MAERM has the potential to reduce the experimental cost of measuring enzymes’ catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.
Abstract
Harnessing enzyme specificity requires a thorough understanding of enzyme promiscuity, which determines enzymes’ catalytic scope; however, measuring this scope still relies heavily on labor-intensive analytical approaches. While data-driven approaches have emerged to predict the catalytic scope of enzymes, these methods continue to face challenges such as restricted datasets and insufficient integration of enzyme structural information and reaction transformations. Here, we introduce MAERM, an innovative mixed-attention model designed to predict enzyme-reaction matching relationships. Built on our MAERM-DB, a dataset with broad coverage of validated and chemoenzymatic catalysis data, MAERM utilizes a local-global attention module to integrate multimodal enzyme information with fine-grained reaction representations, thereby predicting enzyme-reaction matching probabilities. Results show that MAERM consistently outperforms all baselines, with an average F1-score of 0.984. Notably, on challenging test samples with less than 40% sequence identity to the training set, MAERM outperforms the second-ranked model by 5.9% in F1-score. In addition, MAERM achieves the highest top-10 success rate of 51.7% on Enzyme-405 and the highest balanced accuracy of 0.697 on BioCat-547, further supporting its generalizability in enzyme screening and chemoenzymatic catalysis. Finally, MAERM can serve as an efficient scoring module. When integrated with ProteinMPNN, MAERM has successfully guided novel enzyme design for two carbonyl reduction reactions, resulting in enhanced catalytic potential for the native substrate and demonstrating broad compatibility. Overall, MAERM has the potential to reduce the experimental cost of measuring enzymes’ catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.
Results support the use of pretrained protein language model embeddings as an effective foundation for enzyme annotation by combining large-scale sequence representations with a lightweight supervised classifier and may facilitate functional annotation of protein sequences derived from large genomic and metagenomic datasets.
Enzymes present a sustainable alternative to traditional chemical industries, drug synthesis, and bioremediation applications. Because catalytic residues are the key amino acids that drive enzyme function, their accurate prediction facilitates enzyme function prediction. Sequence similarity-based approaches such as BLAST are fast but require previously annotated homologues. Machine-learning (ML) approaches aim to overcome this limitation; however, current gold-standard ML-based methods require high-quality 3D structures limiting their application to large datasets. To address these challenges, we developed Squidly, a sequence-only tool that leverages contrastive representation learning with a biology-informed, rationally designed pairing scheme to distinguish catalytic from non-catalytic residues using per-token Protein Language Model embeddings. Squidly surpasses state-of-the-art ML annotation methods in catalytic residue prediction while remaining sufficiently fast to enable wide-scale screening of databases. We ensemble Squidly with BLAST to provide an efficient tool that annotates catalytic residues with high precision and recall for both in- and out-of-distribution sequences.
