligand interaction analysis with MD, rational mutations, salt bridge), SRD (conservation analysis followed by SSM), literature	IsPETase variant: S121E, W159H, D186H, F238A	${\mathsf{\Delta }T}_m$ = +4.9 °C Increased catalytic activity on PET	[261]
10	LCCICCG	stability	RD (docking to identify hotspots followed by SSM, disulfide design)	LCC variant: F243I, D238C, S283C, Y127G	${\mathsf{\Delta }T}_m$ = +9.3 °C 82% PET conversion in 20 h compared to 53% of WT at 72 °C	[4]
11	LCCICCG_RIP	stability	RD (proline residues, hydrophilic surface, hydrophobic core)	LCCICCG variant: A59R, V63I, N248P	More products at 85 °C	[272]
12	LCCICCG_I6M	activity, stability	ML, SRD (coevolutionary analysis)	LCCICCG variant: S32L, D18T, S98R, T157P, E173Q, N213P	$T_{opt}$ for 39% crystalline PET increased from 65 °C to 75–80 °C	[267]
13	LCC-A2	affinity	RD (docking)	LCCICCG variant: H218Y, N248D	${\mathsf{\Delta }T}_m$ = +1.11 °C Increased relative activity by 80.1% at 78 °C compared to LCCICCG	[257]
14	LCC-YGA	affinity, activity	RD (remodeling hydrophilicity of binding site, correlation based accumulated mutagenesis strategy), SRD (homolog information), literature	LCCICCG variant: H183Y, L124G, S29A	2.07-fold hydrolytic activity of LCCICCG	[271]
15	LCC-G	stability	RD (glycosylation)	Introduction of N-linked glycosylation at sites N197, N239, and N266 by expressing WT LCC in Pichia pastoris	$\mathrm{Increased}{T}_m$, at 70 and 75 °C, 1.6- and 1.2-fold more active, respectively	[117]
16	TurboPETase	stability, activity	$\mathrm{ML},\mathrm{RD}(\mathsf{\Delta }\mathsf{\Delta }{G}_f$), literature	BhrPETase variant: H218S, F222I, W104L, F243T, A209R, D238K, A251C, A281C	$T_m$= 84 °C and a 3.4-fold improvement in specific activity towards GF-PET films	[259]
17	Est1 variant	stability	SRD (consensus design)	Est1 variant A68V, T253P	$\mathrm{Increased}{T}_m$ and activity	[273]
18	Mors1 chimera	activity	SRD (loop exchange)	Loop exchange of an active site loop from LCC	Shift in optimal temperature from 25 °C to 45 °C, increase 5x in PET hydrolysis when compared with WT at 25 °C.	[122]
19	HSH-25	De novo PETase activity	RD (de novo)	De novo design of a 25 amino acid thermostable peptide capable of depolymerizing PET	Confirmed degradation of PET	[269]
20	IsPETase-IsMHETase chimera	activity	RD (fusion with linker to achieve synergistic action)	Construction of a bifunctional chimeric enzyme fusion of IsPETase with IsMHETase	Chimeric proteins of varying linker lengths all exhibit improved turnover relative to the free enzymes	[270]

Note: DE: directed evolution, ASR: ancestral sequence reconstruction, BhrPETase: bacterium HR29 polyethylene terephthalate hydrolase, Est1: Thermobifida alba AHK119 cutinase, IsMHETase: Ideonella sakaiensis mono(2-hydroxyethyl) terephthalate hydrolase, IsPETase: Ideonella sakaiensis PETase; LCC: leaf and branch compost cutinase, ML: machine learning, Mors1: Moraxella sp. TA144 cutinase, RD: rational design, SRD: semi-rational design, SSM: site saturation mutagenesis, TfCUT: Thermobifida fusca cutinase.

Table 2. Protein engineering examples for enhancing PETases.

