Conformational Diversity and Allosteric Network Enable Multi-Substrate Recognition in Laccase Enzyme

Conformational Diversity and Allosteric Network Enable Multi-Substrate Recognition in Laccase Enzyme | 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 Conformational Diversity and Allosteric Network Enable Multi-Substrate Recognition in Laccase Enzyme Sudipta Mitra , View ORCID Profile Ranjit Biswas , View ORCID Profile Suman Chakrabarty doi: https://doi.org/10.1101/2025.11.20.689517 Sudipta Mitra 1 Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences , Block-JD, Sector-III, Salt Lake, Kolkata 700106, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ranjit Biswas 1 Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences , Block-JD, Sector-III, Salt Lake, Kolkata 700106, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ranjit Biswas Suman Chakrabarty 1 Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences , Block-JD, Sector-III, Salt Lake, Kolkata 700106, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Suman Chakrabarty For correspondence: sumanc{at}bose.res.in Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Laccase is a multicopper oxidase with remarkable potential for environmental remediation due to its ability to degrade a wide range of pollutants. However, the molecular origins of its broad substrate scope have remained elusive. Here, through extensive molecular dynamics (MD) simulations coupled with machine learning (ML) based Markov State Model (MSM), we uncover a previously uncharacterized allosteric pathway that governs laccase’s catalytic activity. This pathway functionally couples the dynamics of two key loops at the active site, and its integrity is crucial for enzymatic efficiency. Our analysis of the apo enzyme’s conformational free energy landscape reveals that this allostery is part of a cooperative global network, leading to distinct, slowly interconverting conformational states. We demonstrate that different dye substrates bind by selectively recognising these pre-existing states, confirming a conformational selection mechanism. Collectively, these findings provide a dynamic blueprint for laccase’s functional versatility and offer a general framework for understanding and engineering other multi-substrate enzymes. Introduction Rapid industrialization is a key to thriving economy but imposes critical environmental challenges. Textile industry, a burgeoning sector that accounts for large-scale employment globally, extensively uses synthetic colouring agents during garment dying and printing processes 1 . These synthetic colouring agents, known as textile dyes, are chemical compounds with different environmental imprints. When unused or excess solution of these dyes are released into water bodies as effluents, critical environmental issues set in motion because some of these organic dye molecules or their fragments can even act as carcinogens. Textile dye wastewater may affect photosynthetic functions of aquatic plants and algae and in turn severely impact the ecosystem and pose a serious threat to human health 2 , 3 . Despite considerable advancements in wastewater treatment technologies, current research continues to focus on developing sustainable, cost effective, and environment friendly methods for the remediation of textile dye effluents 4 , 5 . In this context, Laccases (EC 1.10.3.2), belonging to the family of multicopper oxidoreductases (MCOs), have attracted significant attention 6 – 8 . They are mostly found in white-rot fungi and can catalyse the oxidation of a wide range of xenobiotic compounds, such as, phenols, polyphenols, aromatic amines and non-phenolic organic compounds using oxygen as a reactant and release water and other relatively less hazardous compounds as by-products 9 , 10 . Experimental studies have further demonstrated their ability to decolorize dye contaminated wastewater to varying extent 11 – 13 . Therefore, being a ‘green catalyst’, laccase-mediated wastewater treatment is a sustainable and eco-friendly solution to prevent contamination of water bodies by hazardous textile dye effluents. The ability of an enzyme to act upon a broad range of substrates is called ‘substrate promiscuity’ 14 – 16 . Using steady-state UV-visible absorption spectroscopy and classical all atom molecular dynamics (MD) simulations, we previously reported 17 the ability of a laccase from the white-rot fungus Trametes versicolor to degrade several industrial dyes of varying shape and chemical nature: brilliant blue (anionic, net charge -2), coumarin 343 (anionic, net charge -1), methyl