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At least 91 records · Page 5

Artificial intelligence methods for protein structure and interaction prediction: Recent advances and challenges

Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. Here, in this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.

Morehead, Alex [Lawrence Berkeley National Laborat↗

Structure of Complex Liquid–Liquid Extraction Organic Phases for Rare Earth Separations

Complex, multicomponent liquids with hierarchical structure and phase transitions are encountered in many natural and industrial processes, including in chemical separations. One notable example is aggregation and organic phase splitting in liquid–liquid extraction (LLE) of metal ions. While these two phenomena that have long been closely associated, a mechanistic link between mesoscale structure and the capacity-limiting organic phase splitting remains elusive due to complexity of these systems. Here, in this study, we combine small-angle X-ray scattering (SAXS), X-ray photon correlation spectroscopy (XPCS), and molecular dynamics simulation to reveal a comprehensive picture of structure at the nano- and mesoscale in these complex solutions. For the representative case of rare earth extraction from an acidic aqueous phase by a malonamide extractant in dodecane, we investigate a wide range of process-relevant extractant and acid concentrations to provide a complete picture of how aggregation depends on composition. We decompose organic phase structure from SAXS into two contributions, which together can capture the scattering at all compositions: composition fluctuations described by the Ornstein–Zernike equation at low wavenumber Q, and nanostructure modeled by a “pre-peak” at intermediate Q. The former contains information about the thermodynamics of demixing, while the latter reflects nanoscopic self-assembly of the extractant and extracted solutes. While fluctuations have typically not been considered in the literature, we find they in fact dominate the total structure for nearly all practical conditions. As only the fluctuations have a strong temperature response, we confirm this attribution with temperature-dependent SAXS measurements, including for extracted europium nitrate complexes. SAXS and XPCS measurements near the critical point find static and dynamic scaling consistent with theory. Overall, this new paradigm for understanding LLE organic phases connects composition, nanoscale, and mesoscale structuring to phase behavior, providing both a comprehensive picture of solution structure and a quantitative link between aggregation and third phase formation.

Peroutka, Allison A. [Argonne National Laboratory ↗

Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence

The 2016–2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the Mw 6.0 Amatrice event on 24 August 2016, followed by the Mw 5.9 Visso event on 26 October and the Mw 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of ~900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L’Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence.

58 GEOSCIENCES↗

Complex spin structure in co-trimer-chain Li 2 Co 3 Se 4 O 12

Complex magnetic materials are extremely attractive for revealing unconventional spin states and novel magnetic excitations. Here, we report the structural, thermodynamic, and magnetic properties of a novel magnetic material Li 2 Co 3 Se 4 O 12 based on x-ray and neutron diffraction, specific heat, magnetization, and x-ray photoelectron spectroscopy measurements. X-ray and neutron diffraction refinements reveal two Co sites Co (1) and Co (2) even though both are in the octahedral environment. While they are not connected along the b and c directions, these octahedra are edge-shared forming the Co (2) – Co (1) – Co (2) trimer chain along the a direction. The magnetic susceptibility exhibits the Curie-Weiss (CW) temperature dependence at high temperatures (above ∼50 K) with the negative CW temperature, a dip centered at T ⁎ ∼ 8.0 K, and an antiferromagnetic transition at T N = 3.3 K. The specific heat confirms that there is a phase transition at T N and a hump at T ⁎ . The long-range magnetic transition at T N implies that, in addition to the intra-chain interaction, there is strong inter-chain interaction, which is likely due to polarized SeO 3 bridging between chains. Single crystal neutron diffraction refinement reveals a complex magnetic structure with the angle between Co (1) and Co (2) moments ∼105°. Within the Co (2) – Co (1) – Co (2) trimer, two Co (2) moments are parallelly aligned. Surprisingly, the Co (1) moment (1.92μB) is only half of the Co (2) moment (3.96μB). There is likely the spin-state change for Co (1) from the high-spin state at T > T ⁎ to the low-spin state at T < T ⁎ , causing a dip in the magnetic susceptibility and a hump in the specific heat. When the magnetic field is applied, multiple metamagnetic transitions are found in all directions, implying field-driven magnetic excitations. Our results demonstrate rich magnetic properties of Li 2 Co 3 Se 4 O 12 that are sensitive to the external stimuli such as the magnetic field.

