Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 10 2024
16 10 2024
Historique:
received:
24
04
2024
accepted:
30
09
2024
medline:
17
10
2024
pubmed:
17
10
2024
entrez:
16
10
2024
Statut:
epublish
Résumé
Deoxyribonucleic acid (DNA) is able to form non-canonical four-stranded helical structures with diverse folding patterns known as G-quadruplexes (G4s). G4 topologies are classified based on their relative strand orientation following the 5' to 3' phosphate backbone polarity. Broadly, G4 topologies are either parallel (4+0), antiparallel (2+2), or hybrid (3+1). G4s play crucial roles in biological processes such as DNA repair, DNA replication, transcription and have thus emerged as biological targets in drug design. While computational models have been developed to predict G4 formation, there is currently no existing model capable of predicting G4 folding topology based on its nucleic acid sequence. Therefore, we introduce G4ShapePredictor (G4SP), an application featuring a collection of multi-classification machine learning models that are trained on a custom G4 dataset combining entries from existing literature and in-house circular dichroism experiments. G4ShapePredictor is designed to accurately predict G4 folding topologies in potassium (
Identifiants
pubmed: 39414858
doi: 10.1038/s41598-024-74826-2
pii: 10.1038/s41598-024-74826-2
doi:
Substances chimiques
DNA
9007-49-2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24238Subventions
Organisme : Singapore Ministry of Education Academic Research Fund Tier 1
ID : RG140/22
Organisme : Singapore Ministry of Education Academic Research Fund Tier 1
ID : RG140/22
Organisme : Singapore Ministry of Education Academic Research Fund Tier 1
ID : RG140/22
Organisme : Singapore Ministry of Education Academic Research Fund Tier 2
ID : MOE-T2EP50223-0014
Informations de copyright
© 2024. The Author(s).
Références
Monsen, R. C., Trent, J. O. & Chaires, J. B. G-quadruplex dna: a longer story. Accounts of Chemical Research 55, 3242–3252. https://doi.org/10.1021/acs.accounts.2c00519 (2022).
doi: 10.1021/acs.accounts.2c00519
pubmed: 36282946
Chen, Y. & Yang, D. Sequence, stability, and structure of g-quadruplexes and their interactions with drugs. Current protocols in nucleic acid chemistry 50, 17–5 (2012).
doi: 10.1002/0471142700.nc1705s50
Phan, A. T. Human telomeric G-quadruplex: structures of DNA and RNA sequences. The FEBS Journal 277, 1107–1117. https://doi.org/10.1111/j.1742-4658.2009.07464.x (2010).
doi: 10.1111/j.1742-4658.2009.07464.x
pubmed: 19951353
Kerwin, S. M. G-quadruplex DNA as a target for drug design. Current Pharmaceutical Design 6, 441–471 (2000).
doi: 10.2174/1381612003400849
pubmed: 10788591
Lim, K. W. et al. Structure of the human telomere in k+ solution: a stable basket-type g-quadruplex with only two g-tetrad layers. Journal of the American Chemical Society 131, 4301–4309. https://doi.org/10.1021/ja807503g (2009).
doi: 10.1021/ja807503g
pubmed: 19271707
pmcid: 2662591
Luu, K. N., Phan, A. T., Kuryavyi, V., Lacroix, L. & Patel, D. J. Structure of the human telomere in k+ solution: an intramolecular (3 + 1) g-quadruplex scaffold. Journal of the American Chemical Society 128, 9963–9970. https://doi.org/10.1021/ja062791w (2006).
doi: 10.1021/ja062791w
pubmed: 16866556
pmcid: 4692383
Dai, J., Carver, M., Punchihewa, C., Jones, R. A. & Yang, D. Structure of the hybrid-2 type intramolecular human telomeric G-quadruplex in K+ solution: insights into structure polymorphism of the human telomeric sequence. Nucleic Acids Research 35, 4927–4940. https://doi.org/10.1093/nar/gkm522 (2007).
doi: 10.1093/nar/gkm522
pubmed: 17626043
pmcid: 1976458
Lim, K.W., Ng, V. C.M., MartÃn-Pintado, N., Heddi, B. & Phan, A.T. Structure of the human telomere in Na+ solution: an antiparallel (2+2) G-quadruplex scaffold reveals additional diversity. Nucleic Acids Research 41, 10556–10562, https://doi.org/10.1093/nar/gkt771 (2013).
