Model Of Complex Research Articles

Phosphorylation of the HMGN1 Nucleosome Binding Domain Decreases Helicity and Interactions with the Acidic Patch.

Intrinsically disordered proteins are abundant in the nucleus and are prime sites for posttranslational modifications that modulate transcriptional regulation. Lacking a defined three-dimensional structure, intrinsically disordered proteins populate an ensemble of several conformational states, which are dynamic and often altered by posttranslational modifications, or by binding to interaction partners. Although there is growing appreciation for the role that intrinsically disordered regions have in regulating protein-protein interactions, we still have a poor understanding of how to determine conformational population shifts, their causes under various conditions, and how to represent and model conformational ensembles. Here, we study the effects of serine phosphorylation in the nucleosome-binding domain of an intrinsically disordered protein - HMGN1 - using NMR spectroscopy, circular dichroism and modelling of protein complexes. We show that phosphorylation induces local conformational changes in the peptide backbone and decreases the helical propensity of the nucleosome binding domain. Modelling studies using AlphaFold3 suggest that phosphorylation disrupts the interface between HMGN1 and the nucleosome acidic patch, but that the models over-predict helicity in comparison to experimental data. These studies help us to build a picture of how posttranslational modifications might shift the conformational populations of disordered regions, alter access to histones, and regulate chromatin compaction.

Parameterization of a Fluctuating Charge Model for Complexes Containing 3d Transition Metals.

Metalloproteins widely exist in biology, playing pivotal roles in diverse life processes. Meanwhile, molecular dynamics (MD) simulations based on classical force fields has emerged as an important tool in scientific research. Partial charges are critical parameters within classical force fields and usually derived from quantum mechanical (QM) calculations. However, QM calculations are often time-consuming and prone to basis set dependence. Alternatively, fluctuating charge (FQ) models offer another avenue for partial charge derivation, which has significant speed advantages and can be used for large-scale screening. Building upon our previous work, which introduced an FQ model for zinc-containing complexes, herein we extend this model to include additional 3d transition metals which are important to the life sciences, namely chromium, manganese, iron, cobalt, and nickel. Employing CM5 charges as target for parametrization, our FQ model accurately reproduces partial charges for 3d metal complexes featuring biologically relevant ligands. Furthermore, by using atomic charges derived by our FQ model, MD simulations have been performed. These charges showed excellent performance in simulating proteomic metal sites housing multiple metal ions, specifically, a metalloprotein containing an iron-sulfur cluster and another containing a dimanganese metal site, showcasing comparable performance to those of RESP charges. We anticipate that our study can accelerate the parametrization of atomic charges for metalloproteins featuring 3d transition metals, thereby facilitating simulations of relevant systems.

Thermostatted Kinetic Theory Structures in Biophysics: Generalizations and Perspectives

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics.

Disease models of Leigh syndrome: From yeast to organoids.

Leigh syndrome (LS) is a severe mitochondrial disease that results from mutations in the nuclear or mitochondrial DNA that impairs cellular respiration and ATP production. Mutations in more than 100 genes have been demonstrated to cause LS. The disease most commonly affects brain development and function, resulting in cognitive and motor impairment. The underlying pathogenesis is challenging to ascertain due to the diverse range of symptoms exhibited by affected individuals and the variability in prognosis. To understand the disease mechanisms of different LS-causing mutations and to find a suitable treatment, several different model systems have been developed over the last 30 years. This review summarizes the established disease models of LS and their key findings. Smaller organisms such as yeast have been used to study the biochemical properties of causative mutations. Drosophila melanogaster, Danio rerio, and Caenorhabditis elegans have been used to dissect the pathophysiology of the neurological and motor symptoms of LS. Mammalian models, including the widely used Ndufs4 knockout mouse model of complex I deficiency, have been used to study the developmental, cognitive, and motor functions associated with the disease. Finally, cellular models of LS range from immortalized cell lines and trans-mitochondrial cybrids to more recent model systems such as patient-derived induced pluripotent stem cells (iPSCs). In particular, iPSCs now allow studying the effects of LS mutations in specialized human cells, including neurons, cardiomyocytes, and even three-dimensional organoids. These latter models open the possibility of developing high-throughput drug screens and personalized treatments based on defined disease characteristics captured in the context of a defined cell type. By analyzing all these different model systems, this review aims to provide an overview of past and present means to elucidate the complex pathology of LS. We conclude that each approach is valid for answering specific research questions regarding LS, and that their complementary use could be instrumental in finding treatment solutions for this severe and currently untreatable disease.

