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Cited by 121Computational Drug Discovery MethodsMachine Learning in BioinformaticsEnzyme Catalysis and Immobilization

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Machine learning modeling of family wide enzyme-substrate specificity screens

Open access
Goldman, Samuel, Das, Ria, Yang, Kevin K., Coley, Connor W.·2022·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1009853PMC PMC8865696
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Abstract

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

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Fig 1 Enzyme-substrate interaction modeling strategies. (A) Current machine learning-directed evolution strategies, which involve design-build-test-model-learn cycles measuring protein variant activity on a single substrate of interest. (B) The “dense screen” setting where homologous enzyme variants from one protein family are profiled against multiple substrates. In this setting, we can aim to generalize to either new enzymes (“enzyme discovery”) or new substrates (“substrate discovery”). (C) Three different styles of models evaluated in this study, where single task models independently build predictive models for rows and columns from panel (B), whereas a CPI model takes both substrates and enzymes as input. (D) An example CPI model architecture where pretrained neural networks extract features from the substrate and enzyme to be fed into a top-level feed forward model for activity prediction.
Fig 1 Enzyme-substrate interaction modeling strategies. (A) Current machine learning-directed evolution strategies, which involve design-build-test-model-learn cycles measuring protein variant activity on a single substrate of interest. (B) The “dense screen” setting where homologous enzyme variants from one protein family are profiled against multiple substrates. In this setting, we can aim to generalize to either new enzymes (“enzyme discovery”) or new substrates (“substrate discovery”). (C) Three different styles of models evaluated in this study, where single task models independently build predictive models for rows and columns from panel (B), whereas a CPI model takes both substrates and enzymes as input. (D) An example CPI model architecture where pretrained neural networks extract features from the substrate and enzyme to be fed into a top-level feed forward model for activity prediction.CC BY · PubMed Central
Fig 2 Assessing enzyme discovery in family wide screens. (A) CPI models are compared against the single task setting by holding out enzymes for a given substrate and allowing models to train on either the full expanded data (CPI) or only data specific to that substrate (single-task). (B) AUPRC is compared on five different datasets, arranged from left to right in order of increasing number of enzymes in the dataset. Baseline models are compared against multi-task models, CPI models, and single-task models. K-nearest neighbor (KNN) baselines are calculated using Levenshtein edit distances to compare sequences; multi-task models use a shared feed forward network (FFN) to compute predictions against all substrate targets, CPI models utilize FFN with either concatenation (“[{prot repr.}, {sub repr.}]”) or dot product interactions (“{prot repr.}•{sub repr.}”), and ridge regression is used for single-task models. ESM-1b features indicate protein features extracted from a masked language model trained on UniRef50 [20]. Halogenase and glycosyltransferase datasets are evaluated using leave-one-out splits, whereas BKACE, phosphatase, and esterase datasets are evaluated with 5 repeats of 10 different cross validation splits. Standard error bars indicate the standard error of the mean of results computed with 3 random seeds. Each method is compared to the single-task L2-regularized logistic regression model (“Ridge: ESM-1b”) using a 2-sided Welch T test, with each additional asterisk representing significance at [0.05, 0.01, 0.001, 0.0001] thresholds respectively after application of a Benjamini-Hochberg correction. (C) Average AUPRC on each individual “substrate task” is compared between compound protein interaction models and single-task models. Points below 1 indicate substrates on which single-task models better predict enzyme activity than CPI models. CPI models used are FFN: [ESM-1b, Morgan] and single-task models are Ridge: ESM-1b. (D) AUPRC values from the ridge regression model are plotted against the average enzyme similarity in a dataset, with higher enzyme similarity revealing better predictive performance. (E) AUPRC values from the ridge regression model broken out by each task are plotted against the fraction of active enzymes in the dataset. Best fit lines are drawn through each dataset to serve as a visual guide.
Fig 2 Assessing enzyme discovery in family wide screens. (A) CPI models are compared against the single task setting by holding out enzymes for a given substrate and allowing models to train on either the full expanded data (CPI) or only data specific to that substrate (single-task). (B) AUPRC is compared on five different datasets, arranged from left to right in order of increasing number of enzymes in the dataset. Baseline models are compared against multi-task models, CPI models, and single-task models. K-nearest neighbor (KNN) baselines are calculated using Levenshtein edit distances to compare sequences; multi-task models use a shared feed forward network (FFN) to compute predictions against all substrate targets, CPI models utilize FFN with either concatenation (“[{prot repr.}, {sub repr.}]”) or dot product interactions (“{prot repr.}•{sub repr.}”), and ridge regression is used for single-task models. ESM-1b features indicate protein features extracted from a masked language model trained on UniRef50 [20]. Halogenase and glycosyltransferase datasets are evaluated using leave-one-out splits, whereas BKACE, phosphatase, and esterase datasets are evaluated with 5 repeats of 10 different cross validation splits. Standard error bars indicate the standard error of the mean of results computed with 3 random seeds. Each method is compared to the single-task L2-regularized logistic regression model (“Ridge: ESM-1b”) using a 2-sided Welch T test, with each additional asterisk representing significance at [0.05, 0.01, 0.001, 0.0001] thresholds respectively after application of a Benjamini-Hochberg correction. (C) Average AUPRC on each individual “substrate task” is compared between compound protein interaction models and single-task models. Points below 1 indicate substrates on which single-task models better predict enzyme activity than CPI models. CPI models used are FFN: [ESM-1b, Morgan] and single-task models are Ridge: ESM-1b. (D) AUPRC values from the ridge regression model are plotted against the average enzyme similarity in a dataset, with higher enzyme similarity revealing better predictive performance. (E) AUPRC values from the ridge regression model broken out by each task are plotted against the fraction of active enzymes in the dataset. Best fit lines are drawn through each dataset to serve as a visual guide.CC BY · PubMed Central
