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Steffen Schultze

Publications and source records attributed to Steffen Schultze.

5 recordsLinked to original sources

Bayesian MINFLUX localization microscopy

MINFLUX microscopy allows for localization of fluorophores with nanometer precision using targeted scanning with an illumination profile with a minimum. However, current scanning patterns and the overall procedure are based on heuristics, and may therefore be suboptimal. Here we present a rigorous Bayesian that offers maximal resolutions from either minimal detected photons or minimal exposures. We estimate using simulated localization runs that this approach should reduce the number of photons required for 1 nm resolution by a factor of about four.

physics.comp-ph↗

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

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 2Å resolution of various small proteins from synthetic scattering images with as low as 15 photons per image.

physics.comp-ph↗

Bayesian electron density determination from sparse and noisy single-molecule X-ray scattering images

Single molecule X-ray scattering experiments using free electron lasers hold the potential to resolve both single structures and structural ensembles of biomolecules. However, molecular electron density determination has so far not been achieved due to low photon counts, high noise levels and low hit rates. Most analysis approaches therefore focus on large specimen like entire viruses, which scatter substantially more photons per image, such that it becomes possible to determine the molecular orientation for each image. In contrast, for small specimen like proteins, the molecular orientation cannot be determined for each image, and must be considered random and unknown. Here we developed and tested a rigorous Bayesian approach to overcome these limitations, and also taking into account intensity fluctuations, beam polarization, irregular detector shapes, incoherent scattering and background scattering. We demonstrate using synthetic scattering images that it is possible to determine electron densities of small proteins in this extreme high noise Poisson regime. Tests on published experimental data from the coliphage PR772 achieved the detector-limited resolution of $9\,\mathrm{nm}$, using only $0.01\,\%$ of the available photons per image.

physics.comp-ph↗

Stochastic Algorithms for Large-Scale Composite Optimization: the Case of Single-Shot X-FEL Imaging

We apply a recently developed framework for analyzing the convergence of stochastic algorithms to the general problem of large-scale nonconvex composite optimization more generally, and nonconvex likelihood maximization in particular. Our theory is demonstrated on a stochastic gradient descent algorithm for determining the electron density of a molecule from random samples of its scattering amplitude. Numerical results on an idealized synthetic example provide a proof of concept. This opens the door to a broad range of algorithmic possibilities and provides a basis for evaluating and comparing different strategies. While this case study is very specific, it shares a structure that transfers easily to many problems of current interest, particularly in machine learning.

math.OC↗

De novo structural ensemble determination from single-molecule X-ray scattering: A Bayesian approach

Single molecule X-ray scattering experiments with free electron lasers have opened a new route to the structure determination of biomolecules. Because typically only very few photons per scattering image are recorded and thus the signal-to-noise ratio is very low in this extreme Poisson regime, structure refinement is quite challenging. In addition, in each scattering event the orientation of the biomolecule is random and unknown. As a further layer of complexity, many biomolecules show structural heterogeneity and conformational transitions between different distinct structures; these structural dynamics are averaged out by existing refinement methods. To overcome these limitations, here we developed and tested a rigorous Bayesian approach and demonstrate that it should be possible to determine not only a single structure, but an entire structural ensemble from these experiments. Using $10^6$ synthetic scattering images generated from molecular dynamics trajectories, our approach was able to resolve ensembles of eight alanine dipeptide conformers at $2\,$Å resolution; similarly, we determined the unfolded ensemble of the protein chignolin at $4-7\,$Å resolution using $1.2\cdot 10^7$ images. Unexpectedly, much fewer images are required to determine an ensemble of $n$ structures of $m$ atoms each than a single structure of $n\times m$ atoms, i.e., of the same total number of degrees of freedom. These findings show that X-ray scattering experiments using state-of-the-art free electron lasers should allow one to determine not only biomolecular structures, but whole structure ensembles and, ultimately, 'molecular movies'.

physics.comp-ph↗