Understanding the 3D molecular structure nano-objects like proteins and viruses is crucial in biology and medicine. With recent advances in X-ray technology, scientists can now collect diffraction images from individual particles, ultimately allowing researchers to visualize molecules at room temperature.
However, determining 3D structure from these single-particle diffraction experiments is a significant hurdle: current data acquisition rates typically result in fewer than 10 useful snapshots per minute, limiting the amount of features that can be resolved. Additionally, the images are often highly corrupted with noise and other experimental artifacts.
To meet these challenges, a team from the Lawrence Berkeley National Laboratory has developed a new algorithmic framework called multi-tiered iterative phasing (M-TIP) that utilizes advanced mathematical techniques to determine 3D molecular structure from very sparse sets of noisy, single-particle data. Developed by CAMERA (The Center for Advanced Mathematics for Energy Research Applications), the algorithms will be contributed to the Exascale Computing Project collaborative initiative led by the SLAC National Accelerator Laboratory to improve the collection and analysis of data from X-ray free-electron lasers.
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