Structural analysis of microtubule binding by minus-end targeting protein Spiral2

Microtubules (MTs) are dynamic cytoskeletal polymers that play a critical role in determining cell polarity and shape. In plant cells, acentrosomal MTs are localized on the cell surface and are referred to as cortical MTs. Cortical MTs nucleate in the cell cortex and detach from nucleation sites. The released MT filaments perform treadmilling, with the plus-ends of MTs polymerizing and the minus-ends depolymerizing. Minus-end targeting proteins, -TIPs, include Spiral2, which regulates the minus-end dynamics of acentrosomal MTs. Spiral2 accumulates autonomously at MT minus-ends and inhibits filament shrinkage, but the mechanism by which Spiral2 specifically recognizes minus-ends of MTs remains unknown. Here we describe the crystal structure of Spiral2's N-terminal MT-binding domain. The structural properties of this domain resemble those of the HEAT repeat structure of the tumor overexpressed gene (TOG) domain, but the number of HEAT repeats is different and the conformation is highly arched. Gel filtration and co-sedimentation analyses demonstrate that the domain binds preferentially to MT filaments rather than the tubulin dimer, and that the tubulin-binding mode of Spiral2 via the basic surface is similar to that of the TOG domain. We constructed an in silico model of the Spiral2-tubulin complex to identify residues that potentially recognize tubulin. Mutational analysis revealed that the key residues inferred in the model are involved in microtubule recognition, and provide insight into the mechanism by which end-targeting proteins stabilize MT ends.

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5′ ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

Molecular basis of global promoter sensing and nucleosome capture by the SWR1 chromatin remodeler.

The SWR1 chromatin remodeling complex is recruited to+1 nucleosomes downstream of transcription start sites of eukaryotic promoters, where it exchanges histone H2A for the specialized variant H2A.Z. Here, we use cryoelectron microscopy (cryo-EM) to resolve the structural basis of the SWR1 interaction with free DNA, revealing a distinct open conformation of the Swr1 ATPase that enables sliding from accessible DNA to nucleosomes. A complete structural model of the SWR1-nucleosome complex illustrates critical roles for Swc2 and Swc3 subunits in oriented nucleosome engagement by SWR1. Moreover, an extended DNA-binding α helix within the Swc3 subunit enables sensing of nucleosome linker length and is essential for SWR1-promoter-specific recruitment and activity. The previously unresolved N-SWR1 subcomplex forms a flexible extended structure, enabling multivalent recognition of acetylated histone tails by reader domains to further direct SWR1 toward the+1 nucleosome. Altogether, our findings provide a generalizable mechanism for promoter-specific targeting of chromatin and transcription complexes.

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking.

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking. In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody-antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository.

A hydration model of actinide complexes by exploring their multiple minima hypersurface: The case of uranyl cation

The multiple minima hypersurface (MMH) approach is a reliable theoretical model for characterizing uranyl-water complexes in aqueous media. It provides insights into probable conformations, solubility, and reactivity through accurate exploration of the local energy landscape via quantum calculations. The approach predicts equilibria and populations of local minima, identifying pre-reactive sites and hydrolyzed ligands. By applying MMH to a wider range of molecular species, a robust theoretical framework is established for understanding the complex chemistry of environmental and nuclear materials. The approach here aims to predict the behavior of weathered uranium mining waste and enhance the study of actinide compounds.

Fast and Robust Modeling of Lanthanide and Actinide Complexes, Biomolecules, and Molecular Crystals with the Extended GFN-FF Model.

Lanthanides (Ln) and actinides (An) have recently become important tools in biomedical and materials science. However, the development of computational methods able to describe such elements in various environments has not kept up with the pace of the field. Addressing this challenge, this work introduces and showcases an extension of the GFN-FF to An alongside a reparameterization for Ln. This development fills a gap for fast computational methods that are out-of-the-box applicable to large f-element-containing systems with thousands of atoms. We discuss the reparameterization of the charge model and the covalent topology setup and showcase the model through various applications: Molecular dynamics simulations, optimization of Ln-containing biomolecules, and optimizations of several periodic structures. With the presented improvements, GFN-FF is a powerful method that routinely delivers robust and accurate geometries for large Ln/An systems with thousands of atoms.

Modification of a Flexible Reinforcement Model for Root-Soil Complexes Based on the Growth Period

Modification of a Flexible Reinforcement Model for Root-Soil Complexes Based on the Growth Period

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Comparison of the Effects of Different Palatal Morphology on Maxillary Expansion via RME and MSE: A Finite Element Analysis.