Fig 3 Assessing substrate discovery in family wide screens. CPI models and single task models are compared on the glycosyltransferase, esterase, and phosphatase datasets, all with 5 trials of 10-fold cross validation. Error bars represent the standard error of the mean across 3 random seeds. Each model and featurization is compared to “Ridge: Morgan” using a 2-sided Welch T test, with each additional asterisk representing significance at [0.05, 0.01, 0.001, 0.0001] thresholds respectively, after applying a Benjamini-Hochberg correction. Pretrained substrate featurizations used in “Ridge: JT-VAE” are features extracted from a junction-tree variational auto-encoder (JT-VAE) [53]. Two compound protein interaction architectures are tested, both concatenation and dot-product, indicated with “[{prot repr.}, {sub repr.}]” and “{prot repr.}•{sub repr.}” respectively. In the interaction based architectures, ESM-1b indicates the use of a masked language model trained on UniRef50 as a protein representation [20]. Models are hyperparameter optimized on a held out halogenase dataset. AUCROC results can be found in Fig D in S1 Text.
Fig 3 Assessing substrate discovery in family wide screens. CPI models and single task models are compared on the glycosyltransferase, esterase, and phosphatase datasets, all with 5 trials of 10-fold cross validation. Error bars represent the standard error of the mean across 3 random seeds. Each model and featurization is compared to “Ridge: Morgan” using a 2-sided Welch T test, with each additional asterisk representing significance at [0.05, 0.01, 0.001, 0.0001] thresholds respectively, after applying a Benjamini-Hochberg correction. Pretrained substrate featurizations used in “Ridge: JT-VAE” are features extracted from a junction-tree variational auto-encoder (JT-VAE) [53]. Two compound protein interaction architectures are tested, both concatenation and dot-product, indicated with “[{prot repr.}, {sub repr.}]” and “{prot repr.}•{sub repr.}” respectively. In the interaction based architectures, ESM-1b indicates the use of a masked language model trained on UniRef50 as a protein representation [20]. Models are hyperparameter optimized on a held out halogenase dataset. AUCROC results can be found in Fig D in S1 Text.CC BY · PubMed Central
Fig 4 Evaluating single-task models on kinase repurposing and discovery tasks. Kinase data from Davis et al. is extracted, featurized, and split as prepared in Hie et al. Multilayer perceptrons (MLP) and Gaussian process + multilayer perceptron (GP+MLP) models are employed. We add variants of these models without CPI training separate single-task models for each enzyme and substrate in the training set, as well as linear models using both pretrained featurizations (“Ridge: JT-VAE”) and fingerprint based featurizations of small molecules (“Ridge: Morgan”). Spearman correlation is shown for (A) held out kinases not in the training set and (B) held out small molecules not in the training set across 5 random initializations. (C) We repeat the retrospective evaluation of lead prioritization. The top 5 average acquired Kd values are shown for the CPI models in Hie et al. compared against a linear, single-task ridge regression model using the same features. (D) The top 25 average acquired Kd values are shown.
Fig 4 Evaluating single-task models on kinase repurposing and discovery tasks. Kinase data from Davis et al. is extracted, featurized, and split as prepared in Hie et al. Multilayer perceptrons (MLP) and Gaussian process + multilayer perceptron (GP+MLP) models are employed. We add variants of these models without CPI training separate single-task models for each enzyme and substrate in the training set, as well as linear models using both pretrained featurizations (“Ridge: JT-VAE”) and fingerprint based featurizations of small molecules (“Ridge: Morgan”). Spearman correlation is shown for (A) held out kinases not in the training set and (B) held out small molecules not in the training set across 5 random initializations. (C) We repeat the retrospective evaluation of lead prioritization. The top 5 average acquired Kd values are shown for the CPI models in Hie et al. compared against a linear, single-task ridge regression model using the same features. (D) The top 25 average acquired Kd values are shown.CC BY · PubMed Central
Fig 5 Structure-based pooling improves enzyme activity predictions. (A) Different pooling strategies can be used to combine amino acid representations from a pretrained protein language model. Yellow coloring in the schematic indicates residues that will be averaged to derive a representation of the protein of interest. (i) We introduce active site pooling, where only embeddings corresponding to residues within a set radius of the protein active site are averaged. By increasing the angstrom radius from the active site, we increase the number of residues pooled. Crystal structures shown are taken from the BKACE reference structure, PDB: 2Y7F rendered with Chimera [60]. (ii, iii) We also introduce two other alignment based pooling strategies: coverage and conservation pooling average only the top-k alignment columns with the fewest gaps and highest number of conserved residues respectively. (iv) Current protein embeddings often take a mean pooling strategy to indiscriminately average over all sequence positions. (B) Enzyme discovery AUPRC values are computed for various different pooling strategies. Each strategy is tested for different thresholds of residues to pool, comparing against both KNN Levenshtein distance baselines and a mean pooling baseline. The same hyperparameters are used as set in Fig 2 for ridge regression models. The kinase repurposing regression task from Hie et al. is shown with Spearman’s ρ instead of AUPRC as interactions are continuous, not binarized. All experiments and are repeated for 3 random seeds.
Fig 5 Structure-based pooling improves enzyme activity predictions. (A) Different pooling strategies can be used to combine amino acid representations from a pretrained protein language model. Yellow coloring in the schematic indicates residues that will be averaged to derive a representation of the protein of interest. (i) We introduce active site pooling, where only embeddings corresponding to residues within a set radius of the protein active site are averaged. By increasing the angstrom radius from the active site, we increase the number of residues pooled. Crystal structures shown are taken from the BKACE reference structure, PDB: 2Y7F rendered with Chimera [60]. (ii, iii) We also introduce two other alignment based pooling strategies: coverage and conservation pooling average only the top-k alignment columns with the fewest gaps and highest number of conserved residues respectively. (iv) Current protein embeddings often take a mean pooling strategy to indiscriminately average over all sequence positions. (B) Enzyme discovery AUPRC values are computed for various different pooling strategies. Each strategy is tested for different thresholds of residues to pool, comparing against both KNN Levenshtein distance baselines and a mean pooling baseline. The same hyperparameters are used as set in Fig 2 for ridge regression models. The kinase repurposing regression task from Hie et al. is shown with Spearman’s ρ instead of AUPRC as interactions are continuous, not binarized. All experiments and are repeated for 3 random seeds.CC BY · PubMed Central

