PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets

PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets | 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 PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets View ORCID Profile Anna Krzynówek , View ORCID Profile Karoline Faust doi: https://doi.org/10.1101/2025.10.28.685028 Anna Krzynówek 1 Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven , Leuven B-3000, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anna Krzynówek For correspondence: annamaria.krzynowek{at}kuleuven.be Karoline Faust 1 Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven , Leuven B-3000, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karoline Faust Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract PlasticEnz is a new open-source tool for detecting plastic-degrading enzymes (plastizymes) in metagenomic data by combining sequence homology-based search with machine learning. It integrates custom Hidden Markov Models, DIAMOND alignments, and polymer-specific classifiers trained on ProtBERT embeddings to identify candidate depolymerases from contigs, genomes, or protein sequences. PlasticEnz supports 11 plastic polymers with ML classifiers for PET and PHB, achieving F1 > 0.7 on independent test sets. Applied to plastic-exposed microcosms and field metagenomes, the tool recovered known PETases and PHBases, distinguished plastic-contaminated from pristine environments, and clustered predictions with validated reference enzymes. PlasticEnz is fast, scalable, and user-friendly, providing a robust framework for exploring microbial plastic degradation potential in complex communities. Author Summary Plastic pollution is a global problem, and one promising solution is using microbes that can break them down. However, finding the enzymes responsible for this in complex environmental samples is not easy. We developed PlasticEnz , a free and easy-to-use tool that helps researchers identify plastic-degrading enzymes or “plastizymes” in metagenomic data. PlasticEnz combines traditional sequence similarity search methods with machine learning models trained on known plastizymes. It works with protein sequences, contigs, or genomes with ML components optimised for detection of two common plastic polymers: PET and PHB. We tested PlasticEnz on both controlled lab experiments and real-world samples from plastic-polluted soils and clean environments. The tool successfully identified known plastic-degrading enzymes and even helped distinguish between polluted and pristine sites. By making plastizyme detection more accessible, PlasticEnz enables researchers to better explore the microbial potential for plastic degradation, which could support future bioremediation efforts. Introduction Plastic pollution is a growing environmental problem posing serious risks to ecosystems, wildlife, and human health( 1 – 4 ). Despite this, only a small fraction of the millions of tons of plastic waste generated each year is recycled or reused, with most ending up in natural environments( 5 , 6 ). The capacity of some microorganisms to biodegrade plastic polymers has attracted considerable attention as a potential strategy aimed at mitigating plastic pollution( 7 – 14 ). In particular, polyester plastics (e.g. polyethylene terephthalate (PET), polylactic acid (PLA), polycaprolactone (PCL)), which are widely used in textile and packaging manufacturing, are of interest for potential microbial bioremediation due to the presence of repeated ester bonds that can be targeted by various extracellular depolymerases( 11 , 13 , 15 , 16 ). Recent advances in bioinformatics combined with decreasing cost of whole-genome sequencing, have greatly improved our ability to screen complex communities for novel enzymes. However, existing computational tools often lack specificity for plastic substrates and involve complex multi-step pipelines that might not be accessible for researchers without bioinformatics expertise. Several publicly available databases now catalogue protein sequences and associated metadata for enzymes involved in plastic degradation( 17 – 19 ). Commonly used approaches to identify candidate plastic-degrading enzymes involve large-scale homology searches against these databases using fast sequence aligners such as DIAMOND( 20 ), Bowtie2( 21 ), or Minimap2( 22 ). Other methods rely on domain-based annotation, such as screening for specific functional domains using tools like InterProScan( 23 ) or Pfam( 24 ). In addition, custom Hidden Markov Motifs (HMMs)( 25 , 26 ) built from curated enzyme sequences and tailored to specific plastic polymers have been employed before to improve search specificity( 27 , 28 ). More recently, the application of machine learning (ML) to protein function prediction is being increasingly explored, enabling the discovery of novel enzyme candidates that lack clear homology to known proteins( 29 – 35 ). In this context, ML models have been applied to predict plastic-degrading enzymes, as demonstrated by Jiang et al. ( 36 , 37 ). However, these models were not tested on real-world metagenomics datasets and are not readily available to be applied to the user’s own protein sequences. To address these limitations, we developed PlasticEnz , an open-access tool that combines homology-based search using custom HMMs with machine learning prediction to improve the detection of plastic-degrading enzymes. In this study, we describe the development, testing, and application of PlasticEnz . The