Large Complex Research Articles

Characterization of rapeseed protein supramolecular structures obtained by aqueous extractions

Aqueous extraction of rapeseed has the potential to produce mildly refined yet functional protein ingredients. However, the colloidal structures of these mildly refined ingredients are still not fully understood. Therefore, the aim of this work was to examine the structures present in rapeseed extracts by analyzing their scattering characteristics across different length scales. Rapeseed protein concentrates were obtained after aqueous extractions at acidic (pH 5.7) or alkaline (pH 8.5) conditions. The structures were investigated using complementary techniques, cryo-transmission electron microscopy, small angle X-ray scattering and small angle neutron scattering. To understand the scattering contribution of various components at characteristic length scales, D2O contrast variation was performed, and SAXS analysis for extracts at pH 5.7 treated with pectinase were compared to those untreated. All suspensions contained globular proteins within a network of soluble polysaccharides as observed by Cryo-TEM. SANS analysis with different D2O/H2O ratios demonstrated that at low q, both proteins and polysaccharide contributed to scattering, suggesting the presence of large complexes containing both. The signal for pH 8.5 suspensions was mainly attributed to oleosomes. SAXS measurements agreed with the SANS data, and pH 5.7 suspensions measured after pectin hydrolysis revealed that the complexes were formed by rapeseed globulins (∼4 nm) and polysaccharide chains. This research highlights the importance of understanding the protein-polysaccharide interactions occurring in rapeseed aqueous extracts, to fully utilize these less refined fractions as value-added ingredients in future sustainable foods.

Autoregulation ensures vertical transmission of the linear prophage GIL01

Betatectiviruses are prophages consisting of linear extrachromosomal genomes without obvious plasmid modules. It remains unclear how betatectiviruses are maintained in low-copy numbers in host cells and how they are vertically transmitted. Phage GIL01 is a model betatectivirus that infects the mosquito pathogen Bacillus thuringiensis serovar israelensis. Previous studies identified two closely spaced promoters, P1 and P2, responsible for the expression of GIL01 genes required for prophage replication and the switch from the lysogenic to lytic cycle. Here, we report that the GIL01-encoded 58-amino acid long gp1 protein forms a large nucleoprotein complex that represses its transcription from the strong promoter P2. Notably, ectopic expression of gp1 resulted in the loss of GIL01 in exponential cultures and immunized cells against infection with GIL01, indicating that gp1 plays a repressive role in the phage cycle. This finding is consistent with mutations in gp1 committing GIL01 to the lytic cycle and we show that maintenance of this phage variant in the bacterial population is contingent on the accumulation of deletions in the P1-P2 region. The fact that gp1 is conserved across most sequenced betatectiviruses suggests that the regulatory mechanism of gp1 that controls prophage maintenance is widespread among these bacteriophages.

3D Controlled‐source electromagnetic modelling in anisotropic media using secondary potentials and a cascadic multigrid solver

AbstractQuantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc.

Multi-source information fused loose particle localization and material identification method for sealed electronic equipment

Sealed electronic equipment are1 an important component of aerospace defense systems, and loose particles pose a significant threat to their reliable operation. Loose particle detection is crucial. For sealed electronic equipment with large scale and complex structure, loose particle detection should not only include the judgment of existence, but also obtain location and material information to facilitate the cleaning and control work. In this paper, the authors proposed a multi-source information fused loose particle localization and material identification method. Firstly, the equipment model was designed, the loose particle samples were made, and loose particle signals were collected. Secondly, the two-stage adaptive energy threshold pulse extraction algorithm was newly proposed to extract effective pulses, and the threshold-judgement-search pulse matching algorithm was improved to match the effective pulse groups. Next, spectrograms were transformed from effective pulses to create the localization and material image set. The time-domain, frequency-domain and gray-level co-occurrence matrix features were used to construct the joint feature library. Then, the channel-weighting feature selection method was used to create the localization and material data set. Finally, PReLU-VGG19-Plus was trained on the localization and material image set to obtain the optimal localization and material neural network, while parameter-optimized XGBoost was trained on the localization and material data set to obtain the optimal localization and material classifier. On this basis, combined with the triple majority voting process, the combined localization and material framework were constructed. Extensive test results show that, the location-identification achieved by combined localization framework and the material-identification accuracy achieved by combined material framework are all 100%. The feasibility, stability, and superiority of the method proposed in this paper have been fully verified. It is an important supplement to the existing loose particle detection research, providing important reference for signal detection and classification research in similar fields, and effectively improving the reliability of sealed electronic equipment.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

