小角X射线散射
内在无序蛋白质
费斯特共振能量转移
回转半径
散射
结晶学
化学物理
生物系统
生物物理学
小角度散射
化学
材料科学
物理
荧光
聚合物
生物
核磁共振
光学
作者
Miao Yu,Andrey Gruzinov,Hao Ruan,Tom Scheidt,Aritra Chowdhury,Sabrina Giofrè,Ahmed S. A. Mohammed,Joana Caria,Paul F. Sauter,Dmitri I. Svergun,Edward A. Lemke
标识
DOI:10.1073/pnas.2415220121
摘要
Intrinsically disordered proteins (IDPs) adopt ensembles of rapidly fluctuating heterogeneous conformations, influencing their binding capabilities and supramolecular transitions. The primary conformational descriptors for understanding IDP ensembles—the radius of gyration ( R G ), measured by small-angle X-ray scattering (SAXS), and the root mean square (rms) end-to-end distance ( R E ), probed by fluorescent resonance energy transfer (FRET)—are often reported to produce inconsistent results regarding IDP expansion as a function of denaturant concentration in the buffer. This ongoing debate surrounding the FRET-SAXS discrepancy raises questions about the overall reliability of either method for quantitatively studying IDP properties. To address this discrepancy, we introduce a genetically encoded anomalous SAXS (ASAXS) ruler, enabling simultaneous and direct measurements of R G and R E without assuming a specific structural model. This ruler utilizes a genetically encoded noncanonical amino acid with two bromine atoms, providing an anomalous X-ray scattering signal for precise distance measurements. Through this approach, we experimentally demonstrate that the ratio between R E and R G varies under different denaturing conditions, highlighting the intrinsic properties of IDPs as the primary source of the observed SAXS-FRET discrepancy rather than shortcomings in either of the two established methods. The developed genetically encoded ASAXS ruler emerges as a versatile tool for both IDPs and folded proteins, providing a unified approach for obtaining complementary and site-specific conformational information in scattering experiments, thereby contributing to a deeper understanding of protein functions.
科研通智能强力驱动
Strongly Powered by AbleSci AI