tool accepts protein sequences, genomic assemblies, or contigs as input, and identifies candidate plastic-degrading enzymes using a combination of custom HMMs, DIAMOND-based homology searches, and optional machine learning classification. Specifically, HMM-based screening is available for P3HP, PBAT, PBS, PBSA, PCL, PEA, PET, PHA, PHB, PHBV, and PLA; DIAMOND-based searches are implemented for PBS, PBSA, PCL, PES, PHBV, and PLA; and machine learning predictions using XGBoost (default) and a more sensitive neural network model are currently available for PET and PHB (Supplementary Table 1). This integrated approach allows PlasticEnz to flexibly detect enzyme homologs across a wide range of plastic polymers with adjustable sensitivity and specificity. To facilitate accessibility, PlasticEnz is implemented as a command-line tool with streamlined output formats that include HMMER/DIAMOND outputs such as bitscore and E-values, ML prediction scores and normalized gene abundances (TPM/RPKM), making the results easier to interpret and compare across diverse metagenomic datasets. We applied PlasticEnz to both controlled microcosm experiments and diverse field metagenomes, demonstrating its ability to discriminate plastic-exposed from pristine environments. In the Laguna Madre microcosm, PlasticEnz identified a strong enrichment of PHB depolymerase homologs in PHA biofilms, while PETase signals remained consistently low across all treatments, aligning with the findings of the original study. In plastic-contaminated soil samples, those collected in Sewapura and Varamin exhibited the highest abundance and prediction scores for both PET and PHB depolymerases, whereas pristine Kamchatka acid hot springs communities yielded negligible hits. Benchmarking against our test set, the ML classifiers achieved F1 values above 0.7 for PET and PHB, with XGBoost maximizing precision and the neural network mode with enhanced sensitivity. Sequence-level analyses further confirmed that predicted homologs clustered closely with experimentally validated reference enzymes. PlasticEnz offers a streamlined and accessible solution for identifying plastic-degrading enzymes in metagenomic data by combining homology-based and machine learning approaches. By integrating curated reference data with predictive models in a single pipeline, it enables researchers regardless of computational background to explore microbial plastic degradation potential across a wide range of environments. Results PlasticEnz database The PlasticEnz database contains 213 unique protein sequences associated with plastic polymer degradation pathways, extracted from 176 peer-reviewed studies. Each database entry includes detailed annotation of the enzyme’s gene name, location (e.g., extracellular or cell-bound), enzyme classification, operon or gene cluster, targeted bond types, and catalytic domains. Enzymes are also linked to their associated reactions (substrate-product) and source organisms, for which marker genes or whole-genome sequences are provided. Furthermore, the database integrates cross-references to external databases (UniProt, NCBI). The comparison with other available Plastizyme databases is shown in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Comparison of stored information across available plastic-degrading enzyme databases. ML model evaluation We evaluated three predictive models, the neural network, random forest, and XGBoost, for classifying plastic-degrading enzymes across several polymer substrates ( Figure 1 ). Overall, all models struggled to accurately classify PLA, PBAT, PBSA, and PCL polymers. However, for PET and PHB, both the Neural Network and XGBoost models achieved F1 values above 0.7 ( Table 2 ). The XGboost model demonstrated higher precision (0.95 and 1 for PET and PHB) than the Neural network (0.84 and 0.63 for PET and PHB), indicating a lower rate of false positive classifications (paired t-test, p-value < 0.01, t=-71.0). However, it was overall more conservative in its identification, as reflected by lower recall (0.76 and 0.64 for PET and PHB) than Neural Network (1 and 0.88 for PET and PHB) (paired t-test, p-value < 0.01, t=59.34). Download figure Open in new tab Figure 1. Bar charts depicting the mean bootstrapped (n=1000) evaluation metrics (F1, Precision, and Recall) for classification of each plastic-degrading enzyme class. Different machine learning models are represented by distinct colors, and error bars indicate the 95% confidence intervals. View this table: View inline View popup Download powerpoint Table 2. Performance metrics of Neural Network and XGBoost models for PET and PHB classification. Numbers in the brackets represent 95% confidence intervals. Therefore, PlasticEnz includes both models to accommodate different research needs: the XGBoost model, which prioritizes precision and is used as the default, and a more sensitive neural network model, available via the --sensitive flag, which maximizes recall and detection of potential candidates. PlasticEnz tool workflow description, runtime, and performance PlasticEnz identifies plastic-degrading enzymes from metagenomic data using a two-step search and optional machine learning classification. The tool accepts contigs, genomes, or protein sequences as input and screens them against a curated database using HMMER( 25 ) and DIAMOND( 20 ). Users can specify the target polymer(s) with the --polymer flag. For PET and PHB, predictions can be refined using a machine learning classifier, namely