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REVIEW 3 major objections 8 minor 24 references

Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives

T0 review · 3 major / 8 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Machine learning turns enzyme mining into a scalable, predictive discovery framework.

desk verdict A serviceable, wide-ranging review of ML tools for enzyme mining that is honestly self-limited in the body but overstates itself in the abstract. read the letter →

arxiv 2507.07666 v1 pith:HDVKKXRY submitted 2025-07-10 q-bio.BM

Yanzi Zhang , Felix Moorhoff , Sizhe Qiu , Wenjuan Dong , David Medina-Ortiz , Jing Zhao , Mehdi D. Davari This is my paper · ORCID
classification q-bio.BM
keywords enzymeminingmachinelearningfunctionalannotationproteinlanguagemodelsbiocatalystdiscoverymetagenomicspropertypredictionECnumber
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Enzyme mining is the effort to find useful biocatalysts inside the enormous, mostly unannotated space of protein sequences. This review argues that machine learning has turned that effort into a scalable and predictive framework: models can now assign Enzyme Commission numbers, Gene Ontology terms, substrate specificity, kinetic parameters, optimal temperature and pH, and solubility directly from sequence. The authors survey the model landscape and selected case studies, and propose a modular pipeline in which protein language model embeddings, functional classifiers, property estimators, and multi-objective scoring prioritize candidates for experimental validation. They also list the obstacles that remain, above all data scarcity, dataset bias, and poor generalization to sequences without close homologs. The payoff, if the claim holds, is that computational prioritization can replace much of the slow cultivation-based screening in biocatalyst discovery.

What carries the argument

The load-bearing mechanism is the protein language model embedding: a neural network trained on vast numbers of protein sequences converts each enzyme into a vector that encodes sequence-function relationships, and these vectors define a latent space in which clustering reveals underexplored or functionally divergent regions. Around that core, the paper assembles functional classifiers (EC number, GO term, substrate specificity) and property estimators (kinetic constants, optimal temperature and pH, solubility) whose outputs feed a multi-objective scoring function that ranks candidates by novelty, predicted promiscuity, and user-defined application traits. That combined machinery lets the pipeline prioritize sequences that homology search would miss, and the closed loop of experimental feedback is what the authors claim will make discovery autonomous.

What would settle it

Take a large metagenomic enzyme pool, run the proposed pipeline for a specific target reaction, and experimentally test the top-ranked candidates while also testing candidates chosen by sequence similarity and by random selection. If the ML-ranked set does not show a clearly higher experimental hit rate across several enzyme families, the claim that ML makes enzyme mining scalable and predictive is not supported.

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Extended reading notes

Core claim

The paper's central claim is that ML-guided enzyme mining now offers a scalable and predictive route to novel biocatalysts, in contrast to cultivation-based discovery and homology-only annotation. To support this, it catalogues machine learning models across functional annotation (EC numbers, GO terms, substrate specificity) and property prediction (Km and kcat, thermostability and thermophilicity, optimal temperature and pH, solubility), and shows through case studies of PET hydrolases, mycotoxin-degrading enzymes, terpene synthases, and phage lysins that predicted candidates repeatedly survive experimental validation. The proposed framework makes the claim concrete: build a targeted enzyme pool, embed sequences with pretrained protein language models, analyze the latent space for underexplored clusters, annotate with classifiers and estimators, rank by novelty, promiscuity, and application traits, validate experimentally, and feed results back into the models. The authors are careful to frame this as the direction of the field rather than a finished system; the unresolved limits they name are data scarcity, dataset bias, limited interpretability, and weak generalization to de novo or low-homology sequences.

Load-bearing premise

The framework assumes that machine-learning predictions on uncharacterized sequences that look unlike any known enzyme are accurate enough that the top-ranked candidates really do work when tested in the lab.

