Ekip, gen düzenleyen proteinleri daha güvenli hale getirmek amacıyla yeniden tasarlamak için AlphaFold AI'yı kullanıyor

DNA'yı seçici olarak hedef alabilen sistemlerin keşfinden birkaç on yıl sonra, gen düzenlemeye dayalı ilk tedavileri görmeye başlıyoruz. Bu gelişmelerin karşılaştığı zorluklardan biri güvenliktir. Bunları düzenlemek istediğimiz gene oldukça özel hale getirebilsek de insan genomu çok büyüktür ve nadir DNA dizileri bile tesadüfen birkaç kez ortaya çıkabilir.

Sonuç olarak, tüm orijinal gen düzenleme sistemleri, hedef dışı etkiler olarak adlandırılan bilinen oranlara sahipti; bu etkilerde, bunlar basitçe yanlış diziyi düzenlediler. Bu düşük olasılıklı bir olay olabilir, ancak yeterli sayıda hücreyi düzenlerseniz (ve terapilerin genellikle çok sayıda düzenleme yapması gerekir) ve hatalar kaçınılmaz hale gelir.

Hedef dışı düzenlemeleri en aza indirmenin veya ortadan kaldırmanın yollarını bulmak için çok çaba harcandı. Nature dergisinin yakın tarihli bir sayısında araştırmacılar, hedef dışı etkilerden sorumlu olan gen düzenleyici proteinlerin temel alanlarının belirlenmesine yardımcı olmak için AI protein katlama yazılımı AlphaFold'un değiştirildiğini açıkladı. Bu alanlar daha sonra sorunları azaltmak için değiştirildi.

Yorumlar Google'ın AlphaFold'u, gen düzenleme proteininin hangi bölümlerinin hatalara yol açtığını belirlemeye yardımcı olabilir. Google's AlphaFold can help ID what parts of a gene editing protein enable mistakes. A couple of decades after the discovery of systems that could selectively target DNA, we’re starting to see the first therapies based on gene editing.

One challenge these developments have faced is safety. While we can make them pretty specific to the gene we want edited, the human genome is very large, and even rare DNA sequences can appear a couple of times by chance. As a result, all the original gene-editing systems had known rates of what are called off-target effects, in which they simply edit the wrong sequence.

This may be a low-probability event, but edit enough cells—and therapies generally have to edit many—and errors become inevitable. A lot of effort has gone into finding ways to minimize or eliminate off-target edits. In a recent issue of Nature, researchers described modifying the AI protein-folding software AlphaFold to help identify key areas of gene-editing proteins responsible for off-target effects.

Those areas were then modified to reduce the problems. Gene editing systems have three key components. The first is guide RNA, which can base-pair with the targeted genome sequence.

It’s possible to design many guide RNAs that all target the same gene, so one approach to making the system safer is to pick sequences that don’t share much similarity with any other genomic locations. This is now widely used as part of the basic design process for a gene-editing system. The second component is a Cas protein named after the CRISPR system’s Cas9 protein.

It interacts with both the guide RNA and genomic RNA and helps enforce the specificity of the interactions. If there are too many mismatches in the base pairing between the RNA and DNA, Cas9 (or other Cas family members) won’t stick there. A variety of approaches have led to improved Cas family members that have reduced the tendency to enable off-target edits.

The final key component of the system is the protein that interacts with Cas9 when it’s bound to DNA and modifies the DNA. In the original CRISPR system, this cut both strands of the double helix, producing damage that’s difficult to control. Researchers have since modified other proteins to interact with Cas9 but catalyze more subtle changes to DNA, such as lopping off a single base or making chemical modifications that alter how it base-pairs.

Overall, the length of the base pairing between the guide RNAs and the genome is on the order of 18 bases long. That should show up in random DNA sequences only about once in 70 billion bases, and our genome is only about 3 billion bases. By that measure, we should be good.

But it turns out that Cas9 can tolerate a small number of mispaired bases without losing its ability to stick to DNA. The exact number and location of the bases where variations are tolerated can vary somewhat, making it difficult to identify in advance which guide RNAs might pose a danger. One route to improving safety is to better understand how these off-target interactions occur.

The team behind the new work, based at a variety of institutions in China, reasoned out their approach in advance. A perfectly matched DNA-RNA hybrid will have one structure, while one with one or more mispaired bases will have a slightly different structure.