XGBoost (default) or a more sensitive neural network (activated via --sensitive). If reads or BAM files are supplied, the tool also estimates gene abundances. The output includes predicted enzyme homologs with homology and ML scores, protein sequences, and abundance estimates if applicable. The full workflow is depicted and described in Figure 2 . Download figure Open in new tab Figure 2. Overview of the PlasticEnz pipeline. PlasticEnz identifies candidate plastic-degrading enzymes in metagenomic datasets through a multi-step workflow. ( 1 ) The user specifies a target plastic polymer or combination of polymers using the --polymer flag and provides a path to one of the following: assembled contigs (--contigs), full genomes (--genome), or protein sequences (--proteins) in FASTA format. Optionally, paired-end sequencing reads or pre-aligned BAM files may be included to quantify gene abundance. ( 2 ) For nucleotide inputs (contigs or genomes), protein-coding genes are predicted using Prodigal [55] and translated to amino acid sequences. ( 3 ) The resulting proteins are screened against our custom-made HMM profiles using HMMER( 25 ) or DIAMOND( 20 ) (singleton sequences). Hits must pass default filters (E-value 20); HMMER hits are further filtered by bias score (must be <10% of bitscore). ( 4 ) Sequences that pass this homology screen are embedded using ProtBERT( 39 ) to generate contextualized feature vectors. ( 5 ) These embeddings are classified using one of two pre-trained machine learning models: XGBoost (default mode) or a neural network (sensitive mode, activated with --sensitive, optimized for recall). Predictions are returned as probabilities for each supported polymer class (currently PET and PHB). ( 6 ) If read data is provided, gene abundance is computed either via alignment-based quantification (Bowtie2( 21 ) + samtools( 40 )) or directly from sorted BAM files using internal scripts. ( 7 ) Final outputs include: a summary .csv file listing hits, homology scores, and ML prediction scores; a .fasta file with protein sequences of predicted homologs; and an optional abundance .csv file containing raw and normalized counts (RPKM, TPM, CPM). During runtime tests, the tool performed well across inputs of various types and sizes (Supplementary Table 5). For smaller inputs such as 1.2Mb single genomes or 100Mb protein files, the real runtime remained under 30 seconds. For medium-sized datasets, including genomes and proteins within 600-650Mb range, PlasticEnz workflow completed in under 4 minutes. The runtime for large datasets of nearly 1Gb remained practical, under 5 minutes for proteins and 35 minutes for contigs. Application of PlasticEnz in PHB and PET-exposed benthic biofilm communities We applied PlasticEnz in both default (XGBoost) and sensitive (Neural Network) modes to assembled contigs from PET, PHB, and ceramic biofilm communities, as well as seawater samples (H2O), to detect putative PET and PHB depolymerases. First, we compared HMMER bitscores across all sample types. PlasticEnz identified 7 putative PETases in the PET biofilm community, with an average bitscore of 43.9 (SD: 9.6) ( Supplementary Table 4 ). In comparison, the average bitscores for seawater and ceramic samples were 52.0 (SD: 12.8) and 41.7 (SD: 12.1), respectively. Next, we examined classifier prediction scores under both model settings. As expected, the sensitive model produced much higher average prediction values than the default model. For PET, seawater, and ceramic samples, average scores under the default model were 0.06 (SD: 0.05), 0.05 (SD: 0.04), and 0.04 (SD: 0.06), respectively, compared to 0.6 (SD: 0.2), 0.43 (SD: 0.2), and 0.3 (SD: 0.2) in the sensitive mode ( Figure 3A ). Finally, we quantified high-confidence predictions, defined as the number of proteins reaching prediction scores > 0.7. In the default mode, no PETases in any sample met this threshold. However, in the sensitive mode, three PETases in the seawater sample (1.48 hits per million proteins) and two in the PET biofilm sample (1.45 hits per million proteins) passed this cutoff ( Figure 3B ). No high-confidence PETases were detected in the ceramic biofilms under either setting. Download figure Open in new tab Figure 3. Predicted PET and PHB-degrading enzymes above 0.7 confidence threshold across samples (CERAMIC, PET, PHB, H2O) in Laguna madre dataset and PlasticEnz models (default, sensitive). Average prediction scores for PET ( A ) and PHB ( C ) degrading enzymes in microbial communities from PET, PHB, ceramic, and seawater samples (H2O). Predictions were generated using the default (blue) or sensitive (orange) PlasticEnz models. Number of PlasticEnz predicted putative enzymes (normalised for sample depth and expressed as number of proteins per million) with a prediction score greater than 0.7 for PET ( B ) and PHB ( D ). In the PHB biofilm community, PlasticEnz identified 826 putative PHB depolymerases with a high average HMM bitscore of 108.4 (SD: 91.1). In contrast, average bitscores were substantially lower in the seawater (56.7, SD: 29.1) and ceramic samples (60.0, SD: 36.5) ( Supplementary Table 4 ). Classifier prediction scores for PHB homologs were higher than those observed for PET, across all modes. In the default (XGBoost) mode, average prediction scores were 0.2 (SD: 0.3) for the PHB biofilm, 0.07 (SD: 0.1) for seawater, and 0.09 (SD: 0.2) for ceramic samples ( Figure 3C ). Again, these values increased substantially under the sensitive (Neural Network) mode, reaching 