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预测蛋白质结构的 AlphaFold 计算溶液参数数据库。

A database of calculated solution parameters for the AlphaFold predicted protein structures.

机构信息

Department of Chemistry and Biochemistry, The University of Montana, 32 Campus Dr, Missoula, MT, 59812, USA.

Proteomica e Spettrometria di Massa, IRCCS Ospedale Policlinico San Martino, Largo R. Benzi 10, 16132, Genova, Italy.

出版信息

Sci Rep. 2022 May 5;12(1):7349. doi: 10.1038/s41598-022-10607-z.

Abstract

Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting "folding frenzy" has already produced predicted protein structure databases for the entire human and other organisms' proteomes. However, rapidly ascertaining a predicted structure's reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients ([Formula: see text], [Formula: see text]) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding [Formula: see text], [Formula: see text], [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold's drawbacks were mitigated, such as generating whenever possible a protein's mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US-SOMO-AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures.

摘要

最近,人工智能程序在从蛋白质序列预测 3D 结构方面取得了惊人的进展,在准确性和速度方面彻底改变了这一领域。由此产生的“折叠狂潮”已经为整个人类和其他生物体的蛋白质组产生了预测的蛋白质结构数据库。然而,应该考虑根据溶液中测量的性质来快速确定预测结构的可靠性。对形状敏感的流体力学参数,如扩散和沉降系数([Formula: see text],[Formula: see text])和固有粘度([η]),可以快速评估整体结构的可能性,而小角 X 射线散射(SAXS)则会产生与结构相关的对距离分布函数 p(r)与 r。使用经过广泛验证的 UltraScan SOlution MOdeler (US-SOMO)套件,实现了一个从 AlphaFold 结构计算相应的[Formula: see text],[Formula: see text],[η],p(r)与 r 和其他参数的数据库。圆二色光谱使用 SESCA 程序进行计算。缓解了 AlphaFold 的一些缺点,例如尽可能生成蛋白质的成熟形式。其他问题,如 AlphaFold 仅直接适用于单链结构、缺乏辅基或灵活性问题,也在讨论中。总的来说,这个 US-SOMO-AF 数据库的实现应该已经有助于快速评估相关部分 AlphaFold 预测蛋白质结构在溶液中的一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9138/9072687/88f02c20c046/41598_2022_10607_Fig1_HTML.jpg

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