HSE Scientists Develop DeepGQ: AI-based 'Google Maps' for G-Quadruplexes
Researchers at the HSE AI Research Centre have developed an AI model that opens up new possibilities for the diagnosis and treatment of serious diseases, including brain cancer and neurodegenerative disorders. Using artificial intelligence, the team studied G-quadruplexes—structures that play a crucial role in cellular function and in the development of organs and tissues. The findings have been published in Scientific Reports.
DNA can be thought of as a long chain of symbols made up of four letters: A, C, G, and T. Beyond the sequence itself, the way the DNA strand is folded and twisted is also crucial. This structure determines which regions of the genetic code are open—accessible to the cell for reading and replication—and which remain closed. One form of such 'packaging' is a special structure known as a G-quadruplex. It can be visualised as a small nodular structure that forms in regions rich in guanine (G). Scientists have hypothesised that each cell type has its own unique set of such nodules, which helps determine the cell’s function. For example, the DNA of nerve cells in the brain differs from that of liver cells in the pattern of these specialised structures. These variations influence how different cell types develop and function. However, investigating these processes in the laboratory is expensive and does not always produce reliable results.
Researchers at the HSE AI Research Centre have developed the DeepGQ AI model, which uses deep learning to generate tissue-specific maps of G-quadruplexes. The model analyses the DNA strand in both directions simultaneously, much like reading it from left to right and from right to left. This bidirectional approach allows the program to capture a complete and accurate picture of the features of the DNA region under study.
Artem Bashkatov, Junior Research Fellow at the Centre for Biomedical Research and Technology of the AI and Digital Science Institute, explains: 'We hypothesised that the characteristics of the cellular environment—not only the DNA structure itself—play a key role in determining how different tissue-specific cell types develop. To test this idea, we developed not a single universal model, but a set of specialised DeepGQ models tailored to different tissues. For example, one model was trained exclusively on brain cells (DeepGQ Neurons), another on liver cells (DeepGQ Liver), and so on across 14 tissue types. This approach enabled each model to identify developmental patterns specific to its tissue.'
Thanks to DeepGQ, scientists now have access to a tool that enables highly accurate prediction of G-quadruplexes. Rather than relying on costly experimental studies, any laboratory investigating, for example, liver cancer or Alzheimer’s disease can use data from patient samples and apply DeepGQ to generate an accurate map of the most likely targets for experimental validation.
'DeepGQ is essentially like Google Maps for G-quadruplexes: it plots landmarks (GQs) and traffic conditions (DHSs and histones) onto a flat DNA map that is unique to each "city," or tissue,' says Maria Poptsova, Director of the Centre for Biomedical Research and Technology at the Institute of AI and Digital Sciences. 'Many serious diseases—from cancer to neurodegeneration—are disorders of lost tissue identity. Cells either forget what they are meant to become, or their specialisation programmes break down. G-quadruplexes may become promising new targets for the treatment of such diseases. If a cancer cell depends on a specific GQ structure for its survival, it may be possible to develop a drug that either disrupts or stabilises this structure until the cellular programme collapses, ultimately killing the cancer cell. In the future, this approach could lead to the creation of a DeepGQ Patient model: by analysing data from a tumour biopsy, researchers could generate a personalised map of active G-quadruplexes and use it to design a truly individualised treatment strategy.'
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