0.6 (SD: 0.2) for PHB, and 0.5 (SD: 0.3) for both seawater and ceramic samples ( Figure 3C ). We also quantified the number of high-confidence predictions (score > 0.7). Under the default model, 64 proteins in the PHB biofilm (38.5 hits per million proteins), 3 in seawater (1.48 hits per million), and 5 in ceramic biofilms (3.69 hits per million) surpassed this threshold. In contrast, the sensitive mode yielded more high-scoring predictions: 363 in the PHB biofilm (219 hits per million), 61 in seawater (30.1 hits per million), and 45 in ceramic samples (33.1 hits per million) ( Figure 3D ). To assess the sequence similarity between PlasticEnz-predicted PETases and PHBases and experimentally validated enzymes, we compared the top 10 predictions from each model (PHB-default, PHB-sensitive, and PET-sensitive) with curated SEED reference sequences from the PlasticEnz database. These SEED sequences represent a non-redundant set of known PET- and PHB-degrading enzymes. For the PHB-default model ( Figure 4A ), most predicted proteins showed close sequence alignment to several SEED enzymes, with an average evolutionary distance of 2.84 (range: 0.80–6.66). The closest SEED matches included depolymerases from Pseudomonas lemoignei (PHB_SEED_3), Alcaligenes faecalis (PHB_SEED_4), and Ralstonia pickettii (PHB_SEED_5, PHB_SEED_26), all with distances below 1.6. The most dissimilar hits were to enzymes from Cupriavidus necator (e.g., PHB_SEED_41, _44, _36), with distances exceeding 4.5. Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 4. Sequence similarities between top 10 highest scoring PlasticEnz predictions and database SEED references for Laguna madre dataset. Heatmaps display the alignment similarity between the top 10 highest-scoring predicted PETases and PHBases identified by PlasticEnz for PHB under default model ( A ), PHB under sensitive model ( B ) and PET under sensitive mode ( C ). PlasticEnz -identified homologs were compared against their respective non-redundant SEED reference enzymes. Pairwise evolutionary distances were computed using the LG substitution model and converted into similarity scores ranging from 0 (low similarity) to 1 (high similarity). In the PHB-sensitive model ( Figure 4B ), predicted homologs were more diverse, with a higher average evolutionary distance of 3.78 (range: 0.51–10.00), indicating broader but less conserved matches. However, some sequences still aligned well with known depolymerases, including those from Ralstonia pickettii (PHB_SEED_5, _26), Burkholderia cepacia (PHB_SEED_18), and Pseudomonas lemoignei (PHB_SEED_24). The most distant predictions again mapped to Cupriavidus necator (PHB_SEED_41, _45, _39) and Paracoccus denitrificans (PHB_SEED_31), with distances above 6.3. No PETases were found under the default model, meanwhile the PET-sensitive model ( Figure 4C ) yielded the least conserved matches overall, with an average distance of 3.99 (range: 0.58–10.00). While most predictions showed weak similarity, a few aligned moderately well with reference enzymes such as PET_SEED_13 ( Bacillus subtilis , p-nitrobenzylesterase), PET_SEED_39 (uncultured bacterium), and PET_SEED_7 ( Pseudomonas aestusnigri ), all showing average distances around 1.2–1.4. The most distant matches were to fungal PETases from Fusarium oxysporum (PET_SEED_10, _14), Fusarium solani (PET_SEED_23), and Humicola insolens (PET_SEED_22), with distances exceeding 7.4. Furthermore, we compared averaged HMM bit scores and neural network prediction scores for contigs with the highest number of SEED sequence matches (e.g., contig_593880_1 , contig_290381_1 , contig_286302_1 ) and those with the fewest (e.g., contig_1230874_4 , contig_541445_1 , contig_525814_1 ). The difference in average HMM scores between the two groups was 48.7 vs. 41.2, meanwhile the prediction scores showed 0.71 vs. 0.50. Comparison between acid-host springs and plastic-contaminated fields communities We used PlasticEnz to identify putative plastic-degrading enzymes in metagenomic samples from plastic-contaminated urban soils (Varamin, Sewapura, Ghazipur) and compared the results to those from thermophilic sediment samples collected from pristine hot springs (Hot_springs_1, _2, _3). First, we assessed the average HMM bitscores for putative homologs across all screened polymers ( Figure 5A ). Putative depolymerases from plastic contaminated sites consistently exhibited higher average bitscores across all polymers, reflecting stronger homology with the known plastic-degrading enzymes from the reference HMM profiles. In contrast, hot spring proteins had consistently lower bitscores, and several polymer groups (e.g., P3HP, PEA in Hot_spring_3; PES, PET in all samples; PHB in Hot_spring_1) had no putative homologs at all. Download figure Open in new tab Download figure Open in new tab Figure 5. PlasticEnz plastic-degrading enzyme homologs from plastic-contaminated urban soil (Ghazipur, Sewapura, Varamin) and pristine hot springs samples (Hot_spring_1–3). ( A ) Heatmap showing average HMMER bitscores for predicted plastic-degrading enzymes across sites and all screened polymers. Sites from pristine hot springs are in blue and plastic contaminated soil samples in red. ( B ) Number of proteins (expressed as hits per million proteins) predicted by PlasticEnz ML component (default mode) as putative PET (brown) or PHB (green) depolymerases at 0.7 prediction score. ( C–E ) Bar plots showing the number of proteins (expressed as hits per million proteins) for each screened