Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2
Abstract
:1. Introduction
2. Results and Discussion
2.1. Replicase Polyprotein 1ab
2.2. NSP1 Flexible Linker Region
2.3. NSP1 Cu(II) Binding Region
2.4. RNA Binding Region in Polymerase
2.5. NSP6 Lipid Binding Region
2.6. Polyprotein 1a
2.7. Spike Glycoprotein
2.8. ORF3a Protein
2.9. Nucleocapsid (N) Protein
2.10. Statistical Analysis
2.11. Comparative Performance of Disorder Prediction Models
2.12. Targeting IDRs in Drug Design
3. Materials and Methods
3.1. SARS-CoV-2 Protein Selection
3.2. Disorder Prediction Tools
3.3. Disorder Scoring and Data Analysis
3.4. Statistical Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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UniProt | SARS-CoV-2 Proteins | Length | Disorder Content |
---|---|---|---|
P0DTC1 | Replicase polyprotein 1a | 4405 | 0.27% |
P0DTC2 | Spike glycoprotein (S) | 1273 | 27.10% |
P0DTC3 | ORF3a protein | 275 | 26.91% |
P0DTC9 | Nucleocapsid (N) protein | 419 | 51.07% |
P0DTD1 | Replicase polyprotein 1ab | 7096 | 4.61% |
Model | URL | Dataset | Algorithm | Properties | AUC | Evaluation Metrics | References |
---|---|---|---|---|---|---|---|
PONDR®VLXT | http://www.pondr.com/ (accessed on 25 September 2024) | Data consist of 8 and 7 long disordered regions from X-ray crystallography and NMR, respectively. | Three feedforward neural networks: VL1, XN, and XC | Less sensitive | 0.757 | ACC = 69.0 ± 0.9 | [44,45] |
PONDR®VSL2 | http://www.pondr.com/ (accessed on 25 September 2024) | Sequences of varying lengths of X-ray crystallographic data (short and long length IDRs) | Combination of neural networks and SVM | 81% accuracy reported | 0.905 | ACC = 82.3 ± 1.1 | [46] |
ADOPT | https://github.com/PeptoneLtd/ADOPT (accessed on 15 October 2024) | Datasets (CheZoD 1325 and CheZoD 117) | Deep bidirectional transformer (ESM library) and supervised predictor | Fast, accurate, and highly sensitive | 0.964 | MCC = 0.799 | [47] |
flDPnn | http://biomine.cs.vcu.edu/servers/flDPnn/ (accessed on 7 November 2024) | Annotated 745 proteins from the DisProt 7.0 database | Deep learning model | Fast | 0.814 | MCC = 0.370 | [48] |
Disorder Level | Z-Score Range | Description |
---|---|---|
Fully Disordered | Z-score < 3 | High degree of disorder, lacking a stable 3D structure |
Partially Disordered | 3 ≤ 8 | Moderate disorder, some degree of structure but still flexible |
Flexible | 8 ≤ 11 | Some degree of secondary structure but still dynamic |
Structured | Z-score ≥ 11 | Well-defined 3D structure, such as alpha-helices or beta-sheets |
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Ilyas, S.; Manan, A.; Lee, D. Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2. Int. J. Mol. Sci. 2025, 26, 3411. https://doi.org/10.3390/ijms26073411
Ilyas S, Manan A, Lee D. Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2. International Journal of Molecular Sciences. 2025; 26(7):3411. https://doi.org/10.3390/ijms26073411
Chicago/Turabian StyleIlyas, Sidra, Abdul Manan, and Donghun Lee. 2025. "Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2" International Journal of Molecular Sciences 26, no. 7: 3411. https://doi.org/10.3390/ijms26073411
APA StyleIlyas, S., Manan, A., & Lee, D. (2025). Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2. International Journal of Molecular Sciences, 26(7), 3411. https://doi.org/10.3390/ijms26073411