Editorial extensions

If this is right

  • If ML-guided prioritization works at scale, experimental screening effort shifts from searching broadly to validating a shortlist, cutting cost and time per discovery.
  • Sequence-only models make enzymes from uncultured and extremophilic microbes accessible to mining, since metagenomic DNA can be analyzed without cultivation.
  • Including substrate and product information in models should improve prediction of promiscuous enzymes and complete reaction outcomes, not just single enzyme-substrate pairs.
  • Multi-task and multi-modal learning, plus positive-unlabeled methods, offer concrete routes around the data-scarcity and annotation-bias problems the paper documents.
  • A closed-loop pipeline that returns experimental results into model training should progressively improve generalizability and move enzyme discovery toward autonomous platforms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If this claim is right, the field's critical bottleneck shifts from discovery to benchmarking: standardized metagenomic pools with measured experimental hit rates per enzyme family would be the infrastructure needed to decide which models truly generalize.
  • The same latent-space embeddings used to mine enzymes could double as fitness landscapes for directed evolution or generative design, linking discovery to engineering more tightly than the paper's pipeline states.
  • One testable extension is to apply the pipeline to enzymes whose substrates are rare or poorly annotated, where negative data are scarce; success there would strengthen the case more than another benchmark on well-studied families.
  • Because most property predictors are trained on a small number of expression hosts, transferring the framework to non-model expression systems is an open risk the paper flags only implicitly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. This manuscript is a review of machine-learning approaches for enzyme mining, covering functional annotation (EC numbers, GO terms, substrate specificity) and prediction of enzymatic properties (kinetic parameters, thermophilicity, optimal temperature/pH, solubility). It surveys a large number of tools and models, supplemented by eight tables in the Supplementary Material, and presents several case studies of ML-guided enzyme discovery, including PET hydrolases, mycotoxin-degrading enzymes, terpene synthases, and phage lysins. The paper also discusses current limitations—data bias, generalizability, interpretability—and proposes a modular, ML-guided enzyme-mining pipeline in Section 4.1. The abstract concludes that these developments 'establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts.'

Significance. The main value of the paper is as a structured survey: it organizes a rapidly growing literature, gives a broad inventory of models and their input/output types, and the supplementary tables provide a useful reference for practitioners. The authors are also explicit about several persistent challenges, which is a strength for a review in this area. However, the paper's headline conclusion overstates what the evidence supports. The proposed pipeline in Section 4.1 is not implemented or evaluated, and the paper's own Section 3.3 acknowledges that current predictors underperform on de novo and low-homology sequences. If the conclusion is tempered to present ML-guided enzyme mining as a promising and accelerating direction rather than an established scalable framework, and if the Section 4.1 pipeline is clearly labeled as a proposal with a discussion of validation needs, the manuscript would be a solid and useful contribution.

major comments (3)
  1. [Abstract; §3.3; §4.2] The abstract's assertion that 'these developments establish ML-guided enzyme mining as a scalable and predictive framework' is not supported by the body of the paper. Section 3.3 states that models trained solely on annotated sequences 'often underperform on de novo sequences without close homologs, a common scenario across all predictors so far,' and Section 4.2 repeats that dataset bias leads to 'reduced generalizability to novel or low-homology sequences.' The case studies in Section 3.4 are selected successes, not a systematic demonstration of scalability or predictive reliability. Please revise the abstract and the concluding statements in Sections 4.2 and 5 to frame ML-guided enzyme mining as a promising direction or an emerging capability, and make the claimed status of the framework explicit rather than asserted.
  2. [§4.1; Data Availability] The proposed ML-guided pipeline is presented as an integrated modular framework, but it is not implemented or evaluated end to end, and the Data Availability statement confirms that the work 'does not produce, collect or use datasets, training models, and novel results.' This is acceptable for a perspective paper, but the manuscript should clearly state in Section 4.1 that the pipeline is a conceptual proposal. As written, the benefits of some steps—for example, latent-space expansion and multi-objective scoring—are described in confident terms as if their effectiveness were established. Please add a discussion of the validation steps needed to test this pipeline, such as benchmark datasets, comparison against homology-based ranking baselines, and criteria for evaluating closed-loop improvement.
  3. [§3.2.2; §3.4] Several performance claims in the main text are reported without sufficient evaluation context, which weakens the paper's reliability as a survey. For instance, Section 3.2.2 states that ThermoFinder 'pushed predictive accuracy beyond 98%' without specifying the dataset, the split, or the comparison baseline, and the DeepMineLys case study reports an F1-score on an independent validation set but does not describe how that set was constructed or whether homology-based screening was compared. Given the paper's central claim of a 'predictive framework,' the review should either provide basic evaluation context for the highlighted tools or explicitly caution that reported accuracies are not directly comparable across different benchmarks and datasets.
minor comments (8)
  1. [§2] The sentence 'Feedback from experimental results is can be used to refine selection criteria' contains a typo; it should read 'Feedback from experimental results can be used'.
  2. [§3.2.2] The phrase 'Seq2Topt extents applicability' should be 'Seq2Topt extends applicability', and 'adaption' should be 'adaptation'.
  3. [§3.3] There are several typographical issues in this section: 'over representation' should be 'overrepresentation', 'such asKm andkcat' should be 'such as Km and kcat', and 'underperform onde novo sequences' should be 'underperform on de novo sequences'.
  4. [§3.3] The sentence beginning 'These imbalances biases toward learning algorithms' is ungrammatical; consider rewriting as 'These imbalances bias learning algorithms toward dominant enzyme families and reduce model performance on understudied or novel proteins.'
  5. [§3.2.1] There is a missing space in 'applications(Ariaeenejad et al., 2024)'; it should read 'applications (Ariaeenejad et al., 2024)'.
  6. [§3.3] The phrase 'undocumented substrate ranges further compromise' is missing a comma; it should read 'undocumented substrate ranges, further compromise'.
  7. [§4.2] The phrase 'the limitations of available training data' could be made more specific; based on the surrounding text, 'the composition and coverage of available training data' would be more accurate.
  8. [§3.4] A summary table of the case studies in Section 3.4—including target enzyme family, ML method, training set size, validation metric, and number of experimentally confirmed hits—would improve the readability and comparative value of the review.

Circularity Check

0 steps flagged · score 1.0 of 10

No circularity found; the review makes no derivation, and self-citations are illustrative rather than load-bearing.

full rationale

This is a narrative review, not a derivation chain. It fits no parameters, trains no models, and reports no predictions; the Data Availability statement itself says 'This work does not produce, collect or use datasets, training models, and novel results.' Section 4.1 proposes a conceptual ML-guided pipeline, but the paper does not claim that the pipeline's outputs are derived from the cited tools by construction, and no end-to-end run is presented. Self-citations (Medina-Ortiz et al. 2024a-c, 2025; Qiu et al. 2025; Zhao et al. 2023; Siedhoff et al. 2020) appear as examples of existing methods and case studies, but they are not used to forbid alternatives, to import a uniqueness theorem, or to define a predicted quantity in terms of the fitted input. The abstract's closing statement that ML-guided enzyme mining is 'a scalable and predictive framework' is a survey-level synthesis, not an equation-level result. Even if the paper's own Section 3.3 caveats about generalization to de novo and low-homology sequences make that conclusion arguable, that is an evidence or correctness concern, not circularity. No Eq. X = Eq. Y by construction, no fitted input renamed as prediction, and no self-citation chain that forces the central claim. Score 1 reflects only the presence of several self-citations in illustrative roles.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper makes no quantitative predictions and introduces no fitted parameters or invented entities. The proposed framework relies on two domain assumptions about ML generalizability and benchmark comparability, which the review itself acknowledges as unresolved.

assumptions (2)
  • domain assumption Machine learning models can predict enzyme function and physicochemical properties from sequence with accuracy sufficient to prioritize candidates for experimental validation.
    Section 4.1's proposed framework depends on this premise, while Section 3.3 documents limited generalization and data bias that threaten it.
  • domain assumption The performance figures of the surveyed tools are comparable across the studies cited.
    Section 3.3 notes the lack of standardized benchmarks, so cross-tool comparisons in the review implicitly assume comparability.

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Cite this review

Pith. "Pith review of Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives." pith.science (2026). https://pith.science/paper/HDVKKXRY

@misc{pith2026250707666,
  author       = {Pith},
  title        = {Pith review of: Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDVKKXRY}},
  note         = {Machine review of arXiv:2507.07666}
}
read the original abstract

Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate their effectiveness in accelerating enzyme discovery. Despite notable progress, current approaches remain limited by data scarcity, model generalizability, and interpretability. We discuss emerging strategies to overcome these challenges, including multi-task learning, integration of multi-modal data, and explainable AI. Together, these developments establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts, with broad applications in biocatalysis, biotechnology, and synthetic biology.