Tucker, B. A. et al. Stability of the na+ form of the human telomeric G-quadruplex: role of adenines in stabilizing G-quadruplex structure. ACS Omega 3, 844–855. https://doi.org/10.1021/acsomega.7b01649 (2018).
doi: 10.1021/acsomega.7b01649
pubmed: 30023791
pmcid: 6045420
Shim, J. W., Tan, Q. & Gu, L.-Q. Single-molecule detection of folding and unfolding of the G-quadruplex aptamer in a nanopore nanocavity. Nucleic Acids Research 37, 972–982. https://doi.org/10.1093/nar/gkn968 (2009).
doi: 10.1093/nar/gkn968
pubmed: 19112078
Bhattacharyya, D., Mirihana Arachchilage, G. & Basu, S. Metal cations in g-quadruplex folding and stability. Frontiers in Chemistry 4 (2016).
Makarov, D.E. & Plaxco, K.W. Measuring distances within unfolded biopolymers using fluorescence resonance energy transfer: The effect of polymer chain dynamics on the observed fluorescence resonance energy transfer efficiency. The Journal of chemical physics 131 (2009).
Kong, D.-M., Yang, W., Wu, J., Li, C.-X. & Shen, H.-X. Structure-function study of peroxidase-like g-quadruplex-hemin complexes. Analyst 135, 321–326 (2010).
doi: 10.1039/B920293E
pubmed: 20098765
Sato, K. & Knipscheer, P. G-quadruplex resolution: from molecular mechanisms to physiological relevance. DNA Repair 130, 103552. https://doi.org/10.1016/j.dnarep.2023.103552 (2023).
doi: 10.1016/j.dnarep.2023.103552
pubmed: 37572578
Biver, T. Discriminating between parallel, anti-parallel and hybrid g-quadruplexes: mechanistic details on their binding to small molecules. Molecules 27, 4165. https://doi.org/10.3390/molecules27134165 (2022).
doi: 10.3390/molecules27134165
pubmed: 35807410
Zhang, R. et al. G-quadruplex structures are key modulators of somatic structural variants in cancers. Cancer Research 83, 1234–1248. https://doi.org/10.1158/0008-5472.CAN-22-3089 (2023).
doi: 10.1158/0008-5472.CAN-22-3089
pubmed: 36791413
pmcid: 10102852
Tian, T., Chen, Y.-Q., Wang, S.-R. & Zhou, X. G-quadruplex: a regulator of gene expression and its chemical targeting. Chem 4, 1314–1344. https://doi.org/10.1016/j.chempr.2018.02.014 (2018).
doi: 10.1016/j.chempr.2018.02.014
Besnard, E. et al. Unraveling cell type-specific and reprogrammable human replication origin signatures associated with G-quadruplex consensus motifs. Nature Structural & Molecular Biology 19, 837–844. https://doi.org/10.1038/nsmb.2339 (2012).
doi: 10.1038/nsmb.2339
Valton, A.-L. et al. G4 motifs affect origin positioning and efficiency in two vertebrate replicators. The EMBO Journal 33, 732–746. https://doi.org/10.1002/embj.201387506 (2014).
doi: 10.1002/embj.201387506
pubmed: 24521668
pmcid: 4000090
Lange, T.d. Shelterin: the protein complex that shapes and safeguards human telomeres. Genes & Development 19, 2100–2110, https://doi.org/10.1101/gad.1346005 (2005).
Siddiqui, G. A. et al. Application of machine learning algorithms to metadynamics for the elucidation of the binding modes and free energy landscape of drug/target interactions: a case study. Chemistry - A European Journal 29, e202302375. https://doi.org/10.1002/chem.202302375 (2023).
doi: 10.1002/chem.202302375
pubmed: 37555841
Han, H. & Hurley, L. H. G-quadruplex DNA: a potential target for anti-cancer drug design. Trends in Pharmacological Sciences 21, 136–142. https://doi.org/10.1016/S0165-6147(00)01457-7 (2000).
doi: 10.1016/S0165-6147(00)01457-7
pubmed: 10740289
Mergny, J.-L. & Hélène, C. G-quadruplex DNA: a target for drug design. Nature Medicine 4, 1366–1367. https://doi.org/10.1038/3949 (1998).
doi: 10.1038/3949
pubmed: 9846570
Teng, F.-Y. et al. G-quadruplex DNA: a novel target for drug design. Cellular and Molecular Life Sciences 78, 6557–6583. https://doi.org/10.1007/s00018-021-03921-8 (2021).
doi: 10.1007/s00018-021-03921-8
pubmed: 34459951
pmcid: 11072987
Su, Z. et al. A G-quadruplex/hemin structure-undamaged method to inhibit peroxidase-mimic DNAzyme activity for biosensing development. Analytica Chimica Acta 1221, 340143. https://doi.org/10.1016/j.aca.2022.340143 (2022).