green (cationic, net charge +2), crystal violet (cationic, net charge +1) and thioflavin T (cationic, net charge +1). The 3D structure of this laccase (PDB ID: 1KYA) 18 and the active site residues 19 : D150, A161, F162, P163, L164, D206, P391, G392 and H458 are shown in Figure 1 . Moreover, the catalytic site of the laccase consists of four copper atoms: the one close to the active site is known as T1 copper (T1 Cu) and the other three copper atoms form a trinuclear copper cluster (TNC) 20 . H458 primarily coordinates the T1 Cu and usually involves in hydrogen bond (H-bond) and π-stacking interactions with substrates 21 . This helps substrates to maintain a stable binding pose near the T1 Cu and thus facilitates electron transfer process required for the enzymatic activity. It is important to note that this crystal structure of laccase corresponds to the enzyme bound to a ligand named 2,5-xylidine. Download figure Open in new tab Figure 1. Crystallographic structure of the laccase from the white-rot fungus Trametes versicolor (PDB: 1KYA). Active site residues are marked and shown in licorice representation. Catalytic site T1 Cu and trinuclear copper cluster (TNC) are shown as orange spheres. We have demonstrated in our earlier work that a loop comprising residues G159-P160-A161-F162-P163-L164 at the active site (see Figure 1 ) adopts multiple distinct conformations when interacting with the six aforementioned ligands in their respective laccase bound states. 17 Binding energy calculations further revealed surprisingly similar binding affinity of the ligands irrespective of their different shape and charge. All these observations necessitate an in-depth investigation of laccase apo ensemble to fully understand it’s biological function. A recent study explored binding pathway of a pollutant to this laccase 22 . In this study, we present a comprehensive characterization of the apo conformational ensemble and free energy landscape of the white-rot fungus laccase (PDB ID: 1KYA) using all-atomistic classical molecular dynamics (MD) simulations combined with state-of-the-art machine learning (ML) techniques. Extensive MD simulations as well as enhanced sampling simulations were performed. Random forest (RF) 23 classifier was utilized for featurization and a variational autoencoder (VAE) 24 was employed to obtain a low dimensional representation of the laccase apo free energy surface. In addition, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) 25 and Variational Approach for Markov Processes using neural networks (VAMPnets) 26 – 28 were utilized to identify metastable conformational states and construct a Markov State Model (MSM) 29 – 32 . Furthermore, we propose an allosteric signal 33 propagation pathway within the substrate binding pocket of laccase that regulates the enzyme’s catalytic efficiency, consistent with experimental observations 34 . Finally, we examined whether dye binding to laccase proceeds via a conformational selection or an induced fit mechanism 35 – 37 . Results and Discussion Construction of Laccase Apo Free Energy Surface and Identification of Metastable Conformational States using Machine Learning Important residue pairs, which can distinguish between laccase apo and holo conformations, were identified using the Random forests (RF) classifier 38 . RF classifier was trained and validated on a dataset generated using a combination of laccase holo and apo trajectories as described in the SI. We selected 38 residue pairs (Table S1) such that they comprised 80% of the total feature importance (Figure S1a) and the corresponding pairwise C α − C α atom distances (Figure S1b) were used as features for all the analyses reported in this study. Notably these residue pairs were located around and beyond the active and catalytic sites of laccase (Figure S1b), hinting at a possible global conformational dynamics associated with the observed structural changes of the laccase active site loop (residues 159-164). Furthermore, we performed HDBSCAN clustering on this 38 dimensional feature space using cumulative 3 μs long six laccase apo trajectories (6 × 500 ns). This analysis helped us to identify initial laccase apo metastable clusters, which subsequently provided the basis of constructing laccase apo free energy surface (FES). The details of performing HDBSCAN clustering is provided in the SI. We observed that the six apo trajectories consistently mapped to six distinct clusters on this high dimensional feature space across different minimum cluster sizes, confirming convergence (Figure S3). Therefore, these 38 residue pairs not only distinguish between laccase apo and holo conformations, it is also possible to differentiate different apo metastable conformations using the same distance features. A low dimensional representation of the laccase apo FES was constructed by comparing two popular dimensionality reduction methods: time-lagged independent component analysis (tICA, linear) 39 – 41 and variational autoencoder (VAE, non-linear) 24 , 42 , 43 . See the methods section and SI for the details of constructing tICA and VAE projection spaces. VAE model was trained and validated on the feature distance data generated using the cumulative 43 μs discrete equilibrium apo trajectories. Different apo clusters obtained from the aforementioned HDBSCAN analysis showed significant overlap on the 2-dimesional tICA space (Figure S6a), but were well separated on the 2-dimesional VAE latent space (Figure S6b). Implied timescale calculations further confirmed that both the methods captured the similar dynamical processes (Figure S6c-d). Therefore, VAE latent space provides more suitable and accurate low dimensional representation of the laccase apo FES than tICA. The final apo FES is shown in Figure 2a on the 2-dimesional VAE latent space. See the SI for details of generating the equilibrium apo trajectories. Download figure Open in new tab Figure 2. a) 2D VAE latent space representation of the laccase apo free energy surface. b) Metastable apo conformational states (denoted as S1-S5) of laccase obtained from the 5-clusters VAMPnets model are highlighted on the same VAE latent space representation. Finally, laccase apo metastable conformational states were identified using several VAMPnets neural networks with 4-6 output nodes trained and validated on the feature distance data generated using the cumulative 43 μs discrete equilibrium apo trajectories. The details of VAMPnets architecture and training procedure are discussed in the methods section and SI. VAMPnets generated different clusters were mapped on the 2-dimensional VAE projection space (Figure S8a-c). It has been observed that all the clusters were well separated on the VAE latent space, enabling proper differentiation and identification of multiple metastable apo conformational states of laccase. Moreover, similar implied timescales were predicted for all the VAMPnets models (Figure S8d-f), showing convergence of our results. However, 4 and 5-clusters VAMPnets models produced implied timescales with minimal error (Figure S8d-e), whereas 6-clusters model was associated with high uncertainties (Figure S8f). Therefore, we decided to select the five metastable conformational states clustering obtained from the 5-clusters VAMPnets model. These metastable states, denoted as S1-S5, are marked in Figure 2b on the apo FES. Markov State Model and Conformational Diversity in the Laccase Apo Ensemble A Markov State Model (MSM) was constructed for these VAMPnets generated five metastable apo conformational states and the resulting kinetic network along with the thermodynamic stabilities of the states are shown in Figure 3 . The details of the MSM construction are discussed in the methods section and SI. The MSM passed the Chapman-Kolmogorov (CK) test (Figure S9). Highest stationary probability of the state S2 makes it the predominant laccase apo state followed by state S4 and others. To enhance visual clarity, the kinetic network shown in Figure 3 omits transitions with rates lower than 1 ms −1 (i.e mean first passage time greater than 1000 μs). Overall, we observed that the transition rates between different metastable conformations of laccase are relatively slow, with all values are on the order of ms −1 . Notably, the S1 → S4 transition is the fastest among them, whereas all other transitions are at least one order of magnitude slower. Download figure Open in new tab Figure 3. Markov state model describing the transitions and thermodynamic stability of the five laccase apo metastable states, S1-S5. Representative protein conformations in each of these metastable states are also shown. Different conformations of the two loops: L1 (residues 159-164) and L2 (residues 330-337) that form boundary of the laccase active site are highlighted. Inter-state transitions with rates smaller than 1 ms −1 are not shown for visual clarity. Width of an arrow in this kinetic network is proportional to the corresponding transition rate, whereas circle sizes represent the stationary probabilities of the respective metastable states. Cu atoms are represented as orange spheres. Representative laccase conformations corresponding to each of