Structural Dynamics-Us最新文献

High-intensity femtosecond-duration x-rays from free electron lasers have enabled innovative imaging techniques that employ smaller crystal sizes than conventional crystallography. Developments aimed at increasing x-ray pulse intensities bring opportunities and constraints due to ultra-fast changes to atomic scattering form factors from electron dynamics. Experiments on silicon by Inoue et al. [Inoue et al., Phys. Rev. Lett. 131, 163201 (2023)] illustrate this by measuring diffraction efficiencies with increasing x-ray pulse intensities. Results at the highest experimental x-ray pulse intensity have been theoretically studied [Inoue et al., Phys. Rev. Lett. 131, 163201 (2023); Ziaja et al., Atoms 11, 154 (2023)] but not fully reproduced, which raises questions about the mechanisms behind these changes. Using collisional radiative simulations and relativistic configuration-averaged atomic data, we compute the ionization dynamics and diffraction efficiency of silicon and find good agreement within the experimental uncertainty. We incorporate the effects of ionization potential depression by removing energy levels close to the ionization threshold over selected charge states. We identify the main electron impact mechanisms present in our simulations. We bridge the gap between high and low intensity and find regimes where electronic damage affects the efficiency of high- and low-momentum transfer. We computationally examine the effects of free electron degeneracy and find that it does not influence ionization dynamics. Finally, we consider how a non-thermal electron distribution may modify our results. This investigation gives insight into the mechanisms and helps guide future experiments that utilize intense x-ray pulses to achieve high-resolution structural determination.

Structural dynamics research requires robust computational methods, reliable software, accessible data, and scalable infrastructure. Managing these components is complex and directly affects reproducibility and efficiency. The SBGrid Consortium addresses these challenges through a three-pillar approach that encompasses Software, Data, and Infrastructure, designed to foster a consistent and rigorous computational environment. At the core is the SBGrid software collection (>620 curated applications), supported by the Capsules Software Execution Environment, which ensures conflict-free, version-controlled execution. The SBGrid Data Bank supports open science by enabling the publication of primary experimental data. SBCloud, a fully managed cloud computing platform, provides scalable, on-demand infrastructure optimized for structural biology workloads. Together, they reduce computational friction, enabling researchers to focus on interpreting time-resolved data, modeling structural transitions, and managing large simulation datasets for advancing structural dynamics. This integrated platform delivers a reliable and accessible foundation for computationally intensive research across diverse scientific fields sharing common computational methods.

In October 2024, the Challenges in Structural Biology Summit was held at the UCLA Lake Arrowhead Lodge. The meeting focused on new advancements and methods developments in structural biology. Here, we briefly summarize the 2024 Challenges in Structural Biology Summit.

The insulating chiral magnet Cu₂OSeO₃ exhibits a rich array of low-temperature magnetic phenomena, making it a prime candidate for the study of its spin dynamics. Using spin wave small-angle neutron scattering (SWSANS), we systematically investigated the temperature-dependent behavior of the helimagnon excitations in the field-polarized phase of Cu₂OSeO₃. Our measurements, spanning 5-55 K, reveal the temperature evolution of spin-wave stiffness and damping constant with unprecedented resolution, facilitated by the insulating nature of Cu₂OSeO₃. These findings align with theoretical predictions and resolve discrepancies observed in previous studies, emphasizing the enhanced sensitivity of the SWSANS method. The results provide deeper insights into the fundamental magnetic properties of Cu₂OSeO₃, contributing to a broader understanding of chiral magnets.