Antiferromagnetism↗

Complex magnetic structure in Ba 5 Ru 3 O 12 with isolated Ru 3 O 12 trimer

We report detailed magnetic, transport, heat-capacity, and neutron diffraction measurements of Ba 5 Ru 3 O 12 , a compound consisting of isolated Ru 3 O 12 trimers. We show that this system develops long-range antiferromagnetic ordering at T N ~ 60 K without structural distortion or metal-insulator-type transition, which is in sharp contrast to other Barium Ruthenate trimer systems such as 9R-BaRuO 3 and Ba 4 Ru 3 O 10 . A complex magnetic structure is revealed which is attributable to the magnetic frustration due to competing exchange interactions between Ru ions on different crystallographic sites within the Ru 3 O 12 trimer and different degree of orbital hybridization on different Ru-sites. In conclusion, we have also investigated the magnetic structure of Ba 4 Ru 3 O 10 for comparisons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

ELT Imaging of MWC 297 from the 23 m LBTI: Complex Disk Structure and a Companion Candidate

Herbig Ae/Be stars represent the early outcomes of star formation and the initial stages of planet formation at intermediate stellar masses. Understanding both of these processes requires detailed characterization of their disk structures and companion frequencies. We present new 3.7 μm imaging of the Herbig Be star MWC 297 from nonredundant masking observations on the phase-controlled, 23 m Large Binocular Telescope Interferometer. The images reveal complex disk structure on the scales of several au, as well as a companion candidate. We discuss physical interpretations for these features and demonstrate that the imaging results are independent of choices such as priors, regularization hyperparameters, and error-bar estimates. With an angular resolution of ∼17 mas, these data provide the first robust Extremely Large Telescope–resolution view of a distant young star.

47 OTHER INSTRUMENTATION↗

Printing noble metal alloy films with compositional gradient

Additive manufacturing has offered great promise in fabricating materials and devices with complex structures and unique properties. In addition to complex structures, additive manufacturing also allows for local changes to material compositions and/or structure, ideal for combinatorial material investigations. Besides, advantages over freedom of design, mass customization, waste minimization and the capability to fast prototyping, the on-demand control of compositional distributions in low dimensional materials remains challenging. In this work, we demonstrate a combinatorial aerosol jet printing technique to create noble metal films with gradient compositions. Nanoparticle sizes and ink chemistry are critical for aerosol jet printing and sintering process in order to achieve defect free thin films. Ruthenium (Ru) and platinum (Pt) nanoparticles with size <5 nm were synthesized and formulated into printable inks. Further, after thermal sintering at temperature as low as 400 °C, printed nanoparticles formed highly reflective films free from microstructural defect. Alloy films of gradient compositions were realized via in-situ mixing Ru and Pt ink streams and varying the mixing ratio during the printing process. This combinatorial printing method provides great potential to rapidly transform nanoparticle inks into new materials compositions and structures unobtainable via conventional methods for high-throughput materials studies.

36 MATERIALS SCIENCE↗

Equivariant Graph Attention Network - 3D Conformers & Feature Fusion

EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.

Shim, Heesung [Lawrence Livermore National Laborat↗

Reduced dynamic complexity allows structure elucidation of an excited state of KRAS G13D

Localized dynamics of RAS, including regions distal to the nucleotide-binding site, is of high interest for elucidating the mechanisms by which RAS proteins interact with effectors and regulators and for designing inhibitors. Among several oncogenic mutants, methyl relaxation dispersion experiments reveal highly synchronized conformational dynamics in the active (GMPPNP-bound) KRAS G13D , which suggests an exchange between two conformational states in solution. Methyl and 31 P NMR spectra of active KRAS G13D in solution confirm a two-state ensemble interconverting on the millisecond timescale, with a major Pγ atom peak corresponding to the dominant State 1 conformation and a secondary peak indicating an intermediate state different from the known State 2 conformation recognized by RAS effectors. High-resolution crystal structures of active KRAS G13D and KRAS G13D -RAF1 RBD complex provide snapshots of the State 1 and 2 conformations, respectively. We use residual dipolar couplings to solve and cross-validate the structure of the intermediate state of active KRAS G13D , showing a conformation distinct from those of States 1 and 2 outside the known flexible switch regions. The dynamic coupling between the conformational exchange in the effector lobe and the breathing motion in the allosteric lobe is further validated by a secondary mutation in the allosteric lobe, which affects the conformational population equilibrium.

