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Rohan Paul· @rohanpaul_ai · X· · Original publication time SelectedAI score61

Anthropic uses Claude to map a 3D structure of a bacterial vault particleMachine translation

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Anthropic’s biology research team says similar proteins from bacteria and the viruses that infect them can also assemble into vault particles, previously known only in cells of complex organisms such as animals and plants. The team used Claude to determine a structure in days, while noting that Claude made some simple mistakes along the way.

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Anthropic’s biology lab used Claude to find and map a vault particle, a hollow barrel of protein that has sat unexplained in human cells since 1986, inside bacteria and the viruses that infect them, which nobody had shown before.

> Claude ran the actual wet-lab pipeline, not just the reading, cutting particle isolation from 3 days to about 90 minutes and delivering a first 3D structural model in 34 hours instead of weeks.

QuoteAnthropic@AnthropicAI
We’re sharing how our biology research team worked with Claude to map the first 3D structure of a vault particle from bacterial life. Vault particles are a longstanding mystery. They are barrel-shaped objects, among the largest protein structures found in our cells. They were first discovered by Leonard Rome and Nancy Kedersha in 1986, but their function remains unknown. With Claude, we’ve found results that might bring us closer to solving that mystery. Until now, vaults were only known in the cells of complex things like animals and plants. We have shown that similar proteins from bacteria (and the viruses that infect them) can also assemble into vaults. Finding the structures in these simpler forms of life may make it easier to investigate their function in our own cells. For our life sciences team, the project was also a demonstration of the potential (and the limitations) of using AI to accelerate science. In our full piece, we discuss how Claude was able to dramatically speed up some stages—manually determining the vault particle’s structure would have taken weeks to months of painstaking work, but here it took days. We also discuss how Claude made some simple mistakes along the way, and how we collaborated with our machine learning researchers to address them. Read more: https://t.co/KowVVCSPp8
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Source: Rohan Paul · x.com

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