polymer and site identified at different HMM bitscore thresholds: >100 ( C ), >80 ( D ), and >50 ( E ). Next, we quantified the number of putative plastic depolymerases per site that exceeded HMM bitscore thresholds of 100, 80, and 50, representing high, medium, and low sequence similarity to the PlasticEnz HMM motifs, respectively ( Figure 5C–E ). Across all thresholds and polymer types, PlasticEnz consistently detected more homologs in plastic-contaminated soils than in hot spring samples. After normalizing for sequencing depth, Sewapura had the highest number of detected depolymerases, followed by Ghazipur and Varamin. As expected, relaxing the bitscore threshold increased the number of hits. For example, the number of predicted PLA-degrading enzymes in hot spring samples rose from 3 (bitscore > 100), to 23 (bitscore > 80), and 99 (bitscore > 50). At the lowest threshold (bitscore > 50), the total number of predicted depolymerases also increased across other polymer classes in the pristine hot springs, reaching 4 for PBAT, 47 for PBSA, 14 for PCL, 4 for PHBV, and 46 for PLA. Furthermore, we examined the PlasticEnz prediction module by quantifying the number of proteins classified as high-confidence PET or PHB depolymerases (prediction score > 0.7) under the default model ( Figure 5B ). Only plastic-contaminated sites were considered, as the highest prediction score for proteins in the hot spring samples was 0.0001. As expected, across all contaminated sites, PHB depolymerases were more abundant than PETases. Sewapura exhibited the highest number of high-confidence hits, with 7.28 PHB and 2.43 PET depolymerases per million proteins. Varamin followed with 6.23 PHB and 0.78 PET hits per million. Ghazipur showed the lowest abundance of PHB depolymerases (5.16 per million) and no PETases exceeding the 0.7 threshold. Finally, we assessed the sequence similarity between PlasticEnz -predicted PETases from contaminated sites and experimentally validated PET-degrading enzymes from the PlasticEnz SEED reference database ( Figure 6 ). We focused on two subsets: (i) PETases predicted with high confidence by the default machine learning component (prediction score > 0.7; Figure 6A ), and (ii) PETases identified using the stringent HMM bitscore threshold (>80; Figure 6B ). Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 6. Evolutionary relatedness of PlasticEnz predicted PETase homologs to known PET-degrading enzymes. ( A, B ) Principal Coordinates Analysis (PCoA) based on Fitch distances showing sequence-level similarities between PlasticEnz -predicted putative PETases (green) and database-derived SEED PETase homologs (black) under high-confidence ML predictions (≥ 0.7) ( A ) or HMMER bitscore > 80 (B ). ( C, D ) Corresponding phylogenetic trees derived from Clustal Omega alignments and built with FastTree under the LG substitution model, showing clustering of predicted PETases (green) with SEED reference enzymes (black). In the ML-predicted group, sequences from Varamin, Sewapura, and Ghazipur clustered closely with multiple SEED PETases on the PCoA plot, suggesting high sequence similarity (PERMANOVA on the clusters, p > 0.05, R² = 0.020; beta-dispersion < 0.05). The sequences displayed strong similarity with diverse enzymes, including PET_SEED_51 ( Nocardioidaceae ), PET_SEED_60 ( Marinactinospora thermotolerans ), PET_SEED_53 ( Saccharopolyspora flava ), PET_SEED_47 ( unknown bacterium ), and PET_SEED_48 ( Micromonosporaceae )( Figure 6C ). The mean pairwise distance between the predicted sequences and SEED references was 0.56 ± 0.12. In contrast, sequences identified using the HMM bitscore threshold formed a more distinct clade, with stronger homology to only two SEED PETases: PET_SEED_13 (p-nitrobenzylesterase from Bacillus subtilis ) and PET_SEED_39 (unknown PET hydrolase from an uncultured bacterium) ( Figure 6D ). These sequences were more divergent from the broader SEED reference set, as reflected by a higher mean pairwise distance (0.74 ± 0.04), and clear separation in the PCoA space ( Figure 6B ). Discussion Our study introduces PlasticEnz , a new bioinformatics tool designed to help identify potential plastic-degrading enzymes within complex microbial communities from metagenomic datasets. At the core of PlasticEnz is a carefully curated database containing experimentally confirmed plastic polymer degradation enzymes. This database allowed us to create our custom Hidden Markov Models (HMM) and provided a resource for training a machine learning classifier used by PlasticEnz to provide prediction scores for putative PET and PHB depolymerases. While most existing tools focus broadly on identifying general enzyme families such as esterases, cutinases, lipases etc., PlasticEnz leverages collected information about plastic degradation enzymes to pinpoint their potential homologs by combining quality HMM and trained ML classifier predictions. Training machine learning classifiers required clearly defined positive and negative sets. For the positive set, i.e. ground truth sequences shown to degrade plastic polymers experimentally, we used the data from PlasticEnz and PlasticDB( 18 ) databases. However, creating a reliable negative, defined as a set of sequences of distantly homologous proteins without proven ability to degrade plastic polymers was challenging due to the widespread presence of plastic contamination across all the planet’s environments( 41 – 43 ). To overcome this, we used sequences from well-characterized bacteria