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

  1. [1]

    S.; Martin, M

    (1) Dalkiran, A.; Rifaioglu, A. S.; Martin, M. J.; Cetin-Atalay, R.; Atalay, V.; Doğan, T., ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinf. 2018, 19 (1),

    work page 2018

  2. [7]

    (105) Madani, M.; Lin, K.; Tarakanova, A., DSResSol: A Sequence -Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks. Int. J. Mol. Sci. 2021, 22 (24), 13555 (106) Thumuluri, V.; Martiny, H. M.; Almagro Armenteros, J. J.; Salomon, J.; Nielsen, H.; Johansen, A. R., NetSolP: predicting protein solubility in Escherichia coli u...

    work page 2021

  3. [12]

    C.; Jiang, X.; Wei, D., Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN

    (110) Wang, X.; Liu, Y.; Du, Z.; Zhu, M.; Kaushik, A. C.; Jiang, X.; Wei, D., Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN. Interdiscip. Sci. Comput. Life Sci. 2021, 13 (4), 703-716. (111) Mehmood, F.; Arshad, S.; Shoaib, M., RPPSP: A Robust and Precise Protein Solubility Predictor by Utilizing Novel Protein Sequence Encode...

    work page 2021

  4. [16]

    BMC Biol

    (109) Wang, C.; Zou, Q., Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol. 2023, 21(1),

    work page 2023

  5. [54]

    E.; Knotts, M.; Shaw, A

    (88) Gado, J. E.; Knotts, M.; Shaw, A. Y.; Marks, D.; Gauthier, N. P.; Sander, C.; Beckham, G. T., Machine learning prediction of enzyme optimum pH. BioRxiv 2024, 2023.06.22.544776. (89) Qiu, S.; Hu, B.; Zhao, J.; Xu, W.; Yang, A., Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature. BioRxiv 2024, 2024.08.12.607600. (90) Zaret...

    work page 2024

  6. [118]

    2023 IEEE BIBM, 2023, 11-

    (108) Chen, J.; Qian, Y.; Huang, Z.; Xiao, X.; Deng, L., Enhancing Protein Solubility Prediction through Pre-trained Language Models and Graph Convolutional Neural Networks . 2023 IEEE BIBM, 2023, 11-

    work page 2023

  7. [285]

    S.; Nantasenamat, C.; Shoombuatong, W., A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

    (63) Charoenkwan, P.; Chotpatiwetchkul, W.; Lee, V. S.; Nantasenamat, C.; Shoombuatong, W., A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Sci. Rep. 2021, 11 (1), 23782. (64) Foroozandeh Shahraki, M.; Farhadyar, K.; Kavousi, K.; Azarabad, M. H.; Boroomand, A.; Aria...

    work page 2021

  8. [331]

    piSAAC: Extended notion of SAAC feature selection novel method for discrimination of Enzymes model using different machine learning algorithm

    (80) Khan, Z.; Pi, D.; Khan, I.; Nawaz, A.; Ahmad, J.; Hussain, M., piSAAC: Extended notion of SAAC feature selection novel method for discrimination of Enzymes model using different machine learning algorithm. arXiv preprint arXiv:2101.03126,

    work page Pith review arXiv

Show all 24 references
  1. [334]

    (2) Littmann, M.; Heinzinger, M.; Dallago, C.; Olenyi, T.; Rost, B., Embeddings from deep learning transfer GO annotations beyond homology. Sci. Rep. 2021, 11 (1),

    2021

  2. [1160]

    M.; Hughes, M

    (3) Visani, G. M.; Hughes, M. C.; Hassoun, S., Enzyme Promiscuity Prediction Using Hierarchy - Informed Multi-Label Classification. Bioinformatics 2021, 37 (14), 2017-2024. (4) Gligorijević, V.; Renfrew, P. D.; Kosciolek, T.; Leman, J. K.; Berenberg, D.; Vatanen, T.; Chandler,...

    2021

  3. [1420]

    E.; Beckham, G

    (85) Gado, J. E.; Beckham, G. T.; Payne, C. M., Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning. J. Chem. Inf. Model. 2020, 60 (8), 4098-4107. (86) Shahraki, M. F.; Atanaki, F. F.; Ariaeenejad, S.; Ghaffari, M. R.; Norouzi -Beir...

    2020

  4. [2020]

    (81) Chu, Y.; Yi, Z.; Zeng, R.; Zhang, G., Predicting the optimum temperature of β-agarase based on the relative solvent accessibility of amino acids. J. Mol. Catal. B Enzym. 2016, 129, 47-53. (82) Yan, S.; Wu, G., Predictors for Predicting Temperature Optimum in Beta-Glucosid...

    2016

  5. [2023]

    R.; Park, M.; Kosaraju, S.; Lee, J.; Lee, H.; Lee, J

    (26) Han, S. R.; Park, M.; Kosaraju, S.; Lee, J.; Lee, H.; Lee, J. H.; Oh, T. J.; Kang, M., Evidential deep learning for trustworthy prediction of enzyme commission number. Brief. Bioinform. 2023, 25 (1), bbad401. (27) Tang, W.; Deng, Z.; Zhou, H.; Zhang, W.; Hu, F.; Choi, K. ...

    2023

  6. [2217]

    Methods 2023, 218, 141-148

    (72) Wan, H.; Zhang, Y.; Huang, S., Prediction of thermophilic protein using 2 -D general series correlation pseudo amino acid features. Methods 2023, 218, 141-148. (73) Xiang, X.; Gao, J.; Ding, Y., DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostabilit...

    2023

  7. [2787]

    H.; Sarma, V

    (36) Lu, C.; Lubin, J. H.; Sarma, V. V.; Stentz, S. Z.; Wang, G.; Wang, S.; Khare, S. D., Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network. PNAS 2023, 120 (39), e2303590120. (37) Rappoport, D.; Jinich, A., Enzyme Substrat...

    2023

  8. [2858]

    P.; Costa, R

    (70) Haselbeck, F.; John, M.; Zhang, Y.; Pirnay, J.; Fuenzalida -Werner, J. P.; Costa, R. D.; Grimm, D. G., Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom. Bioinform. 2023, 5(4), lqad087. (71) Zhao, J.; Yan, W.; Yang, Y., DeepTP: ...

    2023

  9. [3168]

    L.; Casadio, R., BENZ WS: the Bologna ENZyme Web Server for four-level EC number annotation

    (5) Baldazzi, D.; Savojardo, C.; Martelli, P. L.; Casadio, R., BENZ WS: the Bologna ENZyme Web Server for four-level EC number annotation. Nucleic Acids Res. 2021, 49 (W1), W60-W66. (6) Khan, K. A.; Memon, S. A.; Naveed, H., A hierarchical deep learning based approach for mult...

    2021

  10. [4139]

    D.; Luo, X., UniKP: a unified framework for the prediction of enzyme kinetic parameters

    (44) Yu, H.; Deng, H.; He, J.; Keasling, J. D.; Luo, X., UniKP: a unified framework for the prediction of enzyme kinetic parameters. Nat. Commun. 2023, 14 (1),

    2023

  11. [4496]

    B.; Purcell, A

    (13) Pan, T.; Li, C.; Bi, Y.; Wang, Z.; Gasser, R. B.; Purcell, A. W.; Akutsu, T.; Webb, G. I.; Imoto, S.; Song, J., PFresGO: an attention mechanism -based deep -learning approach for protein annotation by integrating gene ontology inter-relationships. Bioinformatics 2023, 39 ...

    2023

  12. [5252]

    (41) Kroll, A.; Engqvist, M. K. M.; Heckmann, D.; Lercher, M. J., Deep learning allows genome -scale prediction of Michaelis constants from structural features. PLoS Biol. 2021, 19 (10), e3001402. (42) Li, F.; Yuan, L.; Lu, H.; Li, G.; Chen, Y.; Engqvist, M. K. M.; Kerkhoven, ...

    2021

  13. [7370]

    Research 2023, 6,

    (24) Shi, Z.; Deng, R.; Yuan, Q.; Mao, Z.; Wang, R.; Li, H.; Liao, X.; Ma, H., Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual -core Multitask Learning Framework. Research 2023, 6,

    2023

  14. [7444]

    (56) Li, M.; Wang, H.; Yang, Z.; Zhang, L.; Zhu, Y., DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences. Comput. Struct. Biotechnol. J. 2023, 21, 5544-5560. (57) Wang, L.; Li, C., Optimal subset selection of...

    2023

  15. [7850]

    H.; Jiang, J.; Li, M.; Zou, Q.; Lv, Z

    (69) Pei, H.-L.; Li, J.; Ma, S. H.; Jiang, J.; Li, M.; Zou, Q.; Lv, Z. J. A. S., Identification of Thermophilic Proteins Based on Sequence -Based Bidirectional Representations from Transformer -Embedding Features. Appl. Sci. 2023, 13(5),

    2023

  16. [8211]

    X.; Yu, H.; Zhu, B.; Long, Y.; Wu, M.; Shi, J

    (45) Du, B. X.; Yu, H.; Zhu, B.; Long, Y.; Wu, M.; Shi, J. Y. GELKcat: An Integration Learning of Substrate Graph with Enzyme Embedding for Kcat Prediction. 2023 IEEE BIBM, 2023, 408-411. (46) Wang, T.; Xiang, G.; He, S.; Su, L.; Wang, Y.; Yan, X.; Lu, H., DeepEnzyme: a robust...

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.