A Mechanistic Understanding of the Activity-Stability Trade-off in PETase

A Mechanistic Understanding of the Activity-Stability Trade-off in PETase | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A Mechanistic Understanding of the Activity-Stability Trade-off in PETase Shuang Chen , Ekram Akram , Weili Qiao , Yifei Zhang , View ORCID Profile Shozeb Haider , Yufei Cao doi: https://doi.org/10.1101/2024.06.09.598049 Shuang Chen 1 Lab of Applied Biocatalysis, School of Food Science and Technology, South China University of Technology , Guangzhou 510640, Guangdong, China 2 Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College London , London WC1N 1AX, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ekram Akram 3 State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology , Beijing 100029, China 4 Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology , Beijing 100029, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weili Qiao 1 Lab of Applied Biocatalysis, School of Food Science and Technology, South China University of Technology , Guangzhou 510640, Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yifei Zhang 3 State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology , Beijing 100029, China 4 Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology , Beijing 100029, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: yufeicao{at}scut.edu.cn shozeb.haider{at}ucl.ac.uk yifeizhang{at}mail.buct.edu.cn Shozeb Haider 2 Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College London , London WC1N 1AX, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shozeb Haider For correspondence: yufeicao{at}scut.edu.cn shozeb.haider{at}ucl.ac.uk yifeizhang{at}mail.buct.edu.cn Yufei Cao 1 Lab of Applied Biocatalysis, School of Food Science and Technology, South China University of Technology , Guangzhou 510640, Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: yufeicao{at}scut.edu.cn shozeb.haider{at}ucl.ac.uk yifeizhang{at}mail.buct.edu.cn Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Enzymatic degradation of poly(ethylene terephthalate) (PET) has garnered considerable interest in plastic recycling efforts. Despite numerous descriptions of both natural and engineered enzymes, the fundamental mechanism underlying PETase-catalyzed PET depolymerization at the solid-liquid interface remains elusive. This lack of understanding hampers the rational design of highly efficient depolymerases. Here, we employ multiscale simulations and experiments to elucidate the complete catalytic pathway of Is PETase, from enzyme adsorption at the interface to PET fragment capture, conformational refinement, and ester bond cleavage. Both endo- and exo-cleavage modes of the enzyme are identified, indicating its capacity for endo- and exo-lytic activities. We discover that the trade-off between the activity and stability of Is PETase’s PET-capturing pliers brings compromises to its PET depolymerization performance. Reshaping the loop dynamics of the enzyme can break this trade-off and enhance its stability and activity simultaneously, as demonstrated by the evolved variant HotPETase. Overall, our study offers comprehensive details into how PETase functions at the interface and provides valuable insights for engineering efficient plastic-degrading enzymes. Introduction The global environmental crisis caused by plastic waste has driven extensive research into finding sustainable solutions for plastic recycling and upcycling 1 - 4 . Among these efforts, the enzyme-catalyzed deconstruction of poly(ethylene terephthalate) (PET) has emerged as a promising approach 5 , due to its mild pH and relatively lower temperature conditions for recycling PET into monomers. Numerous natural and engineered enzymes, known as PET hydrolase or PETase, have been reported for PET biodegradation 4 , 6 - 10 . PETase specifically targets the ester bonds of the PET chain, facilitating monomer recovery. Unlike most enzymes that function in homogeneous aqueous environments, PETase operates at the solid-liquid interface, presenting a significant challenge for PETase engineering and mechanistic studies. Most strategies for enhancing PETase efficiency and elucidating its catalytic mechanism rely on unrealistic substrate fragments 4 , 11 - 14 . Despite extensive experimental and theoretical research, the mechanism underlying PETase-catalyzed PET depolymerization at the solid-liquid interface, including polymeric substrate accessibility, enzyme-substrate complexation, its endolytic or exolytic activity, and enzyme stability, remains poorly understood 5 . The detailed interactions between macromolecular PET substrates and enzyme have yet to be reported, posing a significant barrier to the rational design of highly efficient engineered PETase. Prior to the discovery of PETase, a class of enzymes called cellulases that also hydrolyze insoluble polymeric substrates had been extensively studied 15 . For instance, exocellulases target the crystalline regions of cellulose fibers, exhibit processive catalysis, and are directionally dependent when cleaving two to four units from the ends of exposed chains 16 . A cellulase typically consists of a carbohydrate-binding module (CBM), a larger catalytic domain, and a linker domain connecting the two 17 , 18 . Unlike