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Citations

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Research works printed but not linked: 8

  1. P Carbonell, 2018, Selenzyme: Enzyme selection tool for pathway design, Bioinformatics, 34, 2153, 10.1093/bioinformatics/bty065 · DOI
  2. BF Fisher, 2019, Site-Selective C–H Halogenation Using Flavin-Dependent Halogenases Identified via Family-Wide Activity Profiling, ACS Central Science, 5, 1844, 10.1021/acscentsci.9b00835 · DOI
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  7. J Jumper, 2020, High accuracy protein structure prediction using deep learning, Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book), 22, 24
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Metadata

Authors
Goldman, S., Das, R., Yang, K. K., & Coley, C. W.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Microsoft Research New England (United States) (US)
Corresponding author
Coley, Connor W.
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2022-02-10
Volume
18
Issue
2
Pages
e1009853
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1009853
PMC
PMC8865696
OpenAlex
W3198646379
Cited by
120
References
62
Citations by year
2026: 27 · 2025: 25 · 2024: 29 · 2023: 29 · 2022: 10
FWCI
12.72
Type
journal article
Language
en
Update notices
new_version (2022-02-23)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • arXiv (Cornell University)
Funders
Machine Learning for Pharmaceutical Discovery and Synthesis Consortium
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
Keywords
enzyme substrate specificity, biocatalysis, compound-protein interaction prediction, kinase inhibitors, protein sequence representation, enzyme discovery, drug-target interactions, machine learning prediction models
MeSH terms
Machine Learning, Amino Acid Sequence, Proteins, Substrate Specificity, Drug Discovery

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