#	Enzyme	Targeted Properties	Engineering Strategies	Modifications	Results	Ref
1	HotPETase	stability, activity	DE	IsPETase variant: S121E, D186H, R280A, P181V, S207R, S214Y, Q119K, S213E, N233C, S282C, R90T, Q182M, N212K, R224L, S58A, S61V, K95N, M154G, N241C, K252M, T270Q	${\mathsf{\Delta }T}_m$ = +35.5 °C	[262]
2	DepoPETase	stability, affinity, activity	DE, SRD (focused surface charge mutations with SSM), literature	IsPETase variant: T88I, D186H, D220N, N233K, N246D, R260Y, S290P	${\mathsf{\Delta }T}_m$ = +23.3 °C 1407-fold more products towards amorphous PET film at 50 °C	[263]
3	Thermo-PETase	stability, activity	RD (structure-based approach), SRD (adopting features from homolog TfCUT), literature	IsPETase variant: S121E, D186H, R280A	${\mathsf{\Delta }T}_m$ = +8.81 °C activity enhanced by 14-fold at 40 °C	[264]
4	FAST-PETase	stability	ML	Thermo-PETase variant: N233K, R224Q	2.4- and 38- fold higher activity at 40 and 50 °C, respectively, compared to ThermoPETase	[111]
5	IsPETase variant	affinity, stability	SRD (smart libraries from homologs), literature	IsPETase variant: S121E, D186H, S242T, N246D (based on Thermo-PETase)	${\mathsf{\Delta }T}_m$ = +12 °C 58-fold increased activity at 37 °C	[265]
6	DuraPETase	stability, activity	$\mathrm{RD}(\mathsf{\Delta }\mathsf{\Delta }{G}_f$)	IsPETase variant: S214H, I168R, W159H, S188Q, R280A, A180I, G165A, Q119Y, L117F, T140D	Enhanced degradation performance (300-fold) on semicrystalline PET films at 40 °C	[258]
7	IsPETase variant	stability, activity	SRD (position-specific amino acid probabilities)	IsPETase variant: W159H, F229Y	${\mathsf{\Delta }T}_m$ = +10.4 °C 40-fold activity increase at 40 °C in 24 h	[266]
8	CombiPETase	affinity, stability, activity	RD (MD, engineering flexibility engineering, disulfides, hydrophobic core packing, hydrogen bond breaking), SRD (ASR), literature	IsPETase variant: K95N, S136E, A179C, D186A, S214T, N233C, S282C	${\mathsf{\Delta }T}_m$ = +27.2 °C 4.25-fold increased activity when compared to WT at their respective $T_{opt}$ 24.6-fold increased protein yield	[260]
9	TS-ΔIsPET	activity, affinity, stability	RD (identifying hotspots through protein-ligand interaction analysis with MD, rational mutations, salt bridge), SRD (conservation analysis followed by SSM), literature	IsPETase variant: S121E, W159H, D186H, F238A	${\mathsf{\Delta }T}_m$ = +4.9 °C Increased catalytic activity on PET	[261]
10	LCCICCG	stability	RD (docking to identify hotspots followed by SSM, disulfide design)	LCC variant: F243I, D238C, S283C, Y127G	${\mathsf{\Delta }T}_m$ = +9.3 °C 82% PET conversion in 20 h compared to 53% of WT at 72 °C	[4]
11	LCCICCG_RIP	stability	RD (proline residues, hydrophilic surface, hydrophobic core)	LCCICCG variant: A59R, V63I, N248P	More products at 85 °C	[272]
12	LCCICCG_I6M	activity, stability	ML, SRD (coevolutionary analysis)	LCCICCG variant: S32L, D18T, S98R, T157P, E173Q, N213P	$T_{opt}$ for 39% crystalline PET increased from 65 °C to 75–80 °C	[267]
13	LCC-A2	affinity	RD (docking)	LCCICCG variant: H218Y, N248D	${\mathsf{\Delta }T}_m$ = +1.11 °C Increased relative activity by 80.1% at 78 °C compared to LCCICCG	[257]
14	LCC-YGA	affinity, activity	RD (remodeling hydrophilicity of binding site, correlation based accumulated mutagenesis strategy), SRD (homolog information), literature	LCCICCG variant: H183Y, L124G, S29A	2.07-fold hydrolytic activity of LCCICCG	[271]
15	LCC-G	stability	RD (glycosylation)	Introduction of N-linked glycosylation at sites N197, N239, and N266 by expressing WT LCC in Pichia pastoris	$\mathrm{Increased}{T}_m$, at 70 and 75 °C, 1.6- and 1.2-fold more active, respectively	[117]
16	TurboPETase	stability, activity	$\mathrm{ML},\mathrm{RD}(\mathsf{\Delta }\mathsf{\Delta }{G}_f$), literature	BhrPETase variant: H218S, F222I, W104L, F243T, A209R, D238K, A251C, A281C	$T_m$= 84 °C and a 3.4-fold improvement in specific activity towards GF-PET films	[259]
17	Est1 variant	stability	SRD (consensus design)	Est1 variant A68V, T253P	$\mathrm{Increased}{T}_m$ and activity	[273]
18	Mors1 chimera	activity	SRD (loop exchange)	Loop exchange of an active site loop from LCC	Shift in optimal temperature from 25 °C to 45 °C, increase 5x in PET hydrolysis when compared with WT at 25 °C.	[122]
19	HSH-25	De novo PETase activity	RD (de novo)	De novo design of a 25 amino acid thermostable peptide capable of depolymerizing PET	Confirmed degradation of PET	[269]
20	IsPETase-IsMHETase chimera	activity	RD (fusion with linker to achieve synergistic action)	Construction of a bifunctional chimeric enzyme fusion of IsPETase with IsMHETase	Chimeric proteins of varying linker lengths all exhibit improved turnover relative to the free enzymes	[270]