cellulases, natural PETase lacks a polymer-binding module and primarily degrades the amorphous region of PET 9 . Consequently, PETase does not function in a processive and directional manner like cellulase. Furthermore, grafting cellulase’s CBM onto PETase has been shown to have minimal impact on its efficiency 19 . The mode of PET degradation by PETase, whether endo- or exo-type, remains debatable. Mass spectrometry analysis suggests endo-type hydrolysis 20 , 21 , while NMR spectroscopy indicates both endo- and exo-type activities 22 . A recent study by Bell et al. proposed an exo-cleavage mechanism, supported by minimal changes in the molecular weight and dispersity of residual PET 9 . To reveal the mechanism of PET biodegradation, we established a realistic PET-water interface model to investigate enzyme adsorption, conformational changes, and ester bond recognition using Idonella sakaiensis PETase ( Is PETase 6 ) as a model enzyme. Our findings revealed that Is PETase exhibits non-specific binding to the PET surface, which could lead to enzyme inactivation due to the inherent instability of the catalytic loop, as confirmed by experimental evidence. Through extensive sampling, we identified both endo- and exocleavage conformations of enzymes and elucidated the detailed processes of ester bond recognition. By comparing Is PETase with the evolved thermostable variant HotPETase, we demonstrated how directed evolution breaks the activity-stability trade-off and enhances the PET depolymerization performance of the enzyme. This trade-off appears to be universal in PETases, as evidenced by studies on engineered leaf-branch compost cutinase (LCC), another extensively investigated PETase. Our work provides guiding principles for modulating loop dynamics in order to create robust plastic-degrading enzymes. Results and Discussion PET substrate recognition by Is PETase We began by constructing a solid-liquid interface model to investigate the mechanism, incorporating an amorphous PET membrane and water (Supplementary Fig. 1-5). An unbiased protocol of supervised molecular dynamics (MD) simulations was developed to sample the binding states of enzymes on the PET surface (see details in the Methods section). During supervised MD, the enzyme gradually approached and eventually bound to the PET surface (Supplementary Fig. 6). After over 100 repeated samplings, we examined the distribution of enzyme adsorption states on the PET surface ( Fig. 1a ). The Is PETase adsorption did not display obvious specificity, as in most cases, the active sites of enzymes were not oriented towards the PET surface. The binding free energy (ΔG binding ) distribution illustrates that the average ΔG binding values of active states were slightly higher than those of the non-active states, further confirming the limited targeted binding capacity of Is PETase ( Fig. 1b-c and Supplementary Fig. 7-8). Download figure Open in new tab Figure 1. PET recognition by Is PETase at a solid-water interface. (a) Various binding states of Is PETase on the PET surface, with the catalytic triad shown in a ball representation. (b) Distribution of binding free energy across all binding states. (c) Binding free energy of active and non-active binding states. Binding was defined as “active” if the distance between Ser160 and the PET surface was less than 5.5 Å in the final binding states. (d) Detailed procedure of ester bond recognition by Is PETase. Ser160 and the captured PET fragments are shown in ball and stick representations, respectively. (e) Evolution of the distance between the side chain hydroxyl of Ser160 and the carbon atom of the recognized PET ester bond. The final pre-cleavage conformation was illustrated. (f) Endolytic and exolytic pre-cleavage modes of Is PETase. The simulations depicted the detailed mechanism of ester bond recognition by the enzyme ( Fig. 1d-e , Supplementary Fig. 9-11), which involves four steps: enzyme binding, PET fragment capture, conformational refinement, and attainment of the final pre-cleavage conformation. Throughout this process, the distance between Ser160 in the active site and the ester bond of PET gradually decreased. Residues within the loops near the active site, including Tyr87, Leu117, Trp159, Met161, Trp185, and Ile208 played a crucial role in capturing the PET fragments (Supplementary Fig. 11). This was consistent with experimental results (Supplementary Fig. 11d). Notably, Tyr87, Trp185 and Ile208, located on two loops, acted as “pliers” for capturing PET ( Fig. 1e ). We identified two distinct PET-capturing states of Is PETase, the endolytic and exolytic modes, indicating that Is PETase possesses both endolytic and exolytic activities ( Fig. 1f , Supplementary Fig. 12-13). This suggests that both two catalytic modes function cooperatively during PET depolymerization. The trade-off between capturing flexibility and stability Upon monitoring the catalytic triad conformation of Is PETase during PET recognition, we observed three types of conformational dynamics: stable, fluctuated, and destroyed ( Fig. 2a-b , Supplementary Fig. 14-15). Thus, PET binding could perturb the conformation of Is PETase and even lead to enzyme inactivation. The disrupted states of the enzyme can potentially be restored or permanently deactivated (Supplementary Fig. 16). To investigate the origin of PET-induced Is PETase inactivation, we conducted a 1 μs-long MD simulation of the enzyme’s unbound state. It revealed the inherent instability of Is PETase, with significant fluctuations observed in the conformation of the enzyme’s active site ( Fig. 2c ). As shown by the root mean square fluctuations (RMSF), the catalytic loop containing Asp206 exhibited high flexibility ( Fig. 2d-e ). This flexibility, which could lead to a loss of active site preorganization, is the possible cause of Is PETase’s inherent instability. We experimentally investigated the deactivation of Is PETase in the presence of PET powders using p -nitrophenyl butyrate ( p NPB) as the substrate. The results indicated that the adsorption of PETase on either low-crystallinity PET (LC-PET) or high-crystallinity PET (HC-PET) accelerates the deactivation of the enzyme, consistent with the MD simulations ( Fig. 2f ). Download figure Open in new tab Figure 2. Inherent instability of Is PETase. (a) Three types of conformational dynamics of the enzyme active site in PET-bound states. (b) Distribution of the distance between the oxygen atom of Asp206 and the hydrogen atom of His237. (c) Evolution of three pairs of distances within the active site of the unbound enzyme. (d) RMSF of unbound and PET-bound Is PETase. (e) Conformational fluctuations of the enzyme catalytic loop. (f) Time-course deactivation of Is PETase when adsorbing on LC-PET and HC-PET powders. In the PET-bound states, the flexible loop and Trp185, which form one side of the PET capture pliers, adopted a confined conformation. They tightly interacted with the PET fragments ( Fig. 2d , Supplementary Fig. 17-21). We speculated that the flexibility of the catalytic loop and the wobbling of Trp185 contribute to the PET capture capability of Is PETase, despite inducing instability. Previous studies by Guo and colleagues showed that the introduction of Trp185 wobbling in Is PETase can enhance the activity of other PETases, albeit at the cost of inducing instability 23 (Supplementary Note 1, Supplementary Fig. 22-23). Taken together, there exists a trade-off between PET capture capacity and the stability of Is PETase. This trade-off may explain Is PETase’s outstanding PET depolymerization activity at moderate temperatures and its instability at higher temperatures 6 . Directed evolution breaks the trade-off Recent work by Bell et al. created a thermostable variant of Is PETase known as HotPETase 9 . HotPETase incorporates 21 mutations, including three from the original protein template, two from the rational insertion of an additional disulfide bridge, and 16 identified through directed evolution ( Fig. 3a ). Analysis of the ΔG binding distribution revealed that HotPETase exhibited a slightly higher binding capability than the wild type enzyme ( Fig. 3b , Supplementary Fig. 24-25), with more states characterized by high ΔG binding value. Adhesion force measured by atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS) showed a consistent trend of binding affinity of Is PETase and HotPETase to a PET film ( Fig. 3c ). The difference in ΔG binding between active and non-active states closely resembled that of Is PETase (Supplementary Fig. 25), indicating that directed evolution did not lead to an improvement in the target binding ability. Notably, all binding states of HotPETase adopted a stable conformation of the catalytic triad, underscoring the significantly improved stability of the enzyme ( Fig. 3d , Supplementary Fig. 26-27). Microsecond-long MD simulation and conformational landscape analysis of the unbound enzyme further confirmed the intrinsic stability of HotPETase (Supplementary Fig. 28). Our experiments also showed that HotPETase exhibits greater stability than Is PETase, both in its free form and in the presence of PET powder ( Fig. 3e , Supplementary Fig. 29). As shown in Fig. 3f , the fluctuation of the catalytic loop was greatly reduced. This reduction was attributed to the salt bridge and hydrogen bond interactions between Arg207 and its neighboring residues (Glu204, Asn205, and Asp206), as well as steric hindrance from Tyr214 ( Fig. 3g , Supplementary Fig. 30). It is noteworthy that Arg207 and Tyr214 are two of the three mutations in the initial round of directed evolution for HotPETase 9 , the round resulting in the largest enhancement of T m (Supplementary Fig. 31). Thus, both experimental and simulation results unanimously affirm the pivotal role of Arg207 and Tyr214 in enhancing the enzyme’s stability. Download figure Open in new tab Figure 3. Intrinsic Stability of HotPETase. (a) Comparison of mutations in HotPETase with Is PETase: three positions in the starting template (blue spheres), a rationally installed disulfide bridge (yellow spheres), and 16 positions identified through evolution (white spheres). The catalytic triad was depicted with a stick representation in green. (b) Distribution of binding free energy for all binding states of Is PETase and HotPETase. (c) Histograms of detachment force distribution of Is PETase and HotPETase interacting with the surface of a PET film. The mean adhesion force was obtained by a Gaussian fitting. (d) Distribution of distances between the oxygen atom of Asp206 and the hydrogen atom of