This study aims to compare and analyze the biomechanical effect and the displacement trend of RME and MSE on the maxillofacial complex under different palatal shapes by using finite element analysis. The three-dimensional model of maxillofacial complex was obtained from a computed tomography image of a person with a normal palate. Then, we modified the shape of the palate to obtain the model with a high palate. Additionally, two expander devices were considered. MSE and RME were created and four models were made: Model 1: Normal-palate craniomaxillofacial complex with RME expander; Model 2: Normal-palate craniomaxillofacial complex with MSE expander; Model 3: High-palate craniomaxillofacial complex with RME expander; Model 4: High-palate craniomaxillofacial complex with MSE expander. Then, lateral forced displacement was applied and the analysis results were obtained. The lateral displacement of the palatal suture of Model 3 is greater than that of Model 1, and the maxilla has more rotation. The crown/root ratio of Model 1 is significantly greater than that of the other three groups. Compared with Model 1, Model 3 has greater stress concentration in the superstructure of the craniomaxillofacial complex. Both of them have greater stress in the anchorage area than Model 2 and Model 4. Different shapes of the palate interfere with the effects of RME and MSE, and its influence on the stress distribution and displacement of the craniomaxillary complex when using RME is greater than MSE. The lateral displacement of the palatal suture of MSE is significantly larger than that of RME. It is more prone to tipping movement of the anchor teeth using RME under normal palate, and MSE may manage the vertical control better due to the smaller crown/root ratio than RME and intrusive movement of molars.

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.

Human cell surface-AAV interactomes identify LRP6 as blood-brain barrier transcytosis receptor and immune cytokine IL3 as AAV9 binder

Adeno-associated viruses (AAVs) are foundational gene delivery tools for basic science and clinical therapeutics. However, lack of mechanistic insight, especially for engineered vectors created by directed evolution, can hamper their application. Here, we adapt an unbiased human cell microarray platform to determine the extracellular and cell surface interactomes of natural and engineered AAVs. We identify a naturally-evolved and serotype-specific interaction between the AAV9 capsid and human interleukin 3 (IL3), with possible roles in host immune modulation, as well as lab-evolved low-density lipoprotein receptor-related protein 6 (LRP6) interactions specific to engineered capsids with enhanced blood-brain barrier crossing in non-human primates after intravenous administration. The unbiased cell microarray screening approach also allows us to identify off-target tissue binding interactions of engineered brain-enriched AAV capsids that may inform vectors’ peripheral organ tropism and side effects. Our cryo-electron tomography and AlphaFold modeling of capsid-interactor complexes reveal LRP6 and IL3 binding sites. These results allow confident application of engineered AAVs in diverse organisms and unlock future target-informed engineering of improved viral and non-viral vectors for non-invasive therapeutic delivery to the brain.

Implementation of a Highly Mobile UAVs Direction Finding Complex Using Virtual Magnetic Dipoles

Relevance. In the context of the development of modern broadband communication systems, the task of performing direction finding of signals using highly mobile direction finding systems (UAVs) is becoming especially urgent. They impose restrictions on the size of antenna elements and the distance between them, which leads to an increase in the mathematical error of the bearing. Problem statement. The paper sets the task of considering the possibility of increasing the direction finding accuracy of a highly mobile complex by using virtual magnetic dipole technology. A feature of this method is the minimization of distortions introduced by the carrier body into the characteristics of the measured field. To measure the field characteristics, as well as their partial components, vector antenna elements were used. Goal of the work is to study the characteristics of a radio direction finding complex using virtual magnetic dipole methods in conditions of distortions introduced by the carrier body. As an example, cases of direction finding of differently polarized waves, taking into account the influence of radomes of antenna elements, assessment of the maximum resolution accuracy, as well as noise stability are considered. The finite element method implemented in DS CST Studio Suite 2024 was used in the modeling. Result. During the research, a plane wave with different bearings fell on a UAV with a direction finding complex, which made it possible to carry out the most accurate research. The results of the work also include the creation of a model of a radio direction finding complex, which can be installed on a small UAV, providing high accuracy of wave direction finding in passive mode.The novelty of the method used lies in the application of methods for the formation of virtual magnetic dipoles based on the measured characteristics of the distorted electric field. New to this work are modeling cases that are associated with noise suppression, the influence of antenna element housings, as well as assessment of the maximum resolution accuracy.Practical significance of the work lies in the creation of a model of a radio location system based on vector antenna elements, which are used to measure the characteristics of the electromagnetic field with subsequent direction finding based on the magnetic field.

