The new tool, ProteinMPNN, described by a group of researchers from the University of Washington in two papers published in Science today (available here and here), offers a powerful complement to that technology.
The papers are the latest example of how deep learning is revolutionizing protein design by giving scientists new research tools. Traditionally researchers engineer proteins by tweaking those that occur in nature, but ProteinMPNN will open an entire new universe of possible proteins for researchers to design from scratch.
“In nature, proteins solve basically all the problems of life, ranging from harvesting energy from sunlight to making molecules. Everything in biology happens from proteins,” says David Baker, one of the scientists behind the paper and director of the Institute for Protein Design at the University of Washington.
“They evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful.”
Proteins consist of hundreds of thousands of amino acids that are linked up in long chains, which then fold into three-dimensional shapes. AlphaFold helps researchers predict the resulting structure, offering insight into how they will behave.
ProteinMPNN will help researchers with the inverse problem. If they already have an exact protein structure in mind, it will help them find the amino acid sequence that folds into that shape. The system uses a neural network trained on a very large number of examples of amino acid sequences, which fold into three-dimensional structures.
But researchers also need to solve another problem. To design proteins that tackle real-world problems, such as a new enzyme that digests plastic, they first have to figure out what protein backbone would have that function.
To do that, researchers in Baker’s lab use two machine-learning methods, detailed in an article in Science last July, that the team calls “constrained hallucination” and “in painting.”
The Blue Technology Barometer 2022/23
The overall rankings tab shows the performance of the examined
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This pillar ranks each country on the sustainability of its
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This pillar ranks each country on its contribution to ocean
sustainable technology research and development, including
expenditure, patents, and startups.
This pillar ranks each country on its stance on ocean
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national-level policies, taxes, fees, and subsidies, and the
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What Shanghai protesters want and fear
You may have seen that nearly three years after the pandemic started, protests have erupted across the country. In Beijing, Shanghai, Urumqi, Guangzhou, Wuhan, Chengdu, and more cities and towns, hundreds of people have taken to the streets to mourn the lives lost in an apartment fire in Urumqi and to demand that the government roll back its strict pandemic policies, which many blame for trapping those who died.
It’s remarkable. It’s likely the largest grassroots protest in China in decades, and it’s happening at a time when the Chinese government is better than ever at monitoring and suppressing dissent.
Videos of these protests have been shared in real time on social media—on both Chinese and American platforms, even though the latter are technically blocked in the country—and they have quickly become international front-page news. However, discussions among foreigners have too often reduced the protests to the most sensational clips, particularly ones in which protesters directly criticize President Xi Jinping or the ruling party.
The reality is more complicated. As in any spontaneous protest, different people want different things. Some only want to abolish the zero-covid policies, while others have made direct calls for freedom of speech or a change of leadership.
I talked to two Shanghai residents who attended the protests to understand what they experienced firsthand, why they went, and what’s making them anxious about the thought of going again. Both have requested we use only their surnames, to avoid political retribution.
Zhang, who went to the first protest in Shanghai after midnight on Saturday, told me he was motivated by a desire to let people know his discontent. “Not everyone can silently suffer from your actions,” he told me, referring to government officials. “No. People’s lives have been really rough, and you should reflect on yourself.”
In the hour that he was there, Zhang said, protesters were mostly chanting slogans that stayed close to opposing zero-covid policies—like the now-famous line “Say no to covid tests, yes to food. No to lockdowns, yes to freedom,” which came from a protest by one Chinese citizen, Peng Lifa, right before China’s heavily guarded party congress meeting last month.
While Peng hasn’t been seen in public since, his slogans have been heard and seen everywhere in China over the past week. Relaxing China’s strict pandemic control measures, which often don’t reflect a scientific understanding of the virus, is the most essential—and most agreed-upon—demand.
Biotech labs are using AI inspired by DALL-E to invent new drugs
Today, two labs separately announced programs that use diffusion models to generate designs for novel proteins with more precision than ever before. Generate Biomedicines, a Boston-based startup, revealed a program called Chroma, which the company describes as the “DALL-E 2 of biology.”
At the same time, a team at the University of Washington led by biologist David Baker has built a similar program called RoseTTAFold Diffusion. In a preprint paper posted online today, Baker and his colleagues show that their model can generate precise designs for novel proteins that can then be brought to life in the lab. “We’re generating proteins with really no similarity to existing ones,” says Brian Trippe, one of the co-developers of RoseTTAFold.
These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function. In effect, this makes it possible to come up with new proteins to do particular jobs on demand. Researchers hope that this will eventually lead to the development of new and more effective drugs. “We can discover in minutes what took evolution millions of years,” says Gevorg Grigoryan, CEO of Generate Biomedicines.
“What is notable about this work is the generation of proteins according to desired constraints,” says Ava Amini, a biophysicist at Microsoft Research in Cambridge, Massachusetts.
Proteins are the fundamental building blocks of living systems. In animals, they digest food, contract muscles, detect light, drive the immune system, and so much more. When people get sick, proteins play a part.
Proteins are thus prime targets for drugs. And many of today’s newest drugs are protein based themselves. “Nature uses proteins for essentially everything,” says Grigoryan. “The promise that offers for therapeutic interventions is really immense.”
But drug designers currently have to draw on an ingredient list made up of natural proteins. The goal of protein generation is to extend that list with a nearly infinite pool of computer-designed ones.
Computational techniques for designing proteins are not new. But previous approaches have been slow and not great at designing large proteins or protein complexes—molecular machines made up of multiple proteins coupled together. And such proteins are often crucial for treating diseases.