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Computational Simulation of X-Ray Diffraction from Ribosome Molecule

A recent article published in Scientific Reports provided a comprehensive computational study of diffraction image formation during single-particle imaging (SPI) of a macromolecule. This macromolecule, containing over 100,000 non-hydrogen atoms, underwent X-Ray free-electron laser (XFEL) irradiation.

Computational Simulation of X-Ray Diffraction from Ribosome Molecule

Image Credit: overlays-textures/

The researchers demonstrated the viability of computational SPI analyses for biological samples of practical dimensions. They also showcased how the SIMEX platform could be utilized for simulations, assisting in the planning of relevant SPI experiments and determining optimal parameters for such endeavors.


Achieving atomic resolution in SPI using XFELs is a significant goal in X-Ray science. It promises invaluable insights into the biological sciences by providing high-resolution structural details of biosamples that are difficult to crystallize.

However, challenges like radiation damage from intense X-Ray pulses and the stochastic nature of diffraction signals persist. Theoretical investigations are crucial for advancing SPI techniques, optimizing their performance, and overcoming existing limitations.

About the Research

In this study, the authors employed the SIMEX simulation framework, accessible at the European XFEL facility, to conduct an SPI experiment. This framework comprises sequential modules for simulating SASE X-Ray pulses, beam propagation, X-Ray-sample interaction, processing diffraction patterns, creating X-Ray scattering patterns, and performing real-time structure determination from assembled X-Ray patterns in reciprocal space.

The selected sample for simulation was a ribosome molecule extracted from E. coli bacteria, identified as 4V6C in the protein data Bank (PDB) database. This molecule consists of 243,324 atoms, including 100,895 hydrogen atoms, with an atom-atom separation of approximately 300 Å.

The dynamics of the irradiated ribosome molecule were simulated using the tree-code-extended X-Ray matter dynamics (XMDYN) code, which employs a Monte Carlo technique to track the ionization dynamics of ions and atoms. It also models the real-space dynamics of atoms/ions and free electrons using a molecular dynamics (MD) technique.

Since both electrons and atoms are treated as classical particles, and the code provides information on each ion's atomic configuration, scattering patterns can be readily calculated from the atomic snapshots.

To capture the random fluctuations within the ribosome molecule under X-Ray irradiation, the researchers generated 100 different MD realizations, each randomly oriented with respect to the incoming X-Ray beam. The nominal X-Ray pulse parameters used in the simulations included a photon energy of 4.96 keV, 5×1011 photons per pulse, a pulse duration of 9 fs, and a focal size of 250 nm × 160 nm.

Research Findings

The researchers analyzed the radiation damage induced by X-Rays in the ribosome molecule by examining the bound electrons, average atomic displacement, and spatial distribution of ionization and displacement across different atomic species.

Non-hydrogen atoms, including carbon (C), nitrogen (N), and oxygen (O), contributed most significantly to the elastic scattering signal. Over time, the ionization of these atoms progressed uniformly, reducing the scattering area at the peak of the pulse to roughly two-thirds of its initial value.

Atomic displacement for these elements remained below 1 Å up to time zero. The study reports a higher degree of ionization in the central region of the molecule compared to the edges due to the electron density gradient, resulting in a non-uniform spatial distribution of bound electrons and impacting imaging quality in the 50-100 Å resolution range.

The study also investigated the convergence of the average three-dimensional (3D) reciprocal-space time-integrated image of the simulated molecule concerning the number of MD realizations. It showed that the relative standard deviation of the signal increased rapidly with increasing q, stabilizing at a value near 0.2.

Calculating the mean reciprocal-space image from N≥25 realizations would yield a relative standard deviation of less than 4 % across the entire q-range. Comparisons with results obtained for a smaller molecule, 2NIP, revealed comparable relative standard deviations, implying a similar number of realizations required for both molecules to achieve similar accuracy for the time-integrated 3D diffraction image.

Additionally, the researchers calculated the resolution-dependent resistance factor (R-factor) for both molecules to evaluate how radiation damage and Compton scattering affected the degradation of diffraction image quality compared to undamaged molecules. They found that the R-factor converged when 25 MD realizations were used for calculation, with similar R-factor values observed for both the ribosome and 2NIP molecules, despite their differing sizes, under the same X-Ray pulse parameters.


The researchers effectively modeled the SPI of a ribosome molecule using SIMEX and XMDYN, analyzing radiation damage, image convergence, and quality degradation. Their findings suggested the feasibility of SPI for realistic biological samples and the potential of SIMEX in optimizing future XFEL experiments.

They also provided valuable insights into radiation damage dynamics and diffraction image formation during SPI of macromolecules, which can enhance data analysis techniques and refine structure determination methods in the field.

Journal Reference

Stransky, M., et al. (2024). Computational study of diffraction image formation from XFEL irradiated single ribosome molecule. Sci Rep.

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.


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