HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines
The cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/6/5960 |
| _version_ | 1797611147613962240 |
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| author | Mingjie Gao Stefan Günther |
| author_facet | Mingjie Gao Stefan Günther |
| author_sort | Mingjie Gao |
| collection | DOAJ |
| description | The cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines are equally reactive or accessible. Hence, to identify targetable cysteines, we propose a novel ensemble stacked machine learning (ML) model to predict hyper-reactive druggable cysteines, named HyperCys. First, the pocket, conservation, structural and energy profiles, and physicochemical properties of (non)covalently bound cysteines were collected from both protein sequences and 3D structures of protein–ligand complexes. Then, we established the HyperCys ensemble stacked model by integrating six different ML models, including K-nearest neighbors, support vector machine, light gradient boost machine, multi-layer perceptron classifier, random forest, and the meta-classifier model logistic regression. Finally, based on the hyper-reactive cysteines’ classification accuracy and other metrics, the results for different feature group combinations were compared. The results show that the accuracy, F1 score, recall score, and ROC AUC values of HyperCys are 0.784, 0.754, 0.742, and 0.824, respectively, after performing 10-fold CV with the best window size. Compared to traditional ML models with only sequenced-based features or only 3D structural features, HyperCys is more accurate at predicting hyper-reactive druggable cysteines. It is anticipated that HyperCys will be an effective tool for discovering new potential reactive cysteines in a wide range of nucleophilic proteins and will provide an important contribution to the design of targeted covalent inhibitors with high potency and selectivity. |
| first_indexed | 2024-03-11T06:23:41Z |
| format | Article |
| id | doaj.art-09d799a650cd47b1a97c9905d66c4cc7 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T06:23:41Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-09d799a650cd47b1a97c9905d66c4cc72023-11-17T11:41:35ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-03-01246596010.3390/ijms24065960HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable CysteinesMingjie Gao0Stefan Günther1Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, 79104 Freiburg, GermanyInstitute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, 79104 Freiburg, GermanyThe cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines are equally reactive or accessible. Hence, to identify targetable cysteines, we propose a novel ensemble stacked machine learning (ML) model to predict hyper-reactive druggable cysteines, named HyperCys. First, the pocket, conservation, structural and energy profiles, and physicochemical properties of (non)covalently bound cysteines were collected from both protein sequences and 3D structures of protein–ligand complexes. Then, we established the HyperCys ensemble stacked model by integrating six different ML models, including K-nearest neighbors, support vector machine, light gradient boost machine, multi-layer perceptron classifier, random forest, and the meta-classifier model logistic regression. Finally, based on the hyper-reactive cysteines’ classification accuracy and other metrics, the results for different feature group combinations were compared. The results show that the accuracy, F1 score, recall score, and ROC AUC values of HyperCys are 0.784, 0.754, 0.742, and 0.824, respectively, after performing 10-fold CV with the best window size. Compared to traditional ML models with only sequenced-based features or only 3D structural features, HyperCys is more accurate at predicting hyper-reactive druggable cysteines. It is anticipated that HyperCys will be an effective tool for discovering new potential reactive cysteines in a wide range of nucleophilic proteins and will provide an important contribution to the design of targeted covalent inhibitors with high potency and selectivity.https://www.mdpi.com/1422-0067/24/6/5960machine learningstructure and sequence baseddruggable cysteinereactivity prediction |
| spellingShingle | Mingjie Gao Stefan Günther HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines International Journal of Molecular Sciences machine learning structure and sequence based druggable cysteine reactivity prediction |
| title | HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines |
| title_full | HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines |
| title_fullStr | HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines |
| title_full_unstemmed | HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines |
| title_short | HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines |
| title_sort | hypercys a structure and sequence based predictor of hyper reactive druggable cysteines |
| topic | machine learning structure and sequence based druggable cysteine reactivity prediction |
| url | https://www.mdpi.com/1422-0067/24/6/5960 |
| work_keys_str_mv | AT mingjiegao hypercysastructureandsequencebasedpredictorofhyperreactivedruggablecysteines AT stefangunther hypercysastructureandsequencebasedpredictorofhyperreactivedruggablecysteines |