these five apo metastable states are also shown in Figure 3 . We observed significant conformational plasticity of two loops, referred here as L1 (residues 159–164) and L2 (residues 330–337), which form the boundary of the laccase active site. L1 generally adopts a disordered loop conformation but exhibits distinct structural variations across the metastable states. This conformational heterogeneity of loop L1, as discussed in our previous study 17 , plays a vital role in enabling substrate promiscuity of laccase. Moreover, L2 displays a broader structural diversity: it is disordered in states S2 and S3, forms anti-parallel β-sheet in S1 and S4, and adopts a helical structure in S5. Such conformational plasticity of L2 has not been reported previously. We carried out H-bond occupancy analysis 44 to investigate which H-bonding residue pairs stabilize these different conformations of loops L1 and L2, and further shed light on the microscopic origin of the observed sluggish inter-state transition kinetics. 50 representative conformations from each of the five state free energy basins were extracted. Residue pairs forming H-bonds in at least one-third of these conformations were identified, and their corresponding occupancy values were computed across all five states. We identified some residue pairs located around both the loops (Figure S10a) which have significant difference in H-bond occupancy values as provided in Table S2. Thus, H-bonds between these residue pairs are appeared to be important in stabilizing different conformations of loops L1 and L2. The occupancy profiles were further compared pairwise across states to quantify the percentage dissimilarities in the H-bond networks. Figure S10b presents these dissimilarities in the H-bond network between each pair of metastable states. Notably, all pairwise comparisons revealed greater than 40% dissimilarity, indicating that the metastable conformational states are substantially distinct from one another with respect to the presence or absence of inter-residue H-bonds. Figure S10c illustrates which residue pairs need to form and break H-bonds during the transition from the second most populated state (S4) to the most populated state (S2). Corresponding H-bond rearrangements for other inter-state transitions are also shown in Figure S10d–e. We observed that these H-bonding residue pairs were distributed across the entire protein rather than being confined around the active site. Thus, transitions between metastable states require extensive global H-bond reorganization, rather than localized rearrangements near the active site. This widespread reformation of H-bonds likely underlies the slow transition kinetics as observed between the metastable apo conformations of laccase, and also shows cooperativity in laccase conformational dynamics. Allosteric Signaling within the Substrate Binding Pocket Controls the Catalytic Efficiency of Laccase As discussed in the previous section, conformations of loops L1 and L2 that form boundary of the laccase active site vary significantly across the metastable states. Moreover, experimental studies 34 have shown that mutations at sites F162 in loop L1, and F332 and F337 in loop L2 substantially altered the catalytic efficiency of laccase, suggesting that the observed structural diversity of loop L2 can directly influence the conformational heterogeneity of L1, and therefore controls the catalytic activity and substrate promiscuous nature of laccase. Figure 4a illustrates three major distinct conformations adopted by loops L1 and L2, namely helical, antiparallel β-sheet, and disordered structures along with the corresponding orientations of the key residues F162 (in L1) and, F332 and F337 (in L2) observed across the three metastable states (S2, S4, and S5). Collectively, these results provide strong evidence of an allosteric coupling between the conformational dynamics of loops L1 and L2. Download figure Open in new tab Figure 4. a) Three major distinct conformations adopted by loops L1 and L2, namely helical, antiparallel β-sheet, and disordered structures along with the corresponding orientations of the key residues F162 (in L1) and F332 and F337 (in L2) observed across the three metastable states (S2, S4, and S5) are shown. b) Correlation matrix for all possible residue pairs within loops L1 and L2. c) Allosteric communication pathway that starts from the source residue F332 (in L2) and ends at the target residue F162 (in L1) is shown in blue. Residues involved in this pathway are also marked. Cu atoms are shown in orange spheres, while red and pink