W. J. Rieger, Mikael Bodén, Frances H. Arnold et al.· eLife· 0 citations
The enzyme kinetic parameters, including the turnover number, Michaelis constant, and inhibition constant, are key metrics for assessing catalytic performance. Although deep learning models have recently incorporated multimodal information from enzymes and substrates to predict these parameters, several obstacles still persist. First, current data sets suffer from limited size, inconsistency, and a lack of unified standards. Second, most existing approaches prioritize cross-modal consistency but fail to sufficiently exploit the unique information residing in each individual modality. Meanwhile, although a limited number of studies have recognized that collaborative exploration of shared and specific information can enhance model performance, these methods remain difficult to directly apply to enzyme-substrate pairs, as enzyme-substrate relationships are inherently interactive rather than semantically equivalent counterparts. Third, the measured kinetic parameters are often unevenly distributed, which severely undermines the predictive accuracy of existing models when dealing with extreme value ranges. To resolve the above challenges, we first compile Kinetic-DB, a large-scale and consistently formatted data set from public resources. Building upon this data set, we develop GAPEK, a new framework for estimating enzyme kinetic parameters. In particular, an adaptive data augmentation module is devised to enrich the diversity of both enzyme and substrate sequences, thereby alleviating the adverse effects of data imbalance. Subsequently, we perform feature extraction using two pretrained models, ESM-2 for enzymes and Mole-BERT for substrates, to obtain multimodal embeddings. To decouple these complex interacting features, we introduce a tailored dual information exploration module to capture both modality-specific and cross-modal information, further refined by domain classification and distribution alignment loss functions. To explicitly handle the imbalanced data distribution, our base model, GAPEK, incorporates an adaptive density-weighted loss function. Building on this, we propose GAPEK+, which integrates the Squared Error Relevance Area (SERA) function to reconfigure the learning objective. By prioritizing high-relevance regions, GAPEK+ effectively calibrates the model's sensitivity to rare but critical extreme values, substantially mitigating the prediction bias inherent in heavy-tailed regression tasks. Experimental results demonstrate that both GAPEK and GAPEK+ achieve superior performance over existing state-of-the-art approaches, particularly across extreme parameter ranges, highlighting their potential as valuable tools applicable to enzyme engineering, synthetic biology, and drug discovery.
Cheng-Hao Zhu, Weiping Ding, Wei Zhang et al.· Journal of Chemical Informat...· 0 citations
It is demonstrated that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and it is proposed that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology.
João Sartori, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado· bioRxiv· 0 citations
Promiscuous enzymes catalyze multiple biochemical reactions, but predicting their substrate profiles remains challenging because annotations are incomplete, reliable negative labels are scarce, and enzyme–substrate relationships are inherently multilabel. Here, we present PreSEPM (Predictive Substrate Explorer for Promiscuous Enzymes), a sequence-only, closed-set framework that combines pretrained protein representations, Gaussian mixture modeling, cluster–substrate alignment, and Bayesian optimization of decision thresholds. PreSEPM operates within a predefined substrate panel and does not require explicit substrate or structural descriptors. On a UniProt-derived triacylglycerol lipase data set, PreSEPM achieved an AUROC of 0.86, an AUPRC of 0.73, and a maximum F1 score of 0.71, outperforming the evaluated conventional machine learning baselines. On an independent high-throughput lipase screen, PreSEPM also outperformed a strong single-task logistic-regression baseline under enzyme-wise cross-validation, increasing Macro-AUPRC from 0.65 to 0.68 and F1 max from 0.63 to 0.69. Under the cross-data set setting, SMOTE-based augmentation increased AUPRC from 0.62 to 0.70. These results support PreSEPM as a useful sequence-based baseline for substrate profile completion and experimental prioritization within promiscuous enzyme families when annotations are sparse and structural or ligand-level information is unavailable.
Rong-Sheng Gao, Cheng-Ye Duan, Nan Qin et al.· ACS Omega· 0 citations
Identifying enzymes capable of catalyzing specific chemical transformations across large sequence databases remains a major challenge in biocatalyst discovery. Conventional fingerprint-based methods capture global molecular structure but fail to represent bond-breaking and bond-forming events, limiting generalization to structurally novel reactions. We introduce a dual-track evaluation framework to distinguish true generalization from memorization, assessing retrieval on structurally isolated reactions (n = 50) within a 63,259-sequence enzyme pool. The strongest fingerprint baseline achieves R@10 = 0.020. To address this limitation, we develop GATv2-ECR, a heterogeneous dual-tower model integrating reaction-center graph encoding, a frozen ESM-2 sequence encoder, contrastive learning, and EC-aware soft reranking. GATv2-ECR achieves R@10 = 0.160 on isolated queries and R@10 = 0.308 on an out-of-distribution subset (n = 39), capturing mechanistically relevant features and supporting generalizable enzyme retrieval under open-world conditions.
Si-Yuan Wang, Dan Wang, Ying Ren et al.· Journal of Chemical Informat...· 0 citations
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