doi: 10.1016/j.aca.2022.340143
pubmed: 35934375
Bhat-Ambure, J. et al. G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA. Cancers 15, 3817. https://doi.org/10.3390/cancers15153817 (2023).
doi: 10.3390/cancers15153817
pubmed: 37568632
pmcid: 10416877
Arola, A. & Vilar, R. Stabilisation of G-Quadruplex DNA by Small Molecules. Current Topics in Medicinal Chemistry 8, 1405–1415. https://doi.org/10.2174/156802608786141106 (2008).
doi: 10.2174/156802608786141106
pubmed: 18991726
Wang, R., Hao, W., Pan, L., Boldogh, I. & Ba, X. The roles of base excision repair enzyme OGG1 in gene expression. Cellular and Molecular Life Sciences 75, 3741–3750. https://doi.org/10.1007/s00018-018-2887-8 (2018).
doi: 10.1007/s00018-018-2887-8
pubmed: 30043138
pmcid: 6154017
Cave, J. W. & Willis, D. E. G-quadruplex regulation of neural gene expression. The FEBS Journal 289, 3284–3303. https://doi.org/10.1111/febs.15900 (2022).
doi: 10.1111/febs.15900
pubmed: 33905176
O’Hagan, M. P., Morales, J. C. & Galan, M. C. Binding and beyond: what else can G-quadruplex ligands do?. European Journal of Organic Chemistry 2019, 4995–5017 (2019).
doi: 10.1002/ejoc.201900692
Shiekh, S., Kodikara, S.G. & Balci, H. Structure, topology, and stability of multiple g-quadruplexes in long telomeric overhangs. Journal of Molecular Biology 168205, https://doi.org/10.1016/j.jmb.2023.168205 (2023).
Takahashi, S., Brazier, J. A. & Sugimoto, N. Topological impact of noncanonical DNA structures on Klenow fragment of DNA polymerase. Proceedings of the National Academy of Sciences 114, 9605–9610 (2017).
doi: 10.1073/pnas.1704258114
Ai, T. et al. Insight into how telomeric G-quadruplexes enhance the peroxidase activity of cellular Hemin. Chemistry - An Asian Journal 13, 1805–1810. https://doi.org/10.1002/asia.201800464 (2018).
doi: 10.1002/asia.201800464
Yu, H., Qi, Y., Yang, B., Yang, X. & Ding, Y. G4Atlas: a comprehensive transcriptome-wide G-quadruplex database. Nucleic Acids Research 51, D126–D134. https://doi.org/10.1093/nar/gkac896 (2023).
doi: 10.1093/nar/gkac896
pubmed: 36243987
Elimelech-Zohar, K. & Orenstein, Y. An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks. Briefings in Bioinformatics 24, bbad252, https://doi.org/10.1093/bib/bbad252 (2023).
Huppert, J. L. & Balasubramanian, S. Prevalence of quadruplexes in the human genome. Nucleic Acids Research 33, 2908–2916. https://doi.org/10.1093/nar/gki609 (2005).
doi: 10.1093/nar/gki609
pubmed: 15914667
pmcid: 1140081
Todd, A. K., Johnston, M. & Neidle, S. Highly prevalent putative quadruplex sequence motifs in human DNA. Nucleic Acids Research 33, 2901–2907. https://doi.org/10.1093/nar/gki553 (2005).
doi: 10.1093/nar/gki553
pubmed: 15914666
pmcid: 1140077
Kudlicki, A. S. G-Quadruplexes involving both strands of genomic DNA are highly abundant and colocalize with functional sites in the human genome. PLOS ONE 11, e0146174. https://doi.org/10.1371/journal.pone.0146174 (2016).
doi: 10.1371/journal.pone.0146174
pubmed: 26727593
pmcid: 4699641
Hon, J., Martínek, T., Zendulka, J. & Lexa, M. pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R. Bioinformatics 33, 3373–3379. https://doi.org/10.1093/bioinformatics/btx413 (2017).
doi: 10.1093/bioinformatics/btx413
pubmed: 29077807
Bedrat, A., Lacroix, L. & Mergny, J.-L. Re-evaluation of G-quadruplex propensity with G4Hunter. Nucleic Acids Research 44, 1746–1759. https://doi.org/10.1093/nar/gkw006 (2016).
