Accurate Structural Models Research Articles

Ionisation of atoms determined by kappa refinement against 3D electron diffraction data.

Conventional refinement strategies used for three-dimensional electron diffraction (3D ED) data disregard the bonding effects between the atoms in a molecule by assuming a pure spherical model called the Independent Atom model (IAM) and may lead toan inaccurate or biased structure. Here we show that it is possible to perform a refinement going beyond theIAM with electron diffraction data. We perform kappa refinement which models charge transfers between atoms while assuming a spherical model. We demonstrate the procedure by analysing five inorganic samples; quartz, natrolite, borane, lutecium aluminium garnet, and caesium lead bromide. Implementation of kappa refinement improved the structure model obtained over conventional IAM refinements and provided information on the ionisation of atoms. The results were validated against periodic DFT calculations. The work presents an extension of the conventional refinement of 3D ED data for a more accurate structure model which enables charge density information to be extracted.

DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

RNA language models predict mutations that improve RNA function.

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data1. Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function2-4. We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB)5. GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

Design of nonlinear gradient sheet-based TPMS-lattice using artificial neural networks

Gradient triply periodic minimal surface (TPMS) structures are renowned for lightweight design and enhanced performance, but their complex and nonlinear configurations pose challenges in achieving targeted design goals. A new design methodology for the nonlinear gradient structure was proposed in this study, with the aim of achieving efficient and accurate modeling of complex and gradient sheet-based TPMS structures under specific performance objectives. This method utilized automated finite element (FE) simulations to obtain structure topology element densities under various boundary conditions. An artificial neural network (ANN) was then employed to efficiently predict the correspondence between these boundary conditions and topology element densities. A mapping was established between topology element densities and TPMS structural parameters, and the gradient structure was accurately constructed by using the voxel modeling technique. Taking a typical cantilever beam TPMS structure as an example of nonlinear gradient design, the results indicate that the error between the ANN-predicted and FE-simulated structure topology element densities is only 2.73 %, with prediction time being only 0.15 % of the simulation time. The thin regions of the gradient structure align with those geometrically removed in regular topology optimization scheme, achieving up to 65.45 % weight reduction, a 28.72 % improvement over the regular scheme, along with uniform structural stress transition and maximum stress reduction. TC4 alloy nonlinear gradient TPMS structures, printed by metal selective laser melting (SLM) technique, confirm the practical application value of this design method.

3D winding path modeling method with fiber overlap effect

The fibers will repeatedly cross and stack on the liner during the continuous winding process, which poses a challenge in creating accurate local and global structure models. To address the issue, a 3D winding path modeling method is proposed to simulate the fiber overlap effect. First, the initial path is offset equidistantly along the direction of width and thickness to construct the 3D path points. Then, by calculating the overlap thickness, the overlapped fibers are raised to the corresponding overlap thickness to eliminate interference in the 3D path points. Finally, a 3D winding path with the fiber overlap effect is obtained by solving the suspended points before and after the overlapping area to describe the behavior of fibers separating from the liner surface under tension. The proposed method is applied to various winding patterns and liner shapes, and the results show that the established 3D winding path can accurately simulate the fiber overlap effect and correspond with the actual local and global winding structures.

OpenNucleome for high-resolution nuclear structural and dynamical modeling.

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome-an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of 'fixed points' within the nucleus-signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

OpenNucleome for high-resolution nuclear structural and dynamical modeling

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome—an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of ‘fixed points’ within the nucleus—signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNAand Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.

Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that serve as model systems for biophysical techniques. Optogenetics utilizes genetic engineering to insert specialized proteins into specific neurons or brain regions, allowing for manipulation of their activity through light and enabling the mapping and control of specific brain areas in living organisms. The obstacle of optogenetics lies in the fact that light has a limited ability to penetrate biological tissues, particularly blue light in the visible spectrum. Despite this challenge, most optogenetic systems rely on blue light due to the scarcity of red-shifted opsins. Finding additional red-shifted rhodopsins would represent a major breakthrough in overcoming the challenge of limited light penetration in optogenetics. However, determining the wavelength absorption maxima for rhodopsins based on their protein sequence is a significant hurdle. Current experimental methods are time-consuming, while computational methods lack accuracy. The paper introduces a new computational approach called RhoMax that utilizes structure-based geometric deep learning to predict the absorption wavelength of rhodopsins solely based on their sequences. The method takes advantage of AlphaFold2 for accurate modeling of rhodopsin structures. Once trained on a balanced train set, RhoMax rapidly and precisely predicted the maximum absorption wavelength of more than half of the sequences in our test set with an accuracy of 0.03 eV. By leveraging computational methods for absorption maxima determination, we can drastically reduce the time needed for designing new red-shifted microbial rhodopsins, thereby facilitating advances in the field of optogenetics.