His237. (e) Time-course deactivation of Is PETase and HotPETase when adsorbing on LC-PET and HC-PET powders. (f) RMSF analysis of Is PETase and HotPETase. (g) Hydrogen bonds induced by mutations located in the catalytic loop. The donor of hydrogen bonds are Arg207 and “M” and “S” denote the mainchain and sidechain of residues. (h) Two distinct wobbling modes observed in Is PETase and HotPETase. The unbound and bound enzymes were shown in blue and red, respectively. (i) The ΔRMSF of several key residues from unbound state to bound state. The current simulations and previous analysis of the HotPETase crystal structure both reveal a missing Trp185 wobbling in HotPETase (Supplementary Fig. 32) and low flexibility of the catalytic loop ( Fig. 3f ). Hence, the flexibility of one jaw of the PET-capturing pliers containing Trp185 and Ile208 was significantly reduced. However, we discovered that Tyr87 on the other jaw of the pliers and its associated loop exhibited higher flexibility compared to the wild type, indicating a new wobbling residue ( Fig. 3f, g-h , Supplementary Fig. 33-35). This originates from three mutations at Thr90, Lys119 and Glu121, leading to a broken salt bridge and less steric hindrance (Supplementary Fig. 36-38). Directed evolution compromised the flexibility of one side of the PET-capturing pliers to improve enzyme stability, while simultaneously introducing wobbling in Tyr87 and enhancing the flexibility of the opposite side of the pliers to offset this loss. Consequently, the reshaped conformational landscape of HotPETase overcomes the limitation of the activity-stability trade-off associated with the flexibility of the catalytic loop and Trp185 wobbling. We propose that Tyr87 wobbling could enhance the activity of other PETases and exhibit greater robustness at elevated temperatures. Origin of HotPETase’s high intrinsic activity and PETase designing opportunities In most experiments involving PET degradation by PETase, enzyme activity is assessed based on the accumulation of mono(2-hydroxyethyl) terephthalate (MHET) and terephthalic acid (TPA) over several hours. Our simulations of the catalytic process of PETase reveal that the apparent enzyme activity reflects not only intrinsic depolymerization activity, but also PET capture capability and stability. Thus, the enhanced catalytic performance cannot be attributed to a single factor. This is supported by the observed improvement in PET depolymerization performance and modest changes (even decrements) in activity towards soluble small substrates in engineered PETase 24 . To elucidate the contribution of directed evolution, we compared the intrinsic activity between the wild-type Is PETase and its evolved variant HotPETase. QM/MM calculations of PET acylation, the rate-limiting step in Is PETase catalysis 25 , 26 , demonstrated a lower free energy barrier for HotPETase-catalyzed PET trimer hydrolysis compared to Is PETase ( Fig. 4a-b , Supplementary Fig. 39). The heightened activity of HotPETase stems from a favorable enthalpic contribution and lower entropic cost ( Fig. 4c and Supplementary Table 1). Analysis of the conformational dynamics of PET-capturing pliers (distance between two loops) indicated that HotPETase adopts a more fluctuant conformation at ground states (GS) and a more confined conformation at transition states (TS) than Is PETase ( Fig. 4d-e , Supplementary Fig. 40). This suggests a more effective TS stabilization by the exquisite active-site preorganization of HotPETase (Supplementary Fig. 41). Hence, the transition from a flexible GS to a rigid TS could lead to activity gains. Download figure Open in new tab Figure 4. High intrinsic activity of HotPETase. (a) Acylation step of Is PETase and HotPETase. (b) Calculated free energy profiles (298.15 K) from reactants to the tetrahedral intermediate in the acylation step. (c) Arrhenius plots depicting ΔG ‡ /T vs. 1/T for catalyzed reactions in Is PETase and HotPETase. (d) The loop distance between residues Tyr87 (C γ atom) and Ile208 (C β atom) at (d) free enzyme states and (e) enzyme-substrate transition states. Drawing insights from directed evolution, tuning the conformational dynamics of PET-capturing pliers of PETase emerges as an effective approach for enzyme engineering. The intrinsic activity, PET capture capability, and enzyme stability are influenced by the dynamics of abundant loops near the enzyme’s active site. Comparing the differences in loop dynamics between different PETases and identifying the key residues that modulate them will facilitate the rational design of highly active and stable PETase enzymes. Conclusion Our multiscale MD simulations and experiments demonstrate how PETase functions at a solid-liquid interface. The dynamic loops near the enzyme active site play a crucial role in capturing polymer substrate fragments. However, the activity-stability trade-off poses a challenge to enhancing enzyme performance. The catalytically superior HotPETase overcomes this trade-off by altering loop dynamics through directed evolution. By shifting flexibility from one side of the PET-capturing pliers to the other, it achieves improvements in both enzyme activity and stability. The comprehensive understanding of PETase’s catalytic process provided by our simulations will contribute to elucidating