that depend on hosts or live parasitically, making them unlikely to synthesise extracellular enzymes targeting plastic polymers. Furthermore, given the limited size of our dataset, we avoided deep learning architectures, which are prone to overfitting under data-scarce conditions( 44 , 45 ). Instead, we employed simpler, more interpretable models such as decision trees, gradient-boosted trees (XGBoost), and a shallow neural network (single hidden layer). To further reduce overfitting risk and improve generalizability, we incorporated regularization techniques such as early stopping and dropout for the neural network( 46 , 47 ) and hyperparameter optimization for all models. In line with previous findings( 37 ), which evaluated thirteen classifiers on plastic degradation enzyme classification problems, XGBoost emerged as the most suitable model. Overall, all models showed strong performance for two polymers: poly(ethylene terephthalate) (PET) and polyhydroxybutyrate (PHB), but consistently underperformed on others like PLA, PBAT, PBSA, and PCL. For these underrepresented classes, precision, recall, and F₁ scores were consistently low and exhibited wide confidence intervals. This is a common effect of class imbalance, where the number of true positive examples is greatly outweighed by negative instances. As the size of the positive set increases, performance metrics such as precision and recall improve accordingly, which is supported by a positive correlation coefficient (Pearson, r = 0.81) between the F₁ score and the number of sequences in the positive set. These results highlight a key limitation in training ML models for plastic degradation classification: the limited number of ground truth sequences. As more enzymes are identified and incorporated into the training set, model performance is expected to improve accordingly. Therefore, to ensure reliable predictions, the PlasticEnz prediction module is limited to PET and PHB, the only polymers with sufficient training data. Users can choose between a default XGBoost model optimized for precision, or a more sensitive neural network model that emphasizes recall, allowing flexibility based on research priorities. To showcase PlasticEnz capabilities, we applied it to two distinct experimental setups: (1) a controlled microcosm study conducted in a hypersaline lagoon (Laguna Madre), and (2) field samples from plastic-contaminated and pristine environments. In the Laguna Madre dataset, originally analyzed by Pinell et al., 2022( 48 ) metagenomic analysis focused on biofilm communities growing on PET, PHA, and ceramic substrates over 28 days. Consistent with the original findings, PlasticEnz did not detect significant enrichment of PET-degrading enzymes in PET biofilms compared to seawater or ceramic controls. No high-confidence PETase was found in any sample under the default mode, and even under the sensitive mode, PET biofilms did not show increased PETase predictions relative to seawater. This aligns with Pinell’s interpretation that the remote location and low plastic exposure likely limited the selection pressure for PET-degrading enzymes in these communities. In contrast, the original study reported that communities extracted from PHA biofilms were significantly enriched in PHB depolymerases in comparison to control biofilms. Using PlasticEnz , we identified over 800 putative PHB depolymerases in these communities, with significantly higher average HMM bitscore and ML prediction scores relative to control samples. Notably, heatmaps showed that many of these predicted enzymes shared high sequence similarity to experimentally validated entries in the SEED database. However, in-vitro assays will be required to confirm their functional activity. To further demonstrate the capabilities of PlasticEnz , we applied the tool to screen microbial communities from two contrasting environments. While plastic-contaminated urban soils of Varamin, Sewapura and Ghazipur served as representative examples of polymer-enriched ecosystems, identifying a truly pristine, plastic-free environment proved challenging due to the ubiquity of microplastics ( 27 , 28 , 41 – 43 , 49 , 50 ). We selected the geothermal area of the Mutnovsky volcano in Kamchatka, a remote region with minimal anthropogenic activity, as a proxy for a low-contamination environment. This site is characterized by extreme physicochemical conditions, including broad gradients in temperature and pH( 51 ). Due to limited organic carbon sources, these communities are dominated by obligately or facultatively chemolithoautotrophic bacteria and the presence of hydrolytic enzymes capable of degrading complex plastic polymers was expected to be minimal( 52 , 53 ). As expected, plastic-contaminated soils contained putative plastic-degrading homologs with higher average HMM bitscores and greater numbers of depolymerases than pristine hot spring samples. The strongest enrichment was seen in Sewapura, followed by Ghazipur and Varamin. Notably, all sites, including hot springs, showed elevated PLA depolymerase signals. This is because most PLA-degrading enzymes belong to the serine protease family, a group of diverse and taxonomically widespread enzymes commonly found across bacterial, fungal, and archaeal taxa( 8 , 54 – 56 ). Next, we evaluated the PlasticEnz default prediction module using a classification threshold of 0.7. None of the putative PET or PHB homologs from the hot spring samples met this threshold; in fact, all prediction scores were below 