doi: 10.1093/nar/gkw006
pubmed: 26792894
pmcid: 4770238
Sahakyan, A. B. et al. Machine learning model for sequence-driven dna g-quadruplex formation. Scientific Reports 7, 14535. https://doi.org/10.1038/s41598-017-14017-4 (2017).
doi: 10.1038/s41598-017-14017-4
pubmed: 29109402
pmcid: 5673958
Cagirici, H. B., Budak, H. & Sen, T. Z. G4Boost: a machine learning-based tool for quadruplex identification and stability prediction. BMC Bioinformatics 23, 240. https://doi.org/10.1186/s12859-022-04782-z (2022).
doi: 10.1186/s12859-022-04782-z
pubmed: 35717172
pmcid: 9206279
Zhang, Z., Zhang, R., Xiao, K. & Sun, X. G4beacon: an in vivo g4 prediction method using chromatin and sequence information. Biomolecules 13, 292. https://doi.org/10.3390/biom13020292 (2023).
doi: 10.3390/biom13020292
pubmed: 36830661
pmcid: 9953394
Cui, Y. et al. Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution cut &tag data. Briefings in Functional Genomics elad024, https://doi.org/10.1093/bfgp/elad024 (2023).
Korsakova, A. & Phan, A. T. Prediction of G4 formation in live cells with epigenetic data: a deep learning approach. NAR genomics and bioinformatics 5, lqad071, https://doi.org/10.1093/nargab/lqad071 (2023).
Rocher, V., Genais, M., Nassereddine, E. & Mourad, R. Deepg4: a deep learning approach to predict cell-type specific active g-quadruplex regions. PLOS Computational Biology 17, e1009308. https://doi.org/10.1371/journal.pcbi.1009308 (2021).
doi: 10.1371/journal.pcbi.1009308
pubmed: 34383754
pmcid: 8384162
Garant, J.-M., Perreault, J.-P. & Scott, M. S. Motif independent identification of potential RNA G-quadruplexes by G4RNA screener. Bioinformatics 33, 3532–3537. https://doi.org/10.1093/bioinformatics/btx498 (2017).
doi: 10.1093/bioinformatics/btx498
pubmed: 29036425
pmcid: 5870565
Barshai, M., Engel, B., Haim, I. & Orenstein, Y. G4mismatch: Deep neural networks to predict g-quadruplex propensity based on g4-seq data. PLOS Computational Biology 19, e1010948. https://doi.org/10.1371/journal.pcbi.1010948 (2023).
doi: 10.1371/journal.pcbi.1010948
pubmed: 36897885
pmcid: 10079223
Gaudreault, J.-G., Branco, P. & Gama, J. An analysis of performance metrics for imbalanced classification. In Discovery Science, 67–77, https://doi.org/10.1007/978-3-030-88942-5_6 (Springer, Cham, 2021).
Jeni, L.A., Cohn, J.F. & De La Torre, F. Facing imbalanced data - recommendations for the use of performance metrics. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 245–251, https://doi.org/10.1109/ACII.2013.47 (2013).
Calvert, C.L. & Khoshgoftaar, T.M. Threshold based optimization of performance metrics with severely imbalanced big security data. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 1328–1334, https://doi.org/10.1109/ICTAI.2019.00184 (2019).
Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. Journal of Big Data 6, 27. https://doi.org/10.1186/s40537-019-0192-5 (2019).
doi: 10.1186/s40537-019-0192-5
Maratea, A., Petrosino, A. & Manzo, M. Adjusted f-measure and kernel scaling for imbalanced data learning. Information Sciences 257, 331–341. https://doi.org/10.1016/j.ins.2013.04.016 (2014).
doi: 10.1016/j.ins.2013.04.016
Fang, T., Lu, N., Niu, G. & Sugiyama, M. Rethinking importance weighting for deep learning under distribution shift. In Advances in Neural Information Processing Systems, vol. 33, 11996–12007 (Curran Associates, Inc., 2020).
Kumar, S., Biswas, S. K. & Devi, D. Tlusboost algorithm: a boosting solution for class imbalance problem. Soft Computing 23, 10755–10767. https://doi.org/10.1007/s00500-018-3629-4 (2019).
doi: 10.1007/s00500-018-3629-4
Bishara, I., Chen, J., Griffiths, J.I., Bild, A.H. & Nath, A. A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types. Frontiers in Genetics 13 (2022).