the mechanism of enzyme catalysis at interfaces and to engineering robust plastic-degrading enzymes. Methods Building the PET membrane model Parameters for the PET monomer were generated with the antechamber module of Amber18 27 using the general Amber force field (GAFF) 28 . The partial charges were set to fit the electrostatic potential generated with B3LYP/TZVP by RESP 29 . Details of the repeat units and tail end of the polymer chain can be found in Supplementary Fig. 1. Initially, twelve PET chains (PET 100 ) were placed in a large periodic cubic box and simulated under vacuum for 50 ns using the NVT ensemble with a V-rescale thermostat 30 set at 800 K. Once a complex structure of PET chains was obtained, pressure was added along the z-axis of the simulation box. The x and y directions of the simulation box had periodic boundary conditions, while z=0 and z=box positions were occupied by two walls composed of CA atoms (atom type in the Amber force field). Under pressure in the z direction, the PET complex was compressed into a membrane. To achieve homogeneity of different PET chains within the membrane, we conducted simulations for 100 ns at 800 K followed by an additional 100 ns simulation at 300 K. During the simulation, an anisotropic Berendsen barostat was used with a reference pressure of 1 bar. The solid PET membrane was then immersed in a water box, with water molecules represented by the three-point charge TIP3P model. Subsequently, the polymer-water system was equilibrated for 10 ns at a constant temperature of 298.15 K and pressure of 1 bar (NPT ensemble), initially by restraining the positions of polymers followed by NPT equilibration for another 200 ns without position restraints. MD simulation of the enzyme-PET complex The crystallographic structure of the Is PETase (PDB ID: 5xjh) 11 and HotPETase (PDB ID: 7qvh) 9 were used as the starting coordinates. PROPKA 3 31 , 32 was utilized to assign the protonation states of titratable residues, with further validation through visual inspection of protonation states and side chain orientations. The His residue of the catalytic triad was singly protonated at N σ . All crystal waters were removed. Enzyme molecules were immersed in a water box containing a PET membrane, as illustrated in Supplementary Fig. 6, with the TIP3P water model applied. All classic MD simulations were conducted using the GROMACS 2019.3 33 , along with the Amber ff14SB 34 force-field parameters for protein. The entire system underwent energy minimization using a combination of steepest descent and conjugate gradient methods (maximum force < 100 kJ/mol/nm). Subsequently, the solvent was equilibrated for 20 ns at constant temperature (298.15 K) and pressure (1 bar) under the NPT ensemble, with enzyme positions restrained. The resulting equilibrated structure served as the initial state for subsequent supervised MD simulations. Temperature and pressure were maintained using a V-rescale thermostat and Parrinello−Rahman barostat 35 , respectively. The cutoff radius for neighbor searching and nonbonded interactions was taken to be 12 Å, and all bonds were constrained using the LINCS algorithm 36 . Visualization of enzyme structures was accomplished using VMD 37 . Supervised MD simulation The simulation protocol was adapted from a previous study on ligand-receptor interactions 38 . During the production of the MD trajectory, the z-coordinate of the enzyme mass center was monitored over a fixed time window (1 ns). A linear function z ( t ) = a* t +b was fitted to the z-coordinates as a function of simulation time. If the slope (a) was less than zero, indicating a decrease in the enzyme-PET distance during the simulation window, the MD simulation was restarted from the last set of coordinates. Otherwise, the simulation was resumed from the original set of coordinates and velocities, and the MD sampling was repeated. If the number of restarts exceeded five, the repeated sampling was terminated, and the MD simulation was restarted from the last set of coordinates. The supervision algorithm continued until three criteria were met: a negative slope, more than 80 contacts between the enzyme and PET (with a distance cutoff of 3.5 Å), and a z-coordinate of the enzyme mass center less than 65 Å. Following the supervised MD, an extended 200 ns-long NPT MD simulation was conducted to obtain the final equilibrated enzyme-PET complex. Analysis of MD simulation results The binding free energy between the enzyme and PET surface, as well as energy decomposition, was assessed using the gmxMMPBSA 39 . Side-chain torsion angles and distances between different protein atoms were calculated using the Python library MDTraj 40 . We analyzed side-chain torsion angles up to χ 2 and employed the Jensen-Shannon (JS) divergence to quantitatively compare the influence of PET binding on the probability distribution of side-chain torsion angles 41 . Using JS divergence, we visualized the change in the probability distribution of side-chain torsion angles of each residue after PET binding. When both χ 1 and χ 2 of a residue existed, the larger JS divergence was chosen. Additionally, time-lagged independent component analysis (tICA) was performed using PyEMMA 2 42 to analyze the enzyme’s conformational landscape, with side-chain torsion