VOA慢速：科学家们因预测和设计蛋白质而荣获诺贝尔化学奖

Scientists Win Nobel in Chemistry for Predicting, Designing Proteins

慢速| 六级 偏易 | 639词 | 6min54s

刘立军供稿

Part I. QUESTIONS

Listen to the news and choose the best answer to each question you hear.

Q1. What is the significance of the AI model AlphaFold2 in the context of the 50-year-old problem?

A. It was the first AI model to predict protein structures.

B. It solved the problem by predicting the structure of all known proteins.

C. It was developed by combining the efforts of all three Nobel winners.

D. It was a precursor to the technology that eventually solved the problem.

Q2. What is the potential impact of the Nobel winners’ work on the field of medicine?

A. It will likely lead to the development of new materials for medical devices.

B. It will enable the creation of personalized medicine for every individual.

C. It will revolutionize the way surgeries are performed.

D. It could result in the discovery of new drugs and vaccines.

Q3. What does the text suggest about the importance of understanding protein structures in biochemistry?

A. It has been a grand challenge for many years.

B. It is a relatively new area of study with few challenges.

C. It is a simple task that can be accomplished with basic laboratory equipment.

D. It is no longer considered a significant problem in the field.

Q4. What specific achievement of David Baker’s is mentioned in the text, and what does it imply about the current state of protein design?

A. He designed the first protein in 2003, indicating protein design was possible but limited.

B. He designed a new protein in 2003, suggesting a broader scope for protein construction.

C. He has only recently begun designing proteins after 2020.

D. He has yet to design a protein that can protect against viruses.

Q5. What can be inferred about the potential applications of the technology in understanding antibiotic resistance?

A. It will help in creating new antibiotics.

B. It will provide a complete understanding of antibiotic resistance mechanisms.

C. Researchers will be able to use it to better understand how organisms develop resistance to antibiotics.

D. It will lead to the immediate development of a vaccine for antibiotic-resistant bacteria.

Q6. What is the role of computational tools in protein design according to the text?

A. They are used to predict the structure of existing proteins.

B. They enable the design of new proteins with unique shapes and functions.

C. They are used to build physical models of proteins for educational purposes.

D. They are used to simulate the interaction between proteins and other molecules.

Q7. What can be inferred about the collaboration between Baker and Hassabis/Jumper in the field of protein design?

A. Baker’s work was independent and did not benefit from Hassabis and Jumper’s AI research.

B. Baker and Hassabis/Jumper worked in completely separate fields and had no interaction.

C. Hassabis and Jumper’s AI research provided a significant boost to Baker’s protein design efforts.

D. Baker’s computational tools were developed in response to Hassabis and Jumper’s work but did not integrate it.

Q8. What can be inferred about the potential of a nasal spray designed by Baker’s team to protect against pandemic viruses?

A. It is a speculative idea with no scientific basis.

B. It is a concept that has been fully realized and is ready for mass production.

C. It is a guaranteed solution to all pandemic viruses.

D. It is based on a protein designed during the pandemic that proved effective against the coronavirus.

Q9. What is the main idea of the text, and how does it reflect the significance of the Nobel Prize in Chemistry winners’ work?

A. The winners’ work has opened up new avenues in protein research, highlighting the importance of interdisciplinary approaches.

B. The winners’ work is a testament to the power of AI in solving complex scientific problems.

C. The winners’ work has revolutionized the field of protein design and has immediate practical applications.

D. The winners’ work has been recognized for its potential to change our understanding of life itself.

Part II. TRANSCRIPT

Scientists Win Nobel in Chemistry for Predicting, Designing Proteins

Three scientists who developed methods to predict the structure of proteins and build new ones have won the Nobel Prize in Chemistry.

The winners were American David Baker, a professor at the University of Washington; Briton Demis Hassabis, head of Google’s DeepMind research laboratory in London; and American John Jumper, a top researcher at DeepMind. The lab centers on artificial intelligence (AI) methods.

Protein is one of the most important substances to life on Earth. Baker succeeded in building new kinds of proteins. Hassabis and Jumper solved a problem that had existed for 50 years. They were finally able to use AI to predict the structure of proteins.

The Nobel Committee for Chemistry said the discoveries “hold enormous potential.”

(Q2) For example, committee members said the ability to build new proteins could lead to the discovery of new drugs and vaccines. It could also help scientists develop extremely small materials, called nanomaterials, and small sensors.

Heiner Linke is Chair of the Nobel Committee for Chemistry. He said the award honored research that made connections for the first time between amino acid sequences and protein structures.

amino adj. 氨基酸的

(Q3) “That was actually a grand challenge in chemistry, and in particular biochemistry for many years,” Linke said.