Bayesian frameworks for integrating petrologic and geochronologic data

Constraining the absolute time and duration of geologic processes is one of the great challenges and goals in Earth sciences. Increasingly, the integration of geochronologic constraints with petrologic information is being qualitatively applied to understanding the timescales of metamorphic, igneous, tectonic, and fluid-related processes. Many rocks and geochronometers preserve relative age constraints such as compositional zoning or cross cutting relationships. This prior information can be formalized in a Bayesian statistical framework to generate a probabilistic posterior chronology. As we show here, these “age-sequence” models can enhance precision on geochronologic dates and rates and insight into tectonic models. Bayesian modeling of complex, concentrically, zoned monazite from the northern Appalachian orogen was used to develop a detailed temperature-time history through the Acadian (∼405–395 Ma) and Neoacadian (∼380–350 Ma) orogenies with significantly reduced uncertainties (40–70 %). Modeling of zoned monazite from a southern Trans-Hudson orogen granulite yielded durations of 0.5+9/-0.4 Ma and 20+5/-8 Ma for biotite-dehydration melting and suprasolidus conditions, respectively. The relatively short intervals of heating and peak conditions are consistent with a back-arc tectonic setting. A complementary approach, Bayesian change point detection, provides a framework to constrain the timing of compositional changes that can be linked with metamorphic reactions. Applying this approach in the northern Appalachian orogen demonstrates contrasting durations of low-Y monazite crystallization (∼10 vs ∼30 myr) in regions with different pressure-temperature histories. Compositionally distinct monazite domains can be linked with garnet stability, which provides a key constraint on tectonic models. Bayesian statistical analysis represent a powerful tool that can be widely applied to refine the absolute time and duration of geologic processes. A more objective and reproducible set of interpretations are produced by this more formal, although not necessarily complex, statistical analysis.

Establishing a user demand hierarchy model driven by a mental model for complex operating systems

In complex human-computer interactions, issues such as task failures, system malfunctions, and frequent accidents caused by user errors are common. Therefore, it is necessary to study complex system interactions to enhance overall efficiency. This study focuses on the task interface of a digital twin system for computer numerical control (CNC) machine tools and examines the relationship between the user mental model and interface design elements. Key mental information is obtained through questionnaires and interviews, forming the basis for establishing a user mental model. High-frequency information words are extracted, experimental samples are designed, and importance rating surveys are conducted. Quantitative analysis methods, including factor analysis and weight calculation, are utilized to analyze the needs of target users. Consequently, a user demand hierarchy model is constructed. This approach aims to effectively reduce user errors in the human-computer interaction process within complex systems and enhance cognitive efficiency.

Investigation of biological activity of oxindole semicarbazones based copper (II) complexes: Synthesis, antimicrobial activities and molecular modelling

Copper (II) acetate reacted with 2-oxindole semicarbazone (2-Hoxsc, H1L), 3-methyl 2-oxindole semicarbazone (3-MeHoxsc, H2L) and 6-Chloro-2-oxindole semicarbazone (6-ClHoxsc, H3L) in 1:2 (M:L) molar ratio to form complexes of general formula, [Cu(L2)] (1L, 1; 2L 2; 3L 3. Stoichiometric ratio of complexes was established using UV–Vis spectroscopy. All the complexes were characterised by the CHN analysis, IR, ESR spectroscopy and Mass spectrometry. From the ESR spectrum, g values obtained (g‖ = 2.20; g⊥ = 2.05) for complex 2 confirms axial symmetry for this complex, whereas a broad isotropic signal in 1 and 3 (giso = 2.060, 1; 2.057, 3) indicates extensive exchange coupling. All the synthesized compounds (ligands and complexes) complexes were examined for their anti-tubercular activity against M. tuberculosis H37RV strain. Compounds were also tested for their anti-bacterial (B. subtilis, K. pneumonia) and antifungal (C. auris, C. albicans) activities. Biological investigations revealed that the antimicrobial activities (anti-TB, antibacterial and antifungal) of ligands get improved on complexation with Cu (II) due to formation of chelate ring, which can make the ligand a more powerful biological agent. Complex 3 has shown excellent anti-TB (MIC = 1.6 g/ml) and antibacterial (ZOI = 26 mm at 5 mg/mL) activities. Strong binding of complex 3 was observed (Kb = 24.22 × 105 M−1) with Human Serum Albumin (HSA) using fluorescence spectroscopy. Molecular modelling of complex 3 was also done with the active site of amino acid of M. tuberculosis enoyl reductase. The minimal binding energy of −10.1 kcal/mol indicated significant intermolecular interaction between M. tuberculosis enoyl reductase and complex 3 and is in well agreement with experimental data.