0.1. Among the plastic-contaminated sites, PETase predictions exceeding the 0.7 confidence threshold were observed in communities from Sewapura and Varamin but not in Ghazipur. Meanwhile, high-confidence predictions of PHB depolymerases were common to all sites. This likely reflects underlying biological differences: PHB depolymerases are widespread due to the role of PHA polymers in microbial carbon and energy storage, whereas PET degradation requires evolved adaptations of esterases or cutinases ( 7 , 14 ). As a result, PET-degrading enzymes remain rare and are typically associated with long-term plastic exposure. Lastly, we conducted a comparison study by clustering PlasticEnz- identified putative PET homologs to previously reported and functionally validated PETases (SEED sequences). This analysis was split into two comparisons: one including high-scoring homologs (HMM bitscore > 80), and a second including sequences assigned a prediction score greater than 0.7 by the XGBoost classifier (default mode). ML-predicted PETase homologs from plastic-contaminated sites clustered with several known reference PETases, while the high HMM score sequences formed a distinct clade associated with two particular SEED reference enzymes. These results demonstrate that the PlasticEnz prediction module can identify candidate PETases with strong evolutionary ties to a broader range of validated enzymes, extending beyond the reach of traditional homology-based methods like HMMs, especially in complex metagenomic datasets. However, it is also possible that the model may be biased toward well-represented sequence patterns in the training data, potentially reducing its ability to detect PETases that were underrepresented in the training set. While PlasticEnz offers a streamlined and accessible framework for identifying putative plastic-degrading enzymes, several limitations should be considered. First, the presented analysis focused on high-confidence predictions based on stringent HMM bitscore and ML probability thresholds; users are encouraged to apply similar cutoffs to ensure reproducibility. Additionally, the speed of the pipeline scales with dataset size, and RAM requirements increase accordingly, particularly during ProtBERT embeddings and contigs translation to proteins. To address this, a --gpu option is provided to accelerate tokenization, and the tool allows the use of multiple cpu cores via the --cores flag. However, since prodigal is not optimised to execute on multiple cores, the users are advised to adjust computational resources as needed for bigger datasets (over 1Gb). Importantly, PlasticEnz relies on sequence homology, and its predictions remain putative until validated through in vitro assays. The accuracy and breadth of the tool are ultimately constrained by the number and diversity of experimentally verified plastic-degrading enzymes available for model training. As the field advances and more polymer-degrading enzymes are experimentally validated, PlasticEnz will be continuously updated with expanded HMM profiles and retrained machine learning classifiers, enhancing its predictive accuracy, polymer coverage, and utility for metagenomic screening in diverse environments. Materials and Methods Curation and processing of plastic-degrading enzyme sequence data We systematically collected data on experimentally confirmed plastic degradation enzymes, including their protein sequences, host organisms, reaction details (including catalysts and conditions), references to relevant publications, and cross-references to external databases such as UniProt( 57 ), NCBI( 58 ), and KEGG( 59 ). This comprehensive information was extracted from published literature and stored in a PlasticEnz SQL database (available within the PlasticEnz package). To enhance our dataset, we also incorporated data from PlasticDB( 18 ), a publicly available database specializing in plastic degradation enzymes. In total, the two combined databases resulted in 422 protein sequences of diverse plastic-degrading enzymes. These protein sequences were then pooled together into combined fasta files based on their respective polymer substrate. To eliminate redundancy caused by the database merge, we clustered each combined fasta at 95% similarity using CD-HIT2( 60 ). Following clustering, multiple sequence alignments (MSA) were performed for each grouped set of unique proteins using T-Coffee in expresso mode( 61 ), which integrates protein structure. The resulting alignments were refined to maintain only those with average to good alignment score (TCoffee alignment score > 50). Sequences that failed to align adequately were pressed into the DIAMOND database( 20 ) (Supplementary Table 2). MSAs were used to generate HMM profiles using the built-in hmmbuild function from the HMMER suite( 25 ) (Supplementary Table 2). Preparation of training and test data sets To build the negative dataset, we used our previously generated HMM profiles to identify homologous sequences in bacterial genomes that are unlikely to be involved in plastic degradation activity. Specifically, we focused on representative Refseq genomes from well-studied host-dependent or parasitic bacteria (e.g., Escherichia coli , Chlamydia trachomatis , Staphylococcus aureus ) (Supplementary Table 3). The resulting distant homologous sequences formed the negative dataset (410 sequences). For the positive dataset, we used our previous set of experimentally confirmed bacterial plastic-degrading enzymes. Positive and