angles of several key residues utilized as features (Supplementary Fig. 21). QM/MM calculations Enzyme-substrate complexes were immersed within a spherical water droplet using the TIP3P water model 43 , with a radius of 42 Å. The choice of these radii ensured that all atoms of enzyme-substrate complexes resided within the inner 85% of the spherical boundary. All calculations utilized the OPLS-AA force field 44 . van der Waals (vdW) and bonding parameters for PET were derived from Schrödinger’s Macromodel, with partial charges assigned through the RESP protocol 45 . Water molecules close to the spherical boundary underwent radial and polarization restraints, in accordance with the SCAAS model 46 . All QM/MM (EVB/MD calculations) were conducted utilizing the Q6 program 47 , implementing the leapfrog integrator with a time step of 1 fs. Long-range interactions were managed employing the local reaction field (LRF) approach 48 with a nonbonded interactions cutoff of 10 Å. A nonbonded interaction cutoff of 99 Å was applied specifically to the reacting fragments. The system temperature was maintained at a constant level through the utilization of the Berendsen thermostat 49 , employing a 100 fs bath coupling time. All simulation systems underwent a stepwise heating process from an initial temperature of 1 K to their respective final temperatures over a duration of 230 ps, subsequently followed by an equilibration period of 100 ps. The SHAKE algorithm 50 was used to constrain bonds and angles of solvent molecules. EVB/MD free energy perturbation (FEP) simulations were conducted with 51 discrete λ windows, each with a duration of 10 ps. The simulations were initiated at λ1 = λ2 = 0.5, in proximity to the transition state, and progressed towards the reactant state (λ1 = 1) and the intermediate state (λ2 = 1). To prevent excessive dissociation of reacting fragments within the Michaelis and product complexes, a modest restraint of 0.1 kcal/mol/Å 2 was applied to the reactive atoms throughout the EVB simulations. To calibrate the EVB reaction potential energy surface, the average free energy profile from 100 independent simulations for the reference reaction system in water was fitted to the corresponding free energy profile obtained by the DFT cluster calculations at 298.15 K. The temperature dependence of the reaction catalyzed by enzyme was analyzed by repeating the free energy profile calculations at 278.15, 288.15, 298.15, 303.15, 308.15, 318.15, and 323.15K. At each temperature, about 100 randomized individual replicate simulations were carried out to obtain the free energy profiles. Stability of enzymes The stability of Is PETase and HotPETase in the soluble and adsorbed states was evaluated by their hydrolytic activity on the small molecular substrate p NPB. Multiple glass vials filled with 1 mL of 50 nM enzyme in glycine-NaOH buffer (50 mM, pH 9.2) were incubated at 40 °C in the absence or presence of 10 mg PET powders (LC-PET with 7.6% crystallinity or HC-PET with 30% crystallinity). The vials were taken after 1, 3, 6, 10, 15, 24, and 48 h incubation and cooled down to 4 °C within 10 min, then 20 μL of 0.2 mM pNPB in acetone was added to each vail for reaction. After 10-min hydrolysis at 4 °C, the absorbance at 405 nm of the solution was monitored on a Nanodrop Microvolume Spectrophotometer (Thermo Fisher Scientific Inc.). The concentration of the hydrolytic product p -nitrophenol was calculated using a molar extinction coefficient of 18,000 M -1 cm -1 . Error bars were obtained from three measurements using parallel samples. AFM‐SMFS measurements The AFM-SMFS measurement was performed on a Cypher VRS AFM (Oxford Instruments, UK) according to the protocol described previously 51 . Briefly, a silicon nitride AFM probe was cleaned with UV/Ozone treatment for 30 min and then silanized with a 3% (w/v) solution of 3-aminopropyltriethoxysilane (APTES) in ethanol for 1 h at room temperature. After drying at 110 °C for 30 min, the AFM tip was functionalized in sequence with 2.0 mg mL -1 NHS-PEG-MAL in dimethylformamide (DMF), 2.0 mg mL -1 HS-NTA in DMF, and 50 mM NiSO 4 solution at room temperature. Each step of functionalization takes 1 hour. The enzyme grafting was achieved by immersing the functionalized AFM tip in 14 μM Is PETase or HotPETase for 1 h at room temperature. For measuring the adhesive force between the enzyme and a PET film, the cantilever’s spring constant was calibrated to be 0.06 N m -1 . The enzyme-functionalized tip was moved toward a PET film (thicknesses of 0.25 mm) soaked in glycine-NaOH buffer (50 mM, pH 9.2) until they were in contact at a force of 0.5 nN for 0.5 s. Then the tip was retracted from the substrate at a 2.0 μm s -1 pulling speed. Data availability The data that support the findings of this study are available from the corresponding author on reasonable request. Author Contributions Y.C., S.H., and Y.Z. supervised the project. Y.C. conceived the idea, built the model, and analyzed the results. S.C. performed all the MD simulations and analysis of the results. E.A. carried out the experiments. W.Q. assisted in data analysis. Y.C., S.C., S.H., and Y.Z. co-wrote the manuscript. All authors discussed the results and assisted with the manuscript preparation. Competing interests The authors declare no