Molecular dynamics simulations shed light into the donor substrate specificity of vertebrate poly-alpha-2,8-sialyltransferases ST8Sia IV

BackgroundSialic acids are essential monosaccharides influencing several biological processes and disease states. The sialyltransferases catalyze the transfer of Sia residues to glycoconjugates playing critical roles in cellular recognition and signaling. Despite their importance, the molecular mechanisms underlying their substrate specificity, especially between different organisms, remain poorly understood. Recently, the human ST8Sia IV, a key enzyme in the synthesis of polysialic acids, was found to accept only CMP-Neu5Ac as a sugar-donor, whereas the whitefish Coregonus maraena enzyme showed a wider donor substrate specificity, accepting CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn. However, what causes these differences in donor substrate specificity is unknown. MethodsComputational approaches were used to investigate the structural and biochemical determinants of the donor substrate specificity in ST8Sia IV. Accurate structural models of the human and fish ST8Sia IV catalytic domains and their complexes with three sialic acid donors (CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn) were generated. Subsequently, molecular dynamics simulations were conducted to analyze the stability and interactions within these complexes and identify differences in complex stability and substrate binding sites between the two ST8Sia IV. ResultsOur MD simulations revealed that the human enzyme effectively stabilizes CMP-Neu5Ac, whereas CMP-Neu5Gc and CMP-Kdn are unstable and explore different conformations. In contrast, the fish ST8Sia IV stabilizes all three donor substrates. Based on these data, we identified the key interacting residues for the different Sias parts of the substrate donors. General significanceThis work advances our knowledge of the enzymatic mechanisms governing sialic acid transfer, shedding light on the evolutionary adaptations of sialyltransferases.

APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

Sparks of function by de novo protein design.

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Specification and formation process of enriched portions in Au veins in an epithermal deposit via clustering and geostatistical approaches

Even though epithermal vein-type deposits are the main sources of Au and Ag worldwide, accurate three-dimensional modeling of vein structures and the extraction of high-grade zones are still difficult because of geologic structure heterogeneities and the scarcity of available data, mostly obtained via drilling. Accordingly, by selecting a low-sulfidation epithermal Au–Ag deposit in the Philippine archipelago, we develop an efficient and reliable workflow for determining the spatial distribution of Au-rich veins in detail and the transport of hydrothermal fluids. The studied deposit is mostly hosted in open fractures and breccias with a relatively large tonnage of low Au+Ag grades and/or a small tonnage of high Au+Ag grades. The whole-rock geochemical data (1877 core samples) and lithological data (56 drilling sites) are processed using hierarchical clustering analysis (HCA) and two geostatistical simulations: turning bands simulation for the Au grade distribution and interpretation of mineralization process and truncated Gaussian simulation for the spatial distribution of clusters with similar Au–Ag grade features as classified by HCA. The general correlation between the Au and Ag grades, with a linear correlation coefficient of ≈ 0.69, is based on the HCA application, and the clusters were likely formed by three-stage mineralization processes. A plausible three-dimensional geologic model is then constructed, delineating in detail hydrothermally mineralized and altered zones with Au enrichment and vein system structures in the study area.

Chemical Structure Characteristics and Model Construction of Coal with Three Kinds of Coalification Degrees.

The coal structure is extensively used for studying the properties of coal, and the construction of accurate and reliable coal structure models can promote these researches. In this study, Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) were used to analyze the changes in the coal structure as a function of the coalification degree, and a semiquantitative model construction method based on FTIR, XRD, and X-ray photoelectron spectroscopy (XPS) analyses was proposed. With an increase in the coalification degree, the size of the aromatic cores in the coal increased. During the conversion from lignite to bituminous coal, the content of aliphatic structures increased, while the content of oxygen-containing functional groups (OFGs) significantly decreased. Conversely, during the conversion from bituminous coal to anthracite, the content of aliphatic structures decreased while the content of OFGs slightly increased. The calculated FTIR spectra of the coal structure models were consistent with the experimental FTIR spectra, which confirmed the accuracy of the models. Furthermore, the models successfully explained the microscopic mechanism underlying the differences in the wettability of the coal samples with varying coalification degrees. The construction method and coal structure models in this study will be more widely applied in future research.