59 BASIC BIOLOGICAL SCIENCES↗

Simultaneous suppression of lignin, tricin and wall‐bound phenolic biosynthesis via the expression of monolignol 4‐ O ‐methyltransferases in rice

Summary Grass lignocelluloses feature complex compositions and structures. In addition to the presence of conventional lignin units from monolignols, acylated monolignols and flavonoid tricin also incorporate into lignin polymer; moreover, hydroxycinnamates, particularly ferulate, cross‐link arabinoxylan chains with each other and/or with lignin polymers. These structural complexities make grass lignocellulosics difficult to optimize for effective agro‐industrial applications. In the present study, we assess the applications of two engineered monolignol 4‐ O ‐methyltransferases (MOMTs) in modifying rice lignocellulosic properties. Two MOMTs confer regiospecific para ‐methylation of monolignols but with different catalytic preferences. The expression of MOMTs in rice resulted in differential but drastic suppression of lignin deposition, showing more than 50% decrease in guaiacyl lignin and up to an 90% reduction in syringyl lignin in transgenic lines. Moreover, the levels of arabinoxylan‐bound ferulate were reduced by up to 50%, and the levels of tricin in lignin fraction were also substantially reduced. Concomitantly, up to 11 μmol/g of the methanol‐extractable 4‐ O ‐methylated ferulic acid and 5–7 μmol/g 4‐ O ‐methylated sinapic acid were accumulated in MOMT transgenic lines. Both MOMTs in vitro displayed discernible substrate promiscuity towards a range of phenolics in addition to the dominant substrate monolignols, which partially explains their broad effects on grass phenolic biosynthesis. The cell wall structural and compositional changes resulted in up to 30% increase in saccharification yield of the de‐starched rice straw biomass after diluted acid‐pretreatment. These results demonstrate an effective strategy to tailor complex grass cell walls to generate improved cellulosic feedstocks for the fermentable sugar‐based production of biofuel and bio‐chemicals.

4-O-methylated↗

Data for Simultaneous Suppression of Lignin, Tricin and Wall-Bound Phenolic Biosynthesis via the Expression of Monolignol 4- O -Methyltransferases in Rice

Grass lignocelluloses feature complex compositions and structures. In addition to the presence of conventional lignin units from monolignols, acylated monolignols and flavonoid tricin also incorporate into lignin polymer; moreover, hydroxycinnamates, particularly ferulate, cross-link arabinoxylan chains with each other and/or with lignin polymers. These structural complexities make grass lignocellulosics difficult to optimize for effective agro-industrial applications. In the present study, we assess the applications of two engineered monolignol 4- O -methyltransferases (MOMTs) in modifying rice lignocellulosic properties. Two MOMTs confer regiospecific para-methylation of monolignols but with different catalytic preferences. The expression of MOMTs in rice resulted in differential but drastic suppression of lignin deposition, showing more than 50% decrease in guaiacyl lignin and up to an 90% reduction in syringyl lignin in transgenic lines. Moreover, the levels of arabinoxylan-bound ferulate were reduced by up to 50%, and the levels of tricin in lignin fraction were also substantially reduced. Concomitantly, up to 11 μmol/g of the methanol-extractable 4- O -methylated ferulic acid and 5–7 μmol/g 4- O -methylated sinapic acid were accumulated in MOMT transgenic lines. Both MOMTs in vitro displayed discernible substrate promiscuity towards a range of phenolics in addition to the dominant substrate monolignols, which partially explains their broad effects on grass phenolic biosynthesis. The cell wall structural and compositional changes resulted in up to 30% increase in saccharification yield of the de-starched rice straw biomass after diluted acid-pretreatment. These results demonstrate an effective strategy to tailor complex grass cell walls to generate improved cellulosic feedstocks for the fermentable sugar-based production of biofuel and bio-chemicals.

Biomass Analytics↗

Disturbance-accelerated succession increases the production of a temperate forest

Many secondary deciduous forests of eastern North America are approaching a transition in which maturing early successional tree species are declining, resulting in an uncertain future for these forests’ century-long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem-girdling induced mortality of >6,700 early successional Populus spp. (aspen) and Betula papyrifera (paper birch). Since 2008, meteorological flux tower-based C cycling observations from the 33-ha treatment forest have been paired with those from a nearby unmanipulated forest. Following over a decade of observations, herein we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid-late successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather the rapid 1-year recovery of total LAI as mid-late successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid-late successional species dominance improved carbon-use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid-late successional species in the treatment forest, suggest changes in aboveground plant respiration and growth, respectively, were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come.

54 ENVIRONMENTAL SCIENCES↗

Quantifying rupture characteristics of microearthquakes in the Parkfield Area using a high-resolution borehole network

It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the M w 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions.

58 GEOSCIENCES↗

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