competing interests. Acknowledgments This work was supported by the National Nature Science Foundation of China under grant number 32371325, and the seed funding of China Petrochemical Corporation (Sinopec Group) under grant number 223260. Reference 1. ↵ Nava , V. et al. Plastic debris in lakes and reservoirs . Nature 619 , 317 – 322 ( 2023 ). OpenUrl 2. ↵ Conk , R. J. et al. Catalytic deconstruction of waste polyethylene with ethylene to form propylene . Science 377 , 1561 – 1566 ( 2022 ). OpenUrl 3. Xu , Z. et al. Chemical upcycling of polyethylene, polypropylene, and mixtures to high-value surfactants . Science 381 , 666 – 671 ( 2023 ). OpenUrl 4. ↵ Tournier , V. et al. An engineered PET depolymerase to break down and recycle plastic bottles . Nature 580 , 216 – 219 ( 2020 ). OpenUrl PubMed 5. ↵ Tournier , V. et al. Enzymes’ Power for Plastics Degradation . Chem. Rev . 123 , 5612 – 5701 ( 2023 ). OpenUrl 6. ↵ Yoshida , S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate) . Science 351 , 1196 – 1199 ( 2016 ). OpenUrl Abstract / FREE Full Text 7. Sulaiman , S. et al. Isolation of a novel cutinase homolog with polyethylene terephthalate-degrading activity from leaf-branch compost by using a metagenomic approach . Appl. Environ. Microbiol . 78 , 1556 – 1562 ( 2012 ). OpenUrl Abstract / FREE Full Text 8. Lu , H. et al. Machine learning-aided engineering of hydrolases for PET depolymerization . Nature 604 , 662 – 667 ( 2022 ). OpenUrl CrossRef 9. ↵ Bell , E. L. et al. Directed evolution of an efficient and thermostable PET depolymerase . Nat. Catal . 5 , 673 – 681 ( 2022 ). OpenUrl 10. ↵ Robles-Martín , A. et al. Sub-micro- and nano-sized polyethylene terephthalate deconstruction with engineered protein nanopores . Nat. Catal . 6 , 1174 – 1185 ( 2023 ). OpenUrl 11. ↵ Joo , S. et al. Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation . Nat. Commun . 9 , 382 ( 2018 ). OpenUrl 12. Austin , H. P. et al. Characterization and engineering of a plastic-degrading aromatic polyesterase . PNAS 115 , E4350 – E4357 ( 2018 ). OpenUrl Abstract / FREE Full Text 13. Knott , B. C. et al. Characterization and engineering of a two-enzyme system for plastics depolymerization . PNAS 117 , 25476 – 25485 ( 2020 ). OpenUrl Abstract / FREE Full Text 14. ↵ Zeng , W. et al. Substrate-Binding Mode of a Thermophilic PET Hydrolase and Engineering the Enzyme to Enhance the Hydrolytic Efficacy . ACS Catal . 12 , 3033 – 3040 ( 2022 ). OpenUrl 15. ↵ Kuhad , R. C. , Gupta , R. & Singh , A. Microbial cellulases and their industrial applications . Enzyme Res . 2011 , 280696 ( 2011 ). OpenUrl CrossRef PubMed 16. ↵ Brady , S. K. , Sreelatha , S. , Feng , Y. , Chundawat , S. P. & Lang , M. J. Cellobiohydrolase 1 from Trichoderma reesei degrades cellulose in single cellobiose steps . Nat. Commun . 6 , 10149 ( 2015 ). OpenUrl CrossRef PubMed 17. ↵ Payne , C. M. et al. Glycosylated linkers in multimodular lignocellulose-degrading enzymes dynamically bind to cellulose . PNAS 110 , 14646 – 14651 ( 2013 ). OpenUrl Abstract / FREE Full Text 18. ↵ Kari , J. et al. Physical constraints and functional plasticity of cellulases . Nat. Commun . 12 , 3847 ( 2021 ). OpenUrl CrossRef 19. ↵ Graham , R. et al. The role of binding modules in enzymatic poly(ethylene terephthalate) hydrolysis at high-solids loadings . Chem Catal . 2 , 2644 – 2657 ( 2022 ). OpenUrl 20. ↵ Eberl , A. et al. Enzymatic surface hydrolysis of poly(ethylene terephthalate) and bis(benzoyloxyethyl) terephthalate by lipase and cutinase in the presence of surface active molecules . J. Biotechnol . 143 , 207 – 212 ( 2009 ). OpenUrl CrossRef PubMed 21. ↵ Kawai , F. , Kawabata , T. & Oda , M. Current knowledge on enzymatic PET degradation and its possible application to waste stream management and other fields . Appl. Microbiol. Biotechnol . 103 , 4253 – 4268 ( 2019 ). OpenUrl CrossRef 22. ↵ Wei , R. et al. Biocatalytic Degradation Efficiency of Postconsumer Polyethylene Terephthalate Packaging Determined by Their Polymer Microstructures . Adv. Sci . 6 , 1900491 ( 2019 ). OpenUrl 23. ↵ Chen , C.-C. et al. General features to enhance enzymatic activity of poly(ethylene terephthalate) hydrolysis . Nat. Catal . 4 , 425 – 430 ( 2021 ). OpenUrl 24. ↵ Cui , Y. et al. Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading . Nat. Commun . 15 , 1417 ( 2024 ). OpenUrl 25. ↵ Jerves , C. , Neves , R. P. P. , Ramos , M. J. , da Silva , S. & Fernandes , P. A. Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations . ACS Catal . 11 , 11626 – 11638 ( 2021 ). OpenUrl 26. ↵ Garcia-Meseguer , R. , Orti , E. , Tunon , I. , Ruiz-Pernia , J. J. & Arago , J. Insights into the Enhancement of the Poly(ethylene terephthalate) Degradation by FAST-PETase from Computational Modeling . J. Am. Chem. Soc . 145 , 19243 – 19255 ( 2023 ). OpenUrl 27. ↵ Case , D. et al. AMBER 18; 2018 . University of California, San Francisco . 28. ↵ Wang , J. , Wolf , R. M. , Caldwell , J. W. , Kollman , P. A. & Case , D. A. Development and testing of a general amber force field . J. Comput. Chem . 25 , 1157 – 1174 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 29. ↵ Bayly , C. I. , Cieplak , P. , Cornell , W. & Kollman , P. A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model . J. Phys. Chem . 