Using the generated AI, chemists quickly calculate the 3D genome structure.

LUSTRATION in the subsequent procedure and chromogen followed by using sequence data to create a chromatin tissue. Credit: Greg Schuette et al
All cells in the body contain the same genetic sequence, but each cell expresses only the subset of those gene. These cell -specific genetic expression patterns that guarantee that brain cells are different from skin cells are partially determined by the three -dimensional structure of genetic substances that control the accessibility of each gene.
MIT chemists have come up with a new way to determine these 3D genome structures using aggressive artificial intelligence. These methods can predict thousands of structures in just a few minutes and are much faster than existing experimental methods for analyzing structures.
Using this method, researchers can easily study how the genome 3D tissue affects the gene expression patterns and functions of individual cells.
The survey results are published in Science Advances.
“Our goal was trying to predict the three -dimensional genome structure from the basic DNA sequence,” said Bin Zhang, an associate professor of chemistry and a senior researcher. “Because this can be done, this method is equivalent to the most advanced experimental technology, so you can have many interesting opportunities.”
Greg Shoot and Zhuohan Lao, a graduate student of MIT, is the chief of this paper.
From sequence to structure
In the cell nucleus, DNA and protein form a complex called chromatin, and have some levels of tissue, so the cells pack 2 meters into DNAs of 1/100 of the diameter. 。 A long -like DNA -like chain around a protein called Histon creates a structure like a string beads.
Chemical tags known as epigenetic modifications can be attached to DNA in a specific place, and these tags that differ depending on the cell type affect the folding of chromatin and the accessibility of the nearby genes. These differences in chromatin three -dimensional structures use different cell types to determine which genes will appear at different times in different cells.
For the past 20 years, scientists have developed experimental methods to determine the chroma -chin structure. One of the widely used technologies known as Hi-C works by linking the adjacent DNA chains in the nucleus of cells. After that, researchers can decide which segments are located near each other by shortening DNA into many small fragments.
This method can use most of the cells to calculate the average structure of the chromatin section or determine the structure in the specific cell with a single cell. However, HI-C and similar methods are labor-intensive, and may take about a week to generate data from one cell.
To overcome these restrictions, Zhang and his students have developed a model to create high -speed and accurate methods that predict the chromatin structure of a single cell using recent progress in produced AI. The AI models designed can quickly analyze the DNA sequence and predict the chromatin structure that can be generated in cells.
“Deep learning is really good at pattern recognition,” says Zhang. “This allows you to analyze a very long DNA segment, thousands of base pairs, and understand what the important information encoded at these DNA bases.”
CHROMOGEN, a model created by researchers, has two components. The deep learning model, the first component, is taught to “read” the genome and analyzes the information encoded with the basic DNA sequence and chromatin accessibility data. The latter is widely used and specific to cell type.
The second component is an AI model that predicts a physical and accurate chromatin three -dimensional structure trained with more than 11 million chromatin three -dimensional structure. These data were generated from experiments using DIP-C (Hi-C variant) in 16 cells from human B lymphocytes.
When integrated, the first component notifies the generation model about how the cell type -specific environment affects the formation of chromatin structure, and this scheme effectively captures the relationship between the sequence structure. For each sequence, researchers use models to generate many possible structures. This is because the DNA is a very disabled molecule, which can cause many different three -dimensional structures that can be different.
“The main complex factor that predicts the structure of the genome is that there is no single solution we are aiming for. There is a structure distribution, regardless of which part of the genome you are looking at. The high -dimensional statistical distribution is very challenging, “says Schuette.
Quick analysis
Once trained, the model can generate predictions on a much faster time scale than Hi-C and other experimental techniques.
“You may spend six months to execute experiments to acquire dozens of structures with a specific cell type, but one GPU uses a model in 20 minutes to use a model of 1000 in a specific area. Can be generated, “says Schuette.
After training the model, researchers used it to generate more than 2,000 DNA sequence structural predictions, compared them with the structure that was experimentally determined. They discovered that the structure generated by the model is the same or very similar to what the experimental data is found.
“We usually look at hundreds or thousands of three -dimensional sitting on each sequence. It gives you a reasonable expression of the structure that can be held by a specific area.” He says. “If you repeat the experiment multiple times with a different cell, it is very likely that it will have a very different three -dimensional structure. That is what our model is trying to predict.”
Researchers have also discovered that they can accurately predict data from cell type other than trained models. This suggests that this model can help analyze how the chromatin structure differs between cells and how they have affected the function. 。 You can use this model to find out how many chromatin states that may exist in a single cell and how they change their genes.
Another possible application is to investigate how a specific DNA sequence changes the three -dimensional structure of chromatin.
“There are many interesting questions that I think can be dealt with with this type of model,” says Zhang.
Researchers have made them use their data and models for others who want to use it.
Details: GREG SCHUETTE ET AL, CHROMOGEN: The diffusion model predicts the three -dimensional structure of a single cell chromatin, the progress of science (2025). Doi: 10.1126/Sciadv.adr8265
Provided by Massachusetts Institute of Technology
This story has been reissued by the favor of MIT NEWS (Web.mit.edu/newsoft/), a popular site that covers news about MIT research, innovation, and education.
Quotation: Using the generated AI, chemists calculate the 3D genome structure (January 31, 2025). 2025年1月31日https://phys.org/news/2025-01-01-01-01-01-ai-chemists-quickly-3d.htmlから取得
This document is subject to copyright. There is no part that is reproduced without writing permission, apart from fair transactions for private research and research purpose. Content is provided only by information.