Protein structure prediction now easier, faster

doi:10.1126/science.373.6552.262

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DOI	10.1126/science.373.6552.262
	Protein structure prediction now easier, faster
	Elizabeth Pennisi
	2021-07-16
发表期刊	Science
出版年	2021
英文摘要	Proteins are the minions of life, working alone or together to build, manage, fuel, protect, and eventually destroy cells. To function, these long chains of amino acids twist and fold and intertwine into complex shapes that can be slow, even impossible, to decipher. Scientists have dreamed of simply predicting a protein's shape from its amino acid sequence—an ability that would open a world of insights into the workings of life. “This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. Now, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues have made AI-based structure prediction more powerful and accessible. Their method, described online in Science this week, works on not just simple proteins, but also complexes of proteins, and its creators have made their computer code freely available. Since the method was posted online last month, the team has used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker's and Baek's program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition ( Science , 4 December 2020, p. [1144][1]). Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind's program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. DeepMind has yet to publish its method and computer code for others to take advantage of. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind's CEO, tweeted that AlphaFold2's details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” DeepMind's 30-minute presentation at CASP was enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI's ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns. When given a new protein to model, RoseTTAFold proceeds along multiple “tracks.” One compares the protein's amino acid sequence with all similar sequences in protein databases. Another predicts pairwise interactions between amino acids within the protein, and a third compiles the putative 3D structure. The program bounces among the tracks to refine the model, using the output of each one to update the others. DeepMind's approach, although still under wraps, involves just two tracks, Baek and others believe. Gira Bhabha, a cell and structural biologist at New York University School of Medicine, says both methods work well. “Both the DeepMind and Baker lab advances are phenomenal and will change how we can use protein structure predictions to advance biology,” she says. A DeepMind spokesperson wrote in an email, “It's great to see examples such as this where the protein folding community is building on AlphaFold to work towards our shared goal of increasing our understanding of structural biology.” But AlphaFold2 solved the structures of only single proteins, whereas RoseTTAFold has also predicted complexes, such as the structure of the immune molecule interleukin-12 latched onto its receptor. Many biological functions depend on protein-protein interactions, says Torsten Schwede, a computational structural biologist at the University of Basel. “The ability to handle protein-protein complexes directly from sequence information makes it extremely attractive for many questions in biomedical research.” Baker concedes that, in general, AlphaFold2's structures are more accurate. But Savvides says the Baker lab's approach better captures “the essence and particularities of protein structure,” such as identifying strings of atoms sticking out of the sides of the protein—features key to interactions between proteins. Agard adds that Baker's and Baek's approach is faster and requires less computing power than DeepMind's, which relied on Google's massive servers. However, the DeepMind spokesperson wrote that its latest algorithm is more than 16 times as fast as the one it used at CASP in 2020. As a result, she wrote, “It's not clear to us that the system being described is an advance in speed.” Beginning on 1 June, Baker and Baek began to challenge their method by asking researchers to send in their most baffling protein sequences. Fifty-six head scratchers arrived in the first month, all of which have now predicted structures. Agard's group sent in an amino acid sequence with no known similar proteins. Within hours, his group got a protein model back “that probably saved us a year of work,” Agard says. Now, he and his team know where to mutate the protein to test ideas about how it functions. Because Baek's and Baker's group has released its computer code on the web, others can improve on it; the code has been downloaded 250 times since 1 July. “Many researchers will build their own structure prediction methods upon Baker's work,” says Jinbo Xu, a computational structural biologist at the Toyota Technological Institute at Chicago. Moult agrees: “When there's a breakthrough like this, 2 years later, everyone is doing it as well if not better than before.” [1]: http://www.sciencemag.org/content/370/6521/1144
领域	气候变化 ; 资源环境
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文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/334304
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Elizabeth Pennisi. Protein structure prediction now easier, faster[J]. Science,2021.
APA	Elizabeth Pennisi.(2021).Protein structure prediction now easier, faster.Science.
MLA	Elizabeth Pennisi."Protein structure prediction now easier, faster".Science (2021).