Designing and predicting proteins

(Q4) Baker first designed a new protein in 2003. Since then, his research group has produced many different proteins. “It seems that you can almost construct any type of protein now with this technology,” said Professor Johan Aqvist of the Nobel committee.

(Q1-2) The committee said Hassabis and Jumper created an AI model called AlphaFold2. It has been able to predict the structure of nearly all 200 million proteins researchers have identified.

Linke said, “Proteins are the molecules that enable life. Proteins are building blocks that form bones, skin, hair and tissue.” He added, “To understand how life works, we first need to understand the shape of proteins.”

molecule n. 分子

Linke said that in 2020, Hassabis and Jumper were able to use AI methods to finally “crack the code.” That made it possible to predict the complex structure of “any known protein in nature.”

(Q5) There are many possible uses for the technology. Researchers should be able to use it to better understand how organisms develop resistance to antibiotics. They might also be able to create images of chemical substances called enzymes that can break down plastic.

antibiotic n. 抗生素

enzyme n. 酶

(Q6) The committee said Baker had developed “computational tools” that enable scientists to design new proteins with new shapes and functions.

(Q7-1) Baker noted that Hassabis and Jumper’s work in AI had greatly helped his team.

“The breakthroughs made by Demis and John on protein structure prediction really highlighted to us the power that AI could have,” he said. (Q7-2) “And that led us to apply these AI methods to protein design and that has greatly increased the power and accuracy.”

Baker was asked during a phone call with Nobel officials and reporters if he had a favorite protein. He said it would be difficult to choose. (Q8) But he noted that one designed during the pandemic proved to be effective in protecting against the coronavirus.

“And I’ve been very excited about the idea of a nasal spray, of little designed proteins, that would protect against all possible pandemic viruses.” Baker said.

nasal adj. 鼻的，鼻腔的

Hassabis is a leader in Britain’s technology industry. He received a knighthood earlier this year for his work in AI. He helped establish DeepMind in 2010. Google later bought the lab.

knighthood n. 骑士身份，骑士地位

DeepMind first gained wide attention by developing an AI system that was able to defeat the human world champion in the Chinese game of Go faster than expected.

This year’s Nobel Prize in Chemistry is worth about $1 million. Half of the prize will go to Baker. Hassabis and Jumper will share the other half.

Award ceremonies for the 2024 Nobel Prize winners will be held on December 10.

Part III. KEY

Q1. B. 细节题。根据“The committee said Hassabis and Jumper created an AI model called AlphaFold2. It has been able to predict the structure of nearly all 200 million proteins researchers have identified.”这表明AlphaFold2通过预测近2亿种已知蛋白质的结构，解决了50年的难题。因此答案是B。

Q2. D. 细节题。根据“For example, committee members said the ability to build new proteins could lead to the discovery of new drugs and vaccines.” 可知获奖者的工作可能会带来新药物和疫苗的发现。因此答案是D。

Q3. A. 细节题。根据“‘That was actually a grand challenge in chemistry, and in particular biochemistry for many years,’ Linke said.” 可知理解蛋白质结构一直是生物化学领域多年来的重大挑战。因此答案是A。

Q4. B. 细节题。根据“Baker first designed a new protein in 2003. Since then, his research group has produced many different proteins.” 可知自2003年以来，Baker及其团队已经设计出了许多不同的蛋白质，暗示了蛋白质设计的广泛可能性。因此答案是B。

Q5. C. 推理题。根据“There are many possible uses for the technology. Researchers should be able to use it to better understand how organisms develop resistance to antibiotics.” 说明该技术的一个潜在应用是帮助研究人员更好地理解生物体对抗生素产生耐药性的机制。因此答案是C。

Q6. B. 细节题。根据“Baker had developed ‘computational tools’ that enable scientists to design new proteins with new shapes and functions.” 可知计算工具使科学家能够设计具有新形状和功能的新型蛋白质。因此答案是B。

Q7. C. 推理题。根据“Baker noted that Hassabis and Jumper’s work in AI had greatly helped his team.”和“And that led us to apply these AI methods to protein design and that has greatly increased the power and accuracy.”可知Hassabis和Jumper在AI领域的研究极大地帮助了Baker的团队，并增强了他们在蛋白质设计方面的能力和准确性。因此答案是C。

Q8. D. 推理题。根据“And I noted that one designed during the pandemic proved to be effective in protecting against the coronavirus.”可知鼻喷雾是Baker团队在疫情期间设计的，并且它被证明是对新冠病毒有效的蛋白质。因此答案是D。

Q9. A. 主旨题。整个文本强调了三位科学家在蛋白质结构预测和设计方面的成就，以及他们的工作如何利用AI方法开辟了新的研究方向。这反映了跨学科方法的重要性，并突出了诺贝尔化学奖得主工作的意义。因此答案是A。

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