coloured spheres represent the C α atoms of the respective residues. Correlations between conformational fluctuations between all pairs of residues, each selected from the two alternating loops, were quantified by computing the linear mutual information 45 between them. The resulting correlation matrix for all possible residue pairs within these two loops is presented in Figure 4b . Details of the computational procedures are provided in the SI. As anticipated, the dynamics of the two loops exhibit strong mutual correlation. Furthermore, several similar allosteric communication pathways 46 connecting loops L1 and L2 were computed (Table S3) and the shortest pathway is presented in Figure 4c , with the participating residues being marked. All pathways originate from residue F332 in loop L2, propagate through residues F463, L459, F457, D456, I455, S113 located around the T1 Cu, and terminate at residue F162 in loop L1. Notably, one of the terminal pathway residue F463 is located in the second coordination shell of the T1 Cu near loop L2, whereas I455 forms part of the second coordination shell on the opposite side, adjacent to loop L1 10 (Figure S11). In addition, other middle pathway residues D456, F457, and L459 are situated near H458, a primary T1 Cu coordinating residue ( Figure 1 ). Therefore, conformational fluctuations originating at loop L2 transmits through the primary and secondary coordination shells of the T1 Cu to reach loop L1. This argument physically validates the proposed allosteric pathway and provides a microscopic rationale for the mechanism of biological signal propagation within the laccase active site that controls the enzyme’s catalytic efficiency under mutations at various sites in loops L1 and L2 as observed experimentally. Dye Binding to Laccase Proceeds via Conformational Selection Mechanism In this section, we qualitatively assess whether several dye molecules bind to laccase via conformational selection or induced fit mechanism. This is achieved by projecting the five dye molecules−brilliant blue, coumarin 343, thioflavin T, methyl green and crystal violet bound laccase conformations taken from our previous study onto the enzyme’s apo free energy surface. The results, presented in Figure 5a , show that the bound projections occupy distinct regions of the apo FES. A closer examination further reveals that these bound conformations correspond to different apo metastable states of laccase: (i) brilliant blue bound conformations reside in state S3; (ii) methyl green, coumarin 343, and thioflavin T bound conformations reside in state S4; and (iii) crystal violet bound conformations correspond to state S1. These apo metastable conformations therefore represent dye binding competent states of the enzyme. As discussed previously, among the five metastable states, S2 is the most populated in the apo ensemble. Consequently, all five dye molecules appear to bind preferentially to higher energy apo conformations of laccase. Based on these observations, we propose that the binding of these dye molecules to laccase most likely proceeds via a conformational selection mechanism. Download figure Open in new tab Figure 5. a) Projections of brilliant blue (BB), coumarin 343 (CM), crystal violet (CV), methyl green (MG) and thioflavin T (TT) bound protein conformations (labelled as Ligand bound) on the laccase apo FES. b) Projections of the last 500 ns of apo protein trajectories generated after deleting the dyes from the laccase active site (labelled as Ligand delete). Figure 5b presents the projections of the final 500 ns of the respective apo laccase trajectories obtained after removing the dye molecules from the active site. It is observed that upon removal of brilliant blue, the protein relaxes toward a nearby local minimum of the state S3. In addition, for the other four dyes, the corresponding dye deleted apo trajectories remain largely confined to the same metastable states of the FES. A comparative analysis of the apo and holo FES is essential to quantify the changes in the populations of these binding competent metastable states and to gain deeper insight into the underlying dye binding mechanism. Conclusion In summary, we present a comprehensive characterization of the laccase apo conformational ensemble, free energy landscape and allosteric communication using all-atomistic MD simulations and machine learning. We discovered that the apo conformational ensemble of laccase is notably diversified, which facilitates different dye binding to laccase via conformational selection mechanism. We further characterized the different