Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals.
Cecilia M CasadeiChing-Ju TsaiAnton BartyMark S HunterNadia A ZatsepinCelestino PadesteGuido CapitaniW Henry BennerSébastien BoutetStefan P Hau-RiegeChristopher KupitzMarc MesserschmidtJohn I OgrenTom PardiniKenneth J RothschildLeonardo SalaBrent SegelkeGarth J WilliamsJames E EvansXiao-Dan LiMatthew ColemanBill PedriniMatthias FrankPublished in: IUCrJ (2018)
Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography at X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump-probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.
Keyphrases
- room temperature
- electron microscopy
- ionic liquid
- high resolution
- air pollution
- dual energy
- particulate matter
- single molecule
- oxidative stress
- computed tomography
- quantum dots
- optical coherence tomography
- high intensity
- magnetic resonance imaging
- radiation therapy
- magnetic resonance
- living cells
- radiation induced
- mass spectrometry
- convolutional neural network