Billion Proteins, Hidden Species & Singapore's 37% Loss
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Billion Proteins, Hidden Species & Singapore's 37% Loss
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ナレーター：
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著者：
(00:00:59) Open-Source vs AlphaFold3 Tradeoff
(00:01:55) Taiwan's Sesame-Seed Sea Slug
(00:02:53) Colombia's Endangered Orchid
(00:03:24) Singapore's 37% Species Loss
(00:03:54) What To Watch Next
Today's briefing opens with one of the most consequential releases in computational biology this year. The Chan Zuckerberg Initiative has published the ESM Atlas — more than one billion predicted protein structures built on ESMFold2 and trained on metagenomic data from soil and ocean samples. That's five times the scale of AlphaFold's database, and it maps proteins from organisms science hasn't formally catalogued yet. Crucially, ESMFold2 is fully open-source with no commercial restrictions, a direct contrast to Google DeepMind's proprietary AlphaFold3. Antibody design benchmarks are strong; the one open question is whether the model closes the earlier ESMFold's documented weakness on structurally unusual proteins.
From the molecular to the microscopic: researchers off the coast of Keelung, Taiwan formally described Thecacera sesama, a nudibranch under three millimeters long. Its food source may itself be an undescribed species, meaning one find opened two gaps. In Colombia's Cundinamarca cloud forest, a new orchid — Epidendrum juaicaense — was named in honour of the indigenous Muisca people and immediately assessed as endangered. Discovery and threatened status arrived simultaneously.
The episode closes with a statistical study on Singapore that calculated a 37% species loss over two centuries using the MODGEE model, which accounts for extinctions that occurred before detection. The implication: conservation baselines everywhere may be significantly underestimated.
The connecting thread — biology is generating knowledge faster than it's protecting what it finds. A YesWee production, built using AI technology.
This episode includes AI-generated content.
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