AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Gregory R Bowman
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引用次数: 0

Abstract

Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, "solved" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.
AlphaFold 和蛋白质折叠:尚未死亡!构象组合是前沿。
就像经典电影 Monty Python 中的黑骑士一样,像蛋白质折叠这样的宏大科学挑战是很难完成的。值得注意的是,AlphaFold 为大众带来了高精度的结构预测,开辟了无数新的研究途径,从而彻底改变了结构生物学。尽管取得了巨大成功,但将结构预测(更不用说蛋白质折叠和相关问题)称为 "已解决 "是危险的,因为这样做可能会阻碍进一步的进展。试想一下,如果我们在第一家商业航空公司开业后就宣布 "解决 "了飞行问题,并停止对进一步研发的投资,世界将会变成什么样。同样,结构预测仍然存在一些重要的局限性,解决这些问题将使我们受益匪浅。此外,我们对单个蛋白质可采用的不同结构(称为构象组合)的巨大多样性以及蛋白质探索这一空间的动力学的理解也很有限。显而易见的是,构象组合对蛋白质的功能至关重要,了解蛋白质动力学的这一方面将提高我们设计新蛋白质和新药物的能力。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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