metastable states by identifying key hydrogen bonded residue pairs located throughout the entire protein. Markov state model unveiled sluggish interconversion kinetics between these metastable states owing to large scale H-bond network reformation throughout the entire protein while making an inter-state transition. This shows cooperativity in laccase conformational dynamics. Moreover, we also identified notable structural plasticity of two loops that form boundary of the laccase substrate binding pocket. Dynamics of these two loops are highly correlated, suggesting allosteric communication between them. Therefore, we proposed an allosteric pathway that connects key residues within these two loops and biological signal propagation via this proposed allosteric pathway regulates the enzyme’s catalytic efficiency. Collectively, these findings enhance our understanding of the biological functions of laccase and provide a foundation for future investigations into the functional mechanisms of other substrate promiscuous enzymes. Computational Methods The white-rot fungus laccase crystallize structure was obtained from the protein data bank, PDB ID: 1KYA and modelled using the ff14SB force field 47 . In this present study, we performed 100 classical MD simulations of 500 ns each starting from 100 different structures of the laccase obtained from tICA-Metadynamics simulations 48 , 49 . Thus, a total of 50 μs trajectories were generated. First 100 ns from each trajectory was discarded from final analyses. All the systems were solvated with ∼ 17,000-20,000 TIP3P 50 waters and neutralized by adding 14 Na + ions into the simulation box. All the simulations were performed using GROMACS 51 patched with PLUMED 52 . See SI for further details. We also used several trajectories from our previous work: (i) five 1 μs long dye molecule bound protein trajectories, and (ii) six 1 μs long apo protein trajectories, named here as ‘ligand delete apo trajectories’, generated after deleting the six ligands (2,5-xylidine, brilliant blue, coumarin 343, thioflavin T, methyl green and crystal violet) from the active site in their respective bound conformations. Therefore, in this work we analysed cumulative 43 μs discrete equilibrium apo trajectories (100 × 400 ns generated in the present study and 6 × last 500 ns of ‘ligand delete apo trajectories’). RF analysis and HDBSCAN clustering were performed using scikit-learn python library. We employed the feature distances obtained from RF analysis to construct a large dataset using the cumulative 43 μs discrete equilibrium apo trajectories. VAE and VAMPnets models were trained and validated on this dataset. VAE was implemented using PyTorch. The encoder block of the VAE comprised of four fully connected hidden layers with nodes 300-200-100-20, leading to a latent space layer with 2 nodes and the decoder part mirrored the encoder. VAMPnets were implemented using Deeptime 53 . VAMPnets neural network lobes for both the instantaneous and time-lagged data shared the same architecture with three fully connected hidden layers with nodes 36-24-16 and a output layer with varying dimensions: 4, 5 and 6. The final MSM was constructed using a lag-time of 70 ns (Figure S8e). All the allostery related analyses were performed using the Correlationplus package 46 . See SI for further details. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author upon reasonable request. ACKNOWLEDGEMENTS The authors acknowledge Science and Engineering Research Board (SERB), Government of India for a project (MTR/2023/000336) that enabled the data analyses reported in this work. Authors also acknowledge the supercomputing facility “Param Rudra” established under the National Supercomputing Mission (NSM), Government of India in SNBNCBS, Kolkata. S.M. acknowledges SNBNCBS for providing research fellowship. Footnotes ↵ * Email: ranjit{at}bose.res.in References ↵ H. Ben Slama , A. Chenari Bouket , Z. Pourhassan , F.N. Alenezi , A. Silini , H. Cherif-Silini , T. Oszako , L. Luptakova , P. Golińska , and L. Belbahri , “ Diversity of synthetic dyes from textile industries, discharge impacts and treatment methods ,” Applied Sciences 11 ( 14 ), 6255 ( 2021 ). OpenUrl ↵ R. Kant , and R. Kant , “ Textile dyeing industry an environmental hazard ,” Nat Sci (Irvine) 4 ( 1 ), 22 – 26 ( 2011 ). OpenUrl ↵ L. Lin , H. Yang , and X. Xu , “ Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review ,” Front Environ Sci 10 , 975 ( 2022 ). OpenUrl ↵ C.R. Holkar , A.J. Jadhav , D. V. Pinjari , N.M. Mahamuni , and A.B. 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