Note: DE: directed evolution, ASR: ancestral sequence reconstruction, BhrPETase: bacterium HR29 polyethylene terephthalate hydrolase, Est1: Thermobifida alba AHK119 cutinase, IsMHETase: Ideonella sakaiensis mono(2-hydroxyethyl) terephthalate hydrolase, IsPETase: Ideonella sakaiensis PETase; LCC: leaf and branch compost cutinase, ML: machine learning, Mors1: Moraxella sp. TA144 cutinase, RD: rational design, SRD: semi-rational design, SSM: site saturation mutagenesis, TfCUT: Thermobifida fusca cutinase.

Noteworthily, optimizing both the enzyme’s active site and distal regions is crucial for enhancing catalytic efficiency and overall stability (Figure 6). For instance, incorporation of disulfide bonds, proline residues, and other stability-enhancing mutations consistently raises the melting temperature and broadens the operational range of PETases, typically without overly compromising enzymatic activity. Additionally, rational and semi-rational approaches synergize effectively with directed evolution and ML-driven approaches to pinpoint beneficial substitutions at a scale and speed not achievable through trial-and-error alone. The most comprehensive studies integrate multiple methodologies and systematically rationalize obtained results, even when they initially manifest as random changes.

5. Conclusions and Prospects

This paper presents a comprehensive review of protein engineering strategies aimed at enhancing enzyme performance for industrial applications, focusing on key principles, methods, and lessons learned from industrialized PETases. These efforts illustrate the transformative potential of protein engineering in addressing industrial and environmental challenges.

Interdisciplinary collaborations that combine computational enzyme design groups with experimentalists and dedicated AI researchers hold the potential to unlock new frontiers in protein engineering. Looking forward, the integration of advanced DL frameworks, such as diffusion models and PLMs, offers exciting prospects for accelerating enzyme design and bridging the gap between laboratory innovation and industrial implementation, potentially rendering the protein engineering problem trivial through text prompts to fully functional designs, just like recent text-to-image models.

Despite their successes, the strategies reviewed exhibit notable limitations. Rational design, while highly effective, relies on detailed structural and mechanistic data, which are not always available or straightforward to obtain. Directed evolution, although powerful, is constrained due to the vast number of potential sequence combinations, experimental biases, and the need for efficient, high-throughput screening methods. Semi-rational design remains reliant on comprehensive evolutionary insights which may not always be available or translate effectively into smart library designs. ML approaches show substantial promise but often require large, high-quality datasets and considerable computational resources, which can limit their practical application.

Scaling these strategies for industrial applications presents additional hurdles. All of these approaches require significant investments in time, expertise, and infrastructure, which may not always align with the cost-sensitive nature of industrial biotechnology. While engineered enzymes may demonstrate significantly enhanced activity, stability, and specificity, their overall production costs, including research, design, scale-up, and the expected return on investment from societal, environmental, and financial perspectives must be carefully evaluated to ensure economic viability. Future efforts should prioritize addressing scalability and cost-effectiveness, alongside a more holistic evaluation of engineered enzymes’ lifecycle impacts. Addressing these factors will help establish protein engineering as a foundation for versatile and innovative industrial biotechnology.