After reading through any of the old science textbooks, you may find that RNA is described as a near-purpose means, a molecular draft paper used to construct DNA-encoded proteins. But in the past decade, scientists have begun to use RNA as a goal. Research projects and biotech companies have sprung up, and their mission is to identify small molecule drugs that target RNA for a variety of diseases, such as infectious diseases and muscular dystrophy. The problem is that RNA often bends, twists, and distort its shape within a few milliseconds. Researchers have found it difficult to hit such a moving target.
“Imagery” made with NMR technology captures the RNA kinetics of HIV-1 virus, revealing shapes that can lead to new drug targets. Photo: Yu Xu, Laura Ganser, Megan Kelly, Hashim M Al-Hashimi, Duke University
Hashim M, Professor of Biochemistry and Chemistry, Duke University School of Medicine Dr. Al-Hashimi said that when it comes to targeting RNA, the devil is in the details, and the details are dynamic. Hashemi and his team invented a technology that captures many states of RNA molecules and screens out tens of thousands or even millions of potential drug candidates. In a study published in the journal Nature Structures and Molecular Biology, they showed that their technology can select compounds with anti-HIV activity from 100,000 people. This may provide a new paradigm for drug discovery, almost every drug is protein-specific. By making accurate target RNA possible, we will open up new areas and potentially save lives.
The “central rule” of molecular biology believes that genetic information is composed of DNA, RNA, protein, and behavior. But only about 2% of rna continues to make proteins. Some studies have shown that more than 90% of the RNA molecules synthesized in the template provided by DNA are RNA as the final state. But in the past 50 years, drug discovery efforts have neglected all of these “non-coding” RNAs. One obvious reason for this apparent omission is that RNA is one of the most flexible and active molecules. It does not have the typical gaps and gaps that drug developers use to target proteins. Even if it does, a given RNA may not sit long enough for scientists to capture it on film.
This is a long-standing problem in which the movement of life is in these molecules. But no one can predict which drugs will affect RNA, in large part because we don’t have good movies for them. Most methods that guide the discovery of drugs either rely on static images captured in the lab, or do not show the effects of molecules, or computation-based computer-generated movies, rather than real data. In 2011, Al-Hashimi combined the two to create a film of the first RNA molecule using MRI and computationally generated movies—in this case, jumping from one shape to another. The virus is infected with the HIV virus. His group then described the film separately, each frame depicting differently shaped RNAs and using computer programs to identify molecules that bind to RNA.
This proves the flexibility to conquer RNA, which is promising, but not rigorous. A more rigorous test is whether you can get a needle from the haystack. In this study, Al-Hashimi and his team used 78 known compounds that combine the same RNA targets with the HIV virus and add them to the chemical library of 100,000 compounds. When they use their technology to screen all compounds, they can extract known anti-HIV activity. Laura Ganser, a lead author of the study and a graduate student at Al-Hashimi Labs, said: The key to this research is that we know if drugs are bound together, which gives us a way to assess the accuracy of RNA shapes. Because it performs very well, we feel that the shape is accurate, so now we can find and design new drugs.
They also showed that if they use inferior quality, the quality of prediction will decline. The more accurate the data, the more accurate the prediction. We can always get the right answer, because of the cause of the error, by doing this specific exercise, and changing the amount of data entering the movie, we are sure that this is true. Researchers have shown that this technology not only predicts that molecules will bind RNA together, but also predicts linear, l-, and s-type molecules of a particular shape. This finding is important because, unlike small molecules, by competing for a specific binding site, a small molecule that targets a protein does not function properly by locking the molecule in an inactive conformation. Al-Hashimi and his team are currently using this technology to screen millions of anti-HIV active compounds before building a small library for further testing.
Boke Academy-Science Popular Science|Reference Journal: Natural Structures and Molecular Biology Structural and Molecular Biology|From: Duke University