97 , 10269 – 10280 ( 2002 ). OpenUrl CrossRef 30. ↵ Bussi , G. , Donadio , D. & Parrinello , M. Canonical sampling through velocity rescaling . J. Chem. Phys . 126 , 014101 ( 2007 ). OpenUrl CrossRef PubMed 31. ↵ Sondergaard , C. R. , Olsson , M. H. , Rostkowski , M. & Jensen , J. H. Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values . J. Chem. Theory Comput . 7 , 2284 – 2295 ( 2011 ). OpenUrl CrossRef PubMed 32. ↵ Olsson , M. H. , Sondergaard , C. R. , Rostkowski , M. & Jensen , J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions . J. Chem. Theory Comput . 7 , 525 – 537 ( 2011 ). OpenUrl CrossRef PubMed 33. ↵ Hess , B. , Kutzner , C. , van der Spoel , D. & Lindahl , E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation . J. Chem. Theory Comput . 4 , 435 – 447 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 34. ↵ Hornak , V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters . Proteins 65 , 712 – 725 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 35. ↵ Parrinello , M. & Rahman , A. Crystal Structure and Pair Potentials: A Molecular-Dynamics Study . Phys. Rev. Lett . 45 , 1196 – 1199 ( 1980 ). OpenUrl CrossRef Web of Science 36. ↵ Hess , B. , Bekker , H. , Berendsen , H. J. C. & Fraaije , J. G. E. M. LINCS: A linear constraint solver for molecular simulations . J. Comput. Chem . 18 , 1463 – 1472 ( 1997 ). OpenUrl CrossRef PubMed Web of Science 37. ↵ Humphrey , W. , Dalke , A. & Schulten , K. VMD: visual molecular dynamics . J Mol Graph 14 , 33 – 38 , 27-38 ( 1996 ). OpenUrl CrossRef PubMed Web of Science 38. ↵ Sabbadin , D. & Moro , S. Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale . J. Chem. Inf. Model . 54 , 372 – 376 ( 2014 ). OpenUrl 39. ↵ Valdes-Tresanco , M. S. , Valdes-Tresanco , M. E. , Valiente , P. A. & Moreno , E. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS . J. Chem. Theory Comput . 17 , 6281 – 6291 ( 2021 ). OpenUrl CrossRef 40. ↵ McGibbon , R. T. et al. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories . Biophys. J . 109 , 1528 – 1532 ( 2015 ). OpenUrl CrossRef PubMed 41. ↵ Cao , Y. , Qiao , Y. , Cui , S. & Ge , J. Origin of Metal Cluster Tuning Enzyme Activity at the Bio-Nano Interface . JACS Au 2 , 961 – 971 ( 2022 ). OpenUrl 42. ↵ Scherer , M. K. et al. PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models . J. Chem. Theory Comput . 11 , 5525 – 5542 ( 2015 ). OpenUrl CrossRef PubMed 43. ↵ Mark , P. & Nilsson , L. Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K . J. Phys. Chem. A 105 , 9954 – 9960 ( 2001 ). OpenUrl CrossRef Web of Science 44. ↵ Kaminski , G. A. , Friesner , R. A. , Tirado-Rives , J. & Jorgensen , W. L. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides . J. Phys. Chem. B 105 , 6474 – 6487 ( 2001 ). OpenUrl CrossRef 45. ↵ Bayly , C. I. , Cieplak , P. , Cornell , W. & Kollman , P. A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model . The Journal of Physical Chemistry 97 , 10269 – 10280 ( 2002 ). OpenUrl CrossRef 46. ↵ King , G. & Warshel , A. A surface constrained all-atom solvent model for effective simulations of polar solutions . J. Chem. Phys . 91 , 3647 – 3661 ( 1989 ). OpenUrl CrossRef Web of Science 47. ↵ Bauer , P. et al. Q6: A comprehensive toolkit for empirical valence bond and related free energy calculations . SoftwareX 7 , 388 – 395 ( 2018 ). OpenUrl 48. ↵ Lee , F. S. & Warshel , A. A local reaction field method for fast evaluation of long-range electrostatic interactions in molecular simulations . J. Chem. Phys . 97 , 3100 – 3107 ( 1992 ). OpenUrl CrossRef Web of Science 49. ↵ Berendsen , H. J. C. , Postma , J. P. M. , van Gunsteren , W. F. , DiNola , A. & Haak , J. R. Molecular dynamics with coupling to an external bath . J. Chem. Phys . 81 , 3684 – 3690 ( 1984 ). OpenUrl CrossRef PubMed Web of Science 50. ↵ Ryckaert , J.-P. , Ciccotti , G. & Berendsen , H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes . J. Comput. Phys . 23 , 327 – 341 ( 1977 ). OpenUrl CrossRef 51. ↵ Akram , E. et al. On the temperature dependence of enzymatic degradation of poly(ethylene terephthalate) . Chin. J. Catal . 60 , 284 – 293 ( 2024 ). 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Share A Mechanistic Understanding of the Activity-Stability Trade-off in PETase Shuang Chen , Ekram Akram , Weili Qiao , Yifei Zhang , Shozeb Haider , Yufei Cao bioRxiv 2024.06.09.598049; doi: https://doi.org/10.1101/2024.06.09.598049 Share This Article: Copy Citation Tools A Mechanistic Understanding of the Activity-Stability Trade-off in PETase Shuang Chen , Ekram Akram , Weili Qiao , Yifei Zhang , Shozeb Haider , Yufei Cao bioRxiv 2024.06.09.598049; doi: https://doi.org/10.1101/2024.06.09.598049 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Biochemistry Subject Areas All Articles Animal Behavior and Cognition (7651) Biochemistry (17746) Bioengineering (13928) Bioinformatics (42066) Biophysics (21499) Cancer Biology (18650) Cell Biology (25579) Clinical Trials (138) Developmental Biology (13409) Ecology (19947) Epidemiology (2067) Evolutionary Biology (24374) Genetics (15633) Genomics (22557) Immunology (17775) Microbiology (40505) Molecular Biology (17217) Neuroscience (88796) Paleontology (667) Pathology (2845) Pharmacology and Toxicology (4836) Physiology (7664) Plant Biology (15179) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9839) Zoology (2272)