negative datasets were combined and clustered at 95% identity with CD-HIT2( 60 ) to obtain 502 unique clusters. To avoid the presence of highly similar sequences between training and test datasets, we randomly split entire clusters into either the training set (80%, 606 protein sequences) or the test set (20%, 200 protein sequences). The feature matrix used for the model training contained one-hot encoded annotations indicating the specific plastic polymer(s) degraded by each enzyme. Embeddings generation with ProtBERT To generate protein embeddings, we used ProtBERT( 39 ), a pre-trained transformer-based model specifically trained on protein sequences. ProtBERT and its tokenizer were loaded from the Hugging Face Transformers library. Sequences were preprocessed by trimming ambiguous residues (’X’, ‘x’) from sequence ends, verifying that only standard amino acids (ACDEFGHIKLMNPQRSTVWY) were present, and formatting each sequence by inserting spaces between individual residues. ProtBERT generated contextual embeddings for each amino acid, which were then aggregated into a fixed-length embedding vector for each protein by mean pooling across the sequence length. Machine Learning Model Selection and Evaluation Full training procedures, hyperparameter settings, and performance metrics are detailed under ‘ Machine Learning Model Selection and Evaluation’ in ‘ Supplementary Materials & Methods 1’ . We evaluated three classifiers—neural network, Random Forest, and XGBoost—using precomputed protein embeddings and one-hot encoded features. All models were trained on a multi-label dataset for PET and PHB degradation using an 80/20 train/test split. Hyperparameters were optimized via grid search and validated on held-out data. Final models were retrained on the full training set and evaluated on the independent test set using precision, recall, and F1-score(Supplementary text 1). Model performance (F1-score, precision and recall) was compared between Neural Network and XGBoost classifiers using paired t -tests. Normality of metric distributions was confirmed using Shapiro–Wilk tests (p > 0.05). Mean, standard deviation, test statistics, and p-values were reported for each metric. Models were implemented and trained with PyTorch, scikit-learn, and XGBoost libraries. Prediction scores from XGBoost and neural network models are included in the PlasticEnz module and represent per-class probabilities for each polymer. Evaluation sets A full description of datasets, processing steps, and analysis parameters is provided under ‘ Evaluation sets ‘ in Supplementary Materials & Methods .” In brief, we analyzed paired-end Illumina metagenomes from a 28-day microcosm study by Pinnell et al. (2019) [NCBI: PRJEB15404]( 48 ), which examined biofilm communities developed on PET, PHA, and ceramic beads in a hypersaline lagoon. Four sample groups were used: PET, PHA (PHB), ceramic, and seawater controls (H 2 O). Raw reads were quality-filtered with Fastp [43] (default settings) and assembled using MEGAHIT (default settings) [44] . Assembled contigs were used directly as an input for PlasticEnz . Hit counts for PHB/PET depolymerases were normalized per million predicted proteins for comparison across samples. To assess functional similarity, top 10 enzyme predictions from PET-sensitive, PHB-default, and PHB-sensitive models were compared to PlasticEnz ’s curated SEED reference set (CD-HIT, 90% identity). Sequences were aligned with MUSCLE ( 64 ), trimmed using trimAl (–automated1 setting)( 65 ), and evolutionary distances were calculated with the LG model [47] in phangorn (R). Normalized pairwise distances were visualized as similarity heatmaps. We validated PlasticEnz custom HMM motifs using datasets from environments with contrasting plastic exposure levels. Thermophilic hot spring sediment samples from Kamchatka, Russia (NCBI: PRJNA419239), served as a low-exposure control, while plastic-contaminated soil microbiomes from India and Iran, including Sewapura (NCBI: PRJNA1077790)( 67 ), Varamin (NCBI: PRJNA924045)( 68 ), and Ghazipur (NCBI: PRJNA388130), served as high-exposure test sites. Downloaded raw paired-end reads were processed identically to Laguna madre samples. High-confidence PETase predictions (prediction score > 0.7 (default model) or HMMER bitscore > 80) were aligned with SEED references using Clustal Omega ( 69 ). Sequence similarity was assessed with Fitch distances( 70 ) ( seqinr )( 71 ) and visualized using Principal Coordinates Analysis ( ape )( 72 ). Phylogenies were generated with FastTree ( 73 ) using trimmed alignments ( trimAl ( 65 ), gap threshold 0.5). Analyses were performed in R (v4.2) and Python (v3.11.11). Visualizations were generated using ggplot2 , ggpubr , patchwork and pheatmap . Code availability and supporting information PlasticEnz is freely available at https://github.com/msysbio/PlasticEnz All the raw data, training sets and scripts are available at 10.5281/zenodo.15395662 Author contributions AK developed the PlasticEnz tool, performed all analyses, and wrote the original manuscript draft. KF contributed to the method development, interpretation and supervised the project. KF and AK reviewed and edited the manuscript. JS helped with developing an early version of the tool. All authors read and approved the manuscript. Funding Fonds Wetenschappelijk Onderzoek (FWO) PhD fellowship 1S03725N. Acknowledgments We thank Maxime Greffe and Hubert Krukowski for kindly testing the tool. 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