The energetics and dynamics of ion assembly in solution has broad influence in nanomaterials and inorganic synthesis. To investigate the fundamental processes involved, we present a time-resolved x-ray solution scattering (TR-XSS) study of the trinuclear silver and thallium complexes of the diplatinum ion PtPOP [Pt₂(H₂P₂O₅) ${}_4^{4-}$ ] in aqueous solution. These complexes, their structural properties, and their electronic structure are not well understood and afford a unique opportunity to study the metal-metal bond formation that influences molecular and material assembly in solution. We present model-independent analysis of the observed dynamics as well as an analysis incorporating time-resolved structural refinements of key bond lengths with <100 fs time resolution. We find that upon photoexcitation, the Pt atoms contract $\sim$ 0.25 Å toward the center of both the Ag- and the Tl-PtPOP complexes, as previously observed for the PtPOP anion. For the AgPtPOP system, an ultrafast Ag-Pt bond expansion of $\sim$ 0.2 Å is observed, whereas in contrast, the TlPtPOP system exhibits a Tl-Pt bond contraction of $\sim$ 0.3 Å upon photoexcitation. For both complexes, the change in electronic state leads to coherent ("wave-packet") oscillations along the metal-Pt coordinates. Based on these structural dynamics, we propose an electronic structure model that describes the metal-metal bonding behavior in both the ground and excited state for both complexes.

Time-resolved x-ray liquidography (TRXL) is a powerful technique for directly tracking ultrafast structural dynamics in real space. However, resolving the motion of vibrational wavepackets generated by femtosecond laser pulses remains challenging due to the limited temporal resolution and signal-to-noise ratio (SNR) of experimental data. This study addresses these challenges by introducing singular spectrum analysis (SSA) as an efficient method for extracting oscillatory signals associated with vibrational wavepackets from TRXL data. To evaluate its performance, we conducted a comparative study using simulated TRXL data, demonstrating that SSA outperforms conventional analysis methods such as the Fourier transform of temporal profiles and singular value decomposition, particularly under low SNR conditions. We further applied SSA to experimental TRXL data on the photodissociation of triiodide ( $I_3^{-}$ ) in methanol, successfully isolating oscillatory signals arising from wavepacket dynamics in ground-state $I_3^{-}$ and excited-state $I_2^{-}$ , which had been challenging to resolve in previous TRXL studies. These results establish SSA as a highly effective tool for analyzing ultrafast structural dynamics in time-resolved experiments and open new opportunities for studying wavepacket dynamics in a wide range of photoinduced reactions.

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

A fundamental challenge for specialists in any field is communicating the importance and intricacies of their work to those outside of it. The 2024 Transactions Symposium held at the 74th annual meeting of the American Crystallographic Association: Structural Science Society was designed to address two pivotal themes concerning the promotion and understanding of structural science: first, pedagogical approaches of teaching structural science, emphasizing the methodologies that enhance student learning and second, strategies to capture the interest of non-specialists and the general public. By reflecting on what makes experts passionate about their field and what they wish others understood about it, the symposium highlighted actionable insight into bridging gaps and fostering a broader appreciation for structural science.

The small reactive molecules, glyoxal (GO) and methylglyoxal (MGO), are common byproducts of metabolic processes. GO and MGO are known to modify proteins, DNA, and lipids, resulting in advance glycation end products (AGEs). AGEs are linked to numerous human diseases but are found across all three domains of life due to the widespread presence of GO and MGO. Recent structural studies have revealed that an antibacterial phospholipase toxin contains a methylglyoxal-derived imidazolium crosslink (MODIC). Unlike AGEs that are associated with human diseases and protein dysfunction, crosslinking is required for the toxin's enzymatic activity, indicating that MODIC acts as a bona fide post-translational modification to promote function. The MODIC-modified toxin represents the first structure in the protein data bank with an AGE-modification. However, because GO and MGO are present in all cells, AGE-modifications are likely more prevalent than currently reported but have gone undetected. We used the toxin's MODIC structural motif to query the protein data bank for other modified proteins. This search recovered the colicin Ia pore-forming toxin. Using the deposited crystal structure and structural data for colicin Ia, we were able to model glyoxal-derived imidazolium crosslink or MODIC modifications into the electron density map, suggesting that GO/MGO modifications may indeed be more common in bacterial proteins.