Accurate modeling of peptide-MHC structures with AlphaFold

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for Tcell surveillance. Reliable in silico prediction of which peptides would be presented and which Tcell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.

Bioinspired Polyoxo-titanium Cluster for Greatly Enhanced Solar-Driven CO2 Reduction.

Developing artificial enzymes with excellent catalytic activities and uncovering the structural and chemical determinants remain a grand challenge. Discrete titanium-oxo clusters with well-defined coordination environments at the atomic level can mimic the pivotal catalytic center of natural enzymes and optimize the charge-transfer kinetics. Herein, we report the precise structural tailoring of a self-assembled tetrahedral Ti4Mn3-cluster for photocatalytic CO2 reduction and realize the selective evolution of CO over specific sites. Experiments and theoretical simulation demonstrate that the high catalytic performance of the Ti4Mn3-cluster should be related to the synergy between active Mn sites and the surrounding functional microenvironment. The reduced energy barrier of the CO2 photoreduction reaction and moderate adsorption strength of CO* are beneficial for the high selective evolution of CO. This work provides a molecular scale accurate structural model to give insight into artificial enzyme for CO2 photoreduction.

Analytical Methods to Develop Accurate Structural Model for the Asmari Reservoir

Crossing of Asmari can be a challenging endeavor in certain instances, particularly when dealing with structural complexities, compounded by the presence of a substantial layer of Gachsaran formation evaporates overlying the reservoir. The primary aim of this study was to establish a precise and comprehensive structural model for the Asmari reservoir. Utilizing geological logs for dip classification offers the advantage of directly depicting the structural origin. This approach helps in identifying the Asmari fault and fracture systems and their impact on production, ultimately resolving structural complexities. To investigate the reasons behind the intersection of the Kalhur member and the unexpected increase in the thickness of the Asmari formation, FMI data was acquired over the interval ranging from 1550m to 2065m. The analysis of picked bedding dips revealed abrupt variations in dip magnitude and azimuth reversals. These observations were pivotal in unraveling the structural intricacies of the reservoir. A significant fault was identified within zone five of the Kalhur member, and its interpretation suggests that it is a reverse fault. This conclusion is based on the observed dip pattern and the distinctive characteristics of the logs. Around the fault, the beds and layers exhibit elevated dips, largely attributed to the plastic nature of anhydrite and marly/shaly anhydrite within the formation. The anhydrite-indicator curve obtained from the FMI and gamma-ray logs provides further evidence that the well entered the Kalhur member after intersecting the major fault located within this particular zone. The interpretation of structural dip played a pivotal role in resolving structural complexities, leading to the precise determination of the well's location within the Asmari reservoir. This achievement was particularly critical as it enabled the well to reach the lower contact of the Asmari formation. This interpretation was facilitated by analyzing FMI images and petrophysical logs in well LL-26.

Characterization of the genesis and spatial variability in tectonic fractures in the Gaosong ore field (South China).

This study examines fractures in the Gaosong ore field to determine the main factors affecting the spatial variability in the fracture structure. The attributes of fractures, including the fracture orientation, intensity and intersection density, in the Wuzishan anticline and near the Lotus mountain fault in the Gaosong ore field in the GeJiu ore district were extracted by using a modified circular scanning line method. The fracture intensity and intersection density were analyzed based on the semivariance geostatistics function by using the volume of variation and the amount of relative variability. These parameters quantitatively describe the spatial variability in the fracture structure. The mean and standard variance of fracture intensity and intersection density in the ore field decrease with distance from the Lotus mountain fault, while the coefficient of variation increases. The spatial anisotropy is closely related to the axial direction of the Wuzishan anticline and the orientation of the Lotus mountain fault. The main factors affecting the spatial variability in the fault structure can be determined with the semivariance geostatistics function, and the results are useful for studying the geology of the mining area and can help to construct an accurate structural model to serve the needs of mine production.