Sallam, N. M., Saleh, A. I., Arafat Ali, H. & Abdelsalam, M. M. An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques. Applied Sciences 12, 10760. https://doi.org/10.3390/app122110760 (2022).
doi: 10.3390/app122110760
Zou, Q., Xie, S., Lin, Z., Wu, M. & Ju, Y. Finding the Best Classification Threshold in Imbalanced Classification. Big Data Research 5, 2–8. https://doi.org/10.1016/j.bdr.2015.12.001 (2016).
doi: 10.1016/j.bdr.2015.12.001
Voigt, T., Fried, R., Backes, M. & Rhode, W. Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy. Advances in Data Analysis and Classification 8, 195–216. https://doi.org/10.1007/s11634-014-0167-5 (2014).
doi: 10.1007/s11634-014-0167-5
Janitza, S., Strobl, C. & Boulesteix, A.-L. An AUC-based permutation variable importance measure for random forests. BMC Bioinformatics 14, 119. https://doi.org/10.1186/1471-2105-14-119 (2013).
doi: 10.1186/1471-2105-14-119
pubmed: 23560875
pmcid: 3626572
Gregorutti, B., Michel, B. & Saint-Pierre, P. Correlation and variable importance in random forests. Statistics and Computing 27, 659–678. https://doi.org/10.1007/s11222-016-9646-1 (2017).
doi: 10.1007/s11222-016-9646-1
Stegle, O., Payet, L., Mergny, J.-L., MacKay, D. J. C. & Huppert, J. L. Predicting and understanding the stability of G-quadruplexes. Bioinformatics 25, i374–i1382. https://doi.org/10.1093/bioinformatics/btp210 (2009).
doi: 10.1093/bioinformatics/btp210
pubmed: 19478012
pmcid: 2687964
Sage, A. T. et al. A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization. Nature Communications 14, 4810. https://doi.org/10.1038/s41467-023-40468-7 (2023).
doi: 10.1038/s41467-023-40468-7
pubmed: 37558674
pmcid: 10412608
Nikolados, E.-M., Wongprommoon, A., Aodha, O. M., Cambray, G. & Oyarzún, D. A. Accuracy and data efficiency in deep learning models of protein expression. Nature Communications 13, 7755. https://doi.org/10.1038/s41467-022-34902-5 (2022).
doi: 10.1038/s41467-022-34902-5
pubmed: 36517468
pmcid: 9751117
Raudys, S. & Jain, A. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 252–264. https://doi.org/10.1109/34.75512 (1991).
doi: 10.1109/34.75512
Kavzoglu, T. & Mather, P. M. The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing 24, 4907–4938. https://doi.org/10.1080/0143116031000114851 (2003).
doi: 10.1080/0143116031000114851
Li, Z., Kamnitsas, K. & Glocker, B. Overfitting of neural nets under class imbalance: analysis and improvements for segmentation. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 402–410, https://doi.org/10.1007/978-3-030-32248-9_45 (Springer, Cham, 2019).
Wei, Z. et al. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. American Journal of Human Genetics 92, 1008–1012. https://doi.org/10.1016/j.ajhg.2013.05.002 (2013).
doi: 10.1016/j.ajhg.2013.05.002
pubmed: 23731541
pmcid: 3675261
Wang, Z., Hu, M. & Zhai, G. Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors 18, 1126. https://doi.org/10.3390/s18041126 (2018).
doi: 10.3390/s18041126
pubmed: 29642454
pmcid: 5948514
Farag, M. & Mouawad, L. Comprehensive analysis of intramolecular g-quadruplex structures: furthering the understanding of their formalism. Nucleic Acids Research 52, 3522–3546 (2024).
doi: 10.1093/nar/gkae182
pubmed: 38512075
pmcid: 11039995
Berman, H. M. et al. The protein data bank. Nucleic Acids Research 28, 235–242. https://doi.org/10.1093/nar/28.1.235 (2000).
doi: 10.1093/nar/28.1.235
pubmed: 10592235
pmcid: 102472
Lu, X.-J. Dssr-enabled innovative schematics of 3d nucleic acid structures with pymol. Nucleic Acids Research 48, e74. https://doi.org/10.1093/nar/gkaa426 (2020).
doi: 10.1093/nar/gkaa426
pubmed: 32442277
pmcid: 7367123
del Villar-Guerra, R., Trent, J.O. & Chaires, J.B. G-quadruplex secondary structure from circular dichroism spectroscopy. Angewandte Chemie (International ed. in English) 57, 7171–7175, https://doi.org/10.1002/anie.201709184 (2018).
Tareen, A. & Kinney, J. B. Logomaker: beautiful sequence logos in Python. Bioinformatics 36, 2272–2274. https://doi.org/10.1093/bioinformatics/btz921 (2020).
doi: 10.1093/bioinformatics/btz921
pubmed: 31821414