Machine learning‐based prediction of enzyme substrate scope: Application to bacterial nitrilases Article Swipe
Zhongyu Mou
,
Jason Eakes
,
Connor J. Cooper
,
Carmen M. Foster
,
Robert F. Standaert
,
Mircea Podar
,
Mitchel J. Doktycz
,
Jerry M. Parks
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/prot.26019
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/prot.26019
Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence‐ and structure‐based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient‐boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. This approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/prot.26019
- OA Status
- green
- Cited By
- 63
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3096046766
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3096046766Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/prot.26019Digital Object Identifier
- Title
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Machine learning‐based prediction of enzyme substrate scope: Application to bacterial nitrilasesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-10-29Full publication date if available
- Authors
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Zhongyu Mou, Jason Eakes, Connor J. Cooper, Carmen M. Foster, Robert F. Standaert, Mircea Podar, Mitchel J. Doktycz, Jerry M. ParksList of authors in order
- Landing page
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https://doi.org/10.1002/prot.26019Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1765501Direct OA link when available
- Concepts
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Scope (computer science), Substrate (aquarium), Enzyme, Computer science, Substrate specificity, Chemistry, Biochemical engineering, Computational biology, Biochemistry, Biology, Engineering, Ecology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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63Total citation count in OpenAlex
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2025: 20, 2024: 17, 2023: 14, 2022: 7, 2021: 5Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.complementary | 80 |
| abstract_inverted_index.corresponding | 118 |
| abstract_inverted_index.physicochemical | 68, 175 |
| abstract_inverted_index.structure‐based | 20 |
| abstract_inverted_index.gradient‐boosted | 134 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5037417242 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I1289243028, https://openalex.org/I75027704 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.89226933 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |