Scientists have developed a groundbreaking AI-designed gene-editing enzyme that significantly enhances the CRISPR toolkit. Published in Science on 16 July 2026, the study introduces a new class of synthetic enzymes called SynTnpBs, which outperform their natural counterparts in both efficiency and versatility. These enzymes, derived from the TnpB family, ancestors of widely used CRISPR-Cas12 systems, are engineered using an advanced AI model known as the ESM Inverse Folding (ESM-IF1) model. This breakthrough marks a major step forward in the quest to create entirely novel gene-editing tools with unique functionalities. The research team, led by Jennifer Doudna, a biochemist at the University of California, Berkeley, and a co-recipient of the 2020 Nobel Prize in Chemistry, sought to overcome a key limitation of existing CRISPR technologies. While traditional CRISPR systems rely on naturally occurring enzymes like Cas9 and Cas12, these enzymes are constrained by evolutionary history. Their structures and functions are optimized for survival in microbial environments, limiting their adaptability for applications in human cells or plants. By leveraging AI, the researchers aimed to generate enzymes with tailored properties that could perform tasks previously unattainable with natural proteins. The process began with the known three-dimensional structure of the TnpB enzyme. Using the ESM-IF1 model, the team inputted this structural data into the AI system, which then proposed numerous variations of the enzyme's amino acid sequence. The goal was to identify modifications that would preserve the enzyme's essential functions while introducing new capabilities. The AI model analyzed vast datasets of evolutionary relationships and protein interactions to determine which mutations would retain functionality without compromising performance. The resulting designs were then tested in bacterial cultures, followed by trials in human cell lines and Arabidopsis thaliana plant cells. Among the 1,980 candidate designs evaluated in bacterial screens, 466 demonstrated measurable activity. Notably, approximately 8% of these variants exhibited superior performance compared to the original TnpB enzyme. In human cells, two of the most effective variants achieved editing efficiencies of up to 50%, surpassing the 28% efficiency of the native enzyme. Some variants matched the original enzyme's effectiveness, while others showed remarkable improvements at specific DNA targets, achieving nearly four times greater editing accuracy. These findings underscore the potential of AI-driven protein design to revolutionize gene-editing capabilities. To ensure the new enzymes were sufficiently distinct from their natural predecessors, the researchers assessed the degree of divergence from the original TnpB. Unlike previous AI approaches that produced proteins with over 99% similarity to natural homologs, the ESM-IF1 model enabled the creation of enzymes with only 72% to 83% similarity to their closest natural counterparts. This reduced resemblance suggests that the AI-generated enzymes possess fundamentally different molecular interactions, potentially opening new avenues for therapeutic and agricultural applications. Despite these successes, the study acknowledges limitations. The current approach focuses on a single family of nucleases, meaning the methodology may not apply universally to all CRISPR systems. Nevertheless, the results highlight the promise of combining AI with structural biology to expand the designable protein space. As the field continues to evolve, further refinements could lead to even more efficient, smaller, and versatile gene-editing tools. The long-term vision, according to the researchers, is to develop fully synthetic enzymes capable of performing functions beyond what nature has already provided.
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Novara MediaNeodvisenSredinaDejstva 60Objektivnost 65pred 7 dneviV članku se razpravlja o naraščajočem pojavu fantov in deklet z umetno inteligenco, raziskuje njihovo privlačnost in polemike, ki jih obkrožajo. Vprašuje, ali takšne tehnologije izboljšujejo človeške odnose ali zgolj predstavljajo nerealno fantazijo. V članku so poudarjene etične in delovne posledice razvoja umetne inteligence, s sklicevanjem na dela Jamesa Muldoona, ki sledijo potovanju umetne inteligence od izkoriščevalnih delovnih pogojev v vzhodni Afriki do njene vloge v osebnih odnosih. V članku ni jasnega stališča, vendar se postavljajo pomembna vprašanja o družbenem vplivu umetne inteligence v intimnih okoliščinah.
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Zakaj te ocene (Dejstva 60 · Objektivnost 65): The article presents subjective views on AI relationships without clear factual grounding. It leans into opinion and speculation rather than presenting verifiable facts, and the tone is biased towards questioning the validity of AI companionship.
Phys.orgNeodvisenSredinapred 5 uramiRaziskovalci so razvili nov encim za urejanje genov, zasnovan z umetno inteligenco, imenovan SynTnpB, ki v številnih aplikacijah presega naravni encim TnpB. Z uporabo modela ESM Inverse Folding (ESM-IF1) so znanstveniki preoblikovali TnpB tako, da so ohranili bistvene aminokisline za prepoznavanje DNK / RNK, medtem ko so spremenili druge regije, da bi izboljšali funkcionalnost. Testiranje na bakterijah, človeških celicah in rastlinskih celicah je pokazalo, da so SynTnpB dosegli znatno višjo učinkovitost urejanja v primerjavi z naravnim encimom, pri čemer so nekatere variante pokazale do štirikrat večjo učinkovitost. Študija poudarja napredek v zasnovanem na umetni inteligenci proteinskem dizajnu za ustvarjanje novih orodij za urejanje genov z izboljšanimi zmogljivostmi.
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Nature NewsNeodvisenSredinavčerajRaziskovalci so 16. julija 2026 objavili ugotovitve v reviji *Science*, ki dokazujejo, da lahko umetna inteligenca oblikuje sintetične encime CRISPR z izboljšano funkcionalnostjo v primerjavi z naravnimi varianti. Te "molekularne škarje", ki jih ustvari AI, bi lahko potencialno spremenile gensko urejanje na različnih področjih, vključno z medicino in kmetijstvom. Študija, ki jo vodi Jennifer Doudna, Nobelova nagrajenka za kemijo, raziskuje potencial AI za pospešitev odkrivanja novih, funkcionalnih nukleaz. Raziskava se osredotoča na proteine TnpB, ki so predniki široko uporabljenih komponent CRISPR, kot je Cas12. Z uporabo AI, znanstveniki želijo premagati omejitve v tradicionalnih eksperimentalnih metodah, kar omogoča hitro raziskovanje proteinov, ki ohranjajo biološko aktivnost.
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