A Framework for Accurate Prediction of Plastic-Degrading Enzymes using Convolutional Neural Networks Article Swipe
Soharth Hasnat
,
Fariah Anjum Shifa
,
Shabab Murshed
,
Tanveer Ahmed Rumee
,
M Murshida Mahbub
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.20.619257
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.20.619257
The growing accumulation of plastic waste presents a significant environmental challenge, necessitating innovative approaches to mitigate its impact. Enzymatic degradation has emerged as a promising solution for addressing plastic pollution. However, the isolation and characterization of plastic-degrading enzymes (PDEs) through laboratory experiments are costly, time-consuming, and often complicated by nonculturable microorganisms. Consequently, accurate in silico identification of PDEs is desirable to explore the diversity of natural enzymes and harness their potential for combating plastic pollution. This study introduces a novel feature extraction strategy for identifying plastic-degrading enzymes, incorporating Autocorrelation (AAutoCor), Composition of k-spaced Amino Acid Pairs (KSAP), Dipeptide Deviation from Expected Mean (DDE), Composition/Transition/Distribution (C/T/D), Conjoint Triad, and Secondary Structure. A combination of ANOVA and XGBoost, feature selection methods, was applied to optimize the feature dimensions for improved performance. Seven supervised machine learning models were employed to evaluate the dataset: Convolutional Neural Network, Random Forest Classifier, Feedforward Neural Network, Logistic Regression, Naive Bayes Classifier, K-nearest Neighbor, and XGBoost Classifier. Among these models, the CNN model demonstrated the best performance, achieving an accuracy of 0.96, an F1 score of 0.80, and an ROC-AUC score of 0.96. These findings underscore the potential of the proposed system as an accurate predictor of plastic-degrading enzymes from environmental sequences. This approach significantly enhances efforts to develop sustainable solutions to plastic waste by accelerating the discovery of novel PDEs.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.10.20.619257
- https://www.biorxiv.org/content/biorxiv/early/2024/10/23/2024.10.20.619257.full.pdf
- OA Status
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- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403684281
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Raw OpenAlex JSON
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https://openalex.org/W4403684281Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.10.20.619257Digital Object Identifier
- Title
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A Framework for Accurate Prediction of Plastic-Degrading Enzymes using Convolutional Neural NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-23Full publication date if available
- Authors
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Soharth Hasnat, Fariah Anjum Shifa, Shabab Murshed, Tanveer Ahmed Rumee, M Murshida MahbubList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.10.20.619257Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/10/23/2024.10.20.619257.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/10/23/2024.10.20.619257.full.pdfDirect OA link when available
- Concepts
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Convolutional neural network, Computer science, Artificial neural network, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.strategy | 82 |
| abstract_inverted_index.Deviation | 98 |
| abstract_inverted_index.Dipeptide | 97 |
| abstract_inverted_index.Enzymatic | 18 |
| abstract_inverted_index.K-nearest | 154 |
| abstract_inverted_index.Neighbor, | 155 |
| abstract_inverted_index.Secondary | 108 |
| abstract_inverted_index.achieving | 169 |
| abstract_inverted_index.combating | 72 |
| abstract_inverted_index.desirable | 59 |
| abstract_inverted_index.discovery | 219 |
| abstract_inverted_index.diversity | 63 |
| abstract_inverted_index.isolation | 32 |
| abstract_inverted_index.potential | 70, 189 |
| abstract_inverted_index.predictor | 197 |
| abstract_inverted_index.promising | 24 |
| abstract_inverted_index.selection | 117 |
| abstract_inverted_index.solutions | 212 |
| abstract_inverted_index.Structure. | 109 |
| abstract_inverted_index.addressing | 27 |
| abstract_inverted_index.approaches | 13 |
| abstract_inverted_index.challenge, | 10 |
| abstract_inverted_index.dimensions | 125 |
| abstract_inverted_index.extraction | 81 |
| abstract_inverted_index.innovative | 12 |
| abstract_inverted_index.introduces | 77 |
| abstract_inverted_index.laboratory | 40 |
| abstract_inverted_index.pollution. | 29, 74 |
| abstract_inverted_index.sequences. | 203 |
| abstract_inverted_index.supervised | 130 |
| abstract_inverted_index.underscore | 187 |
| abstract_inverted_index.(AAutoCor), | 89 |
| abstract_inverted_index.Classifier, | 145, 153 |
| abstract_inverted_index.Classifier. | 158 |
| abstract_inverted_index.Composition | 90 |
| abstract_inverted_index.Feedforward | 146 |
| abstract_inverted_index.Regression, | 150 |
| abstract_inverted_index.combination | 111 |
| abstract_inverted_index.complicated | 47 |
| abstract_inverted_index.degradation | 19 |
| abstract_inverted_index.experiments | 41 |
| abstract_inverted_index.identifying | 84 |
| abstract_inverted_index.significant | 8 |
| abstract_inverted_index.sustainable | 211 |
| abstract_inverted_index.accelerating | 217 |
| abstract_inverted_index.accumulation | 2 |
| abstract_inverted_index.demonstrated | 165 |
| abstract_inverted_index.performance, | 168 |
| abstract_inverted_index.performance. | 128 |
| abstract_inverted_index.Consequently, | 51 |
| abstract_inverted_index.Convolutional | 140 |
| abstract_inverted_index.environmental | 9, 202 |
| abstract_inverted_index.incorporating | 87 |
| abstract_inverted_index.necessitating | 11 |
| abstract_inverted_index.nonculturable | 49 |
| abstract_inverted_index.significantly | 206 |
| abstract_inverted_index.identification | 55 |
| abstract_inverted_index.Autocorrelation | 88 |
| abstract_inverted_index.microorganisms. | 50 |
| abstract_inverted_index.time-consuming, | 44 |
| abstract_inverted_index.characterization | 34 |
| abstract_inverted_index.plastic-degrading | 36, 85, 199 |
| abstract_inverted_index.Composition/Transition/Distribution | 103 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5114380522 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210135468, https://openalex.org/I885507782 |
| citation_normalized_percentile.value | 0.70316091 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |