Structure determination from single-molecule X-ray scattering images using stochastic gradient ascent

TL;DR

The paper introduces RASTA, a novel Bayesian-based stochastic gradient ascent method for direct atomistic structure determination from single-molecule X-ray scattering images, achieving high resolution with low photon counts.

Contribution

RASTA is a new approach that enables direct atomistic electron density determination from noisy single-molecule X-ray scattering data using Bayesian methods.

Findings

Achieved 2Å resolution for small proteins from synthetic data.

Successfully reconstructed structures with as low as 15 photons per image.

Demonstrated effectiveness of RASTA in challenging low-signal scenarios.

Abstract

Scattering experiments using ultrashort X-ray free electron laser (XFEL) pulses have opened a new path for structure determination of a wide variety of specimens, including nano-crystals and entire viruses, approaching atomistic spatial and femtoseconds time resolution. However, random and unknown sample orientations as well as low signal to noise ratios have so far prevented a successful application to smaller specimens like single biomolecules. We here present resolution-annealed stochastic gradient ascent (RASTA), a new approach for direct atomistic electron density determination, which utilizes our recently developed rigorous Bayesian treatment of single-particle X-ray scattering. We demonstrate electron density determination at 2\r{A} resolution of various small proteins from synthetic scattering images with as low as 15 photons per image.