|
|
@@ -81,18 +81,6 @@ What is still lacking is an explanation of why it should be that certain amino a
|
|
|
|
|
|
AlphaFold 2 is a stunning achievement in terms of building a machine to transform sequence data into protein models, but we may have to wait for further study of the program itself to know what it is telling us about the big picture of protein behavior.
|
|
|
|
|
|
-{{<rawhtml>}}
|
|
|
-
|
|
|
-<amp-ad width="100vw" height="320"
|
|
|
- type="adsense"
|
|
|
- data-ad-client="ca-pub-2730293094729387"
|
|
|
- data-ad-slot="5998325770"
|
|
|
- data-auto-format="rspv"
|
|
|
- data-full-width="">
|
|
|
- <div overflow=""></div>
|
|
|
-</amp-ad>
|
|
|
-
|
|
|
-{{</rawhtml>}}
|
|
|
|
|
|
## 兩大頂級 AI 演算法開源！Alphafold2 蛋白質預測準度逼近滿分，將顛覆生醫產業樣貌
|
|
|
|
|
|
@@ -230,18 +218,6 @@ RoseTTAFold 開源 地址
|
|
|
|
|
|
訂閱《TechOrange》每日電子報！
|
|
|
|
|
|
-{{<rawhtml>}}
|
|
|
-
|
|
|
-<amp-ad width="100vw" height="320"
|
|
|
- type="adsense"
|
|
|
- data-ad-client="ca-pub-2730293094729387"
|
|
|
- data-ad-slot="5998325770"
|
|
|
- data-auto-format="rspv"
|
|
|
- data-full-width="">
|
|
|
- <div overflow=""></div>
|
|
|
-</amp-ad>
|
|
|
-
|
|
|
-{{</rawhtml>}}
|
|
|
|
|
|
## DeepMind open-sources AlphaFold 2 for protein structure predictions
|
|
|
|
|
|
@@ -269,38 +245,12 @@ Beyond aiding the pandemic response, DeepMind expects AlphaFold will be used to
|
|
|
|
|
|
DeepMind says it’s committed to making AlphaFold available “at scale” and collaborating with partners to explore new frontiers, like how multiple proteins form complexes and interact with DNA, RNA, and small molecules. Earlier this year, the company announced a new partnership with the Geneva-based Drugs for Neglected Diseases initiative, a nonprofit pharmaceutical organization that used AlphaFold to identify fexinidazole as a replacement for the toxic compound melarsoprol in the treatment of sleeping sickness.
|
|
|
|
|
|
-{{<rawhtml>}}
|
|
|
-
|
|
|
-<amp-ad width="100vw" height="320"
|
|
|
- type="adsense"
|
|
|
- data-ad-client="ca-pub-2730293094729387"
|
|
|
- data-ad-slot="5998325770"
|
|
|
- data-auto-format="rspv"
|
|
|
- data-full-width="">
|
|
|
- <div overflow=""></div>
|
|
|
-</amp-ad>
|
|
|
-
|
|
|
-{{</rawhtml>}}
|
|
|
-
|
|
|
## Highly accurate protein structure prediction with AlphaFold
|
|
|
|
|
|
]
|
|
|
|
|
|
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
|
|
|
|
|
|
-{{<rawhtml>}}
|
|
|
-
|
|
|
-<amp-ad width="100vw" height="320"
|
|
|
- type="adsense"
|
|
|
- data-ad-client="ca-pub-2730293094729387"
|
|
|
- data-ad-slot="5998325770"
|
|
|
- data-auto-format="rspv"
|
|
|
- data-full-width="">
|
|
|
- <div overflow=""></div>
|
|
|
-</amp-ad>
|
|
|
-
|
|
|
-{{</rawhtml>}}
|
|
|
-
|
|
|
## How DeepMind's AI Cracked a 50-Year Science Problem Revealed
|
|
|
|
|
|
]
|
|
|
@@ -328,20 +278,3 @@ Proteins are essential to life, and understanding their structure can facilitate
|
|
|
DeepMind’s groundbreaking work and the open-sourcing of AlphaFold version 2.0 may help accelerate innovation through scientific collaboration in life sciences, biotechnology, data science, molecular biology, pharmaceutical, and more industries to discover novel treatments and medications to extend human longevity in the future.
|
|
|
|
|
|
Copyright © 2021 Cami Rosso All rights reserved.
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
-{{<rawhtml>}}
|
|
|
-
|
|
|
-<amp-ad width="100vw" height="320"
|
|
|
- type="adsense"
|
|
|
- data-ad-client="ca-pub-2730293094729387"
|
|
|
- data-ad-slot="5171471751"
|
|
|
- data-auto-format="mcrspv"
|
|
|
- data-full-width="">
|
|
|
- <div overflow=""></div>
|
|
|
-</amp-ad>
|
|
|
-{{</rawhtml>}}
|
|
|
-
|
|
|
-
|
Your Name