High-Fugacity Expansion and Crystallization in Non-sliding Hard-Core Lattice Particle Models Without a Tiling Constraint

In this paper, we prove the existence of a crystallization transition for a family of hard-core particle models on periodic graphs in dimension d≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d\\ge 2$$\\end{document}. We consider only models featuring a single species of particles, which in particular forbids the particles from rotation and reflection, and establish a criterion under which crystallization occurs at sufficiently high densities. The criterion is more general than that in Jauslin and Lebowitz (Commun Math Phys 364:655–682, 2018), as it allows models in which particles do not tile the space in the close-packing configurations, such as discrete hard-disk models. To prove crystallization, we prove that the pressure is analytic in the inverse of the fugacity for large enough complex fugacities, using Pirogov–Sinai theory. One of the main new tools used for this result is the definition of a local density, based on a discrete generalization of Voronoi cells. We illustrate the criterion by proving that it applies to three examples: staircase models and the radius 2.5 hard-disk model on Z2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb Z^{2}$$\\end{document}, and a heptacube model on Z3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb Z^{3}$$\\end{document}.

Development of crystal optics for X-ray multi-projection imaging for synchrotron and XFEL sources.

X-ray multi-projection imaging (XMPI) is an emerging experimental technique for the acquisition of rotation-free, time-resolved, volumetric information on stochastic processes. The technique is developed for high-brilliance light-source facilities, aiming to address known limitations of state-of-the-art imaging methods in the acquisition of 4D sample information, linked to their need for sample rotation. XMPI relies on a beam-splitting scheme, that illuminates a sample from multiple, angularly spaced viewpoints, and employs fast, indirect, X-ray imaging detectors for the collection of the data. This approach enables studies of previously inaccessible phenomena of industrial and societal relevance such as fractures in solids, propagation of shock waves, laser-based 3D printing, or even fast processes in the biological domain. In this work, we discuss in detail the beam-splitting scheme of XMPI. More specifically, we explore the relevant properties of X-ray splitter optics for their use in XMPI schemes, both at synchrotron insertion devices and XFEL facilities. Furthermore, we describe two distinct XMPI schemes, designed to faciliate large samples and complex sample environments. Finally, we present experimental proof of the feasibility of MHz-rate XMPI at the European XFEL. This detailed overview aims to state the challenges and the potential of XMPI and act as a stepping stone for future development of the technique.

Modelling pressure dynamics in a gas pipeline with an in-line sampling

Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure duringstart-up or shutdown and at specific extraction points. This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point. The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

Disruption of PHF6 Peptide Aggregation from Tau Protein: Mechanisms of Palmatine Chloride in Preventing Early PHF6 Aggregation.

The formation of neurofibrillary tangles (NFTs), composed of tau protein aggregates, is a hallmark of neurodegenerative diseases known as tauopathies, including Alzheimer's disease (AD). NFTs consist of paired helical filaments (PHFs) of tau protein with a dominant β-sheet secondary structure. Within these PHFs, the PHF6 hexapeptide (Val306-Gln-Ile-Val-Tyr-Lys311) has been commonly highlighted as a key site for tau protein nucleation. Palmatine chloride (PC) has been identified as an inhibitor of PHF6 aggregation, capable of reducing aggregation propensity at submicromolar concentrations. In pursuit of novel anti-AD drugs targeting early tau aggregation stages, we conducted an in silico study to elucidate PC's mechanism of action during PHF6 aggregation. Our observations suggest that while PHF6 can still initiate self-aggregation in the presence of PC, PC molecules subtly influence PHF6 aggregation dynamics, favoring smaller aggregates over larger complexes. The study underlined the key roles of aromatic rings in PC binding to different PHF6 aggregates by interacting through π-π stacking with the PHF6 Tyr310 side chain. The presence of aromatic rings in compounds to be able to inhibit the earlier complexation phase seems to be essential. These in silico findings lay a foundation for the design of compounds that could intervene in resolving the neurotoxicity of protein aggregates in AD.

Illuminating cellular architecture and dynamics with fluorescence polarization microscopy.

Ever since Robert Hooke's 17th century discovery of the cell using a humble compound microscope, light-matter interactions have continuously redefined our understanding of cell biology. Fluorescence microscopy has been particularly transformative and remains an indispensable tool for many cell biologists. The subcellular localization of biomolecules is now routinely visualized simply by manipulating the wavelength of light. Fluorescence polarization microscopy (FPM) extends these capabilities by exploiting another optical property - polarization - allowing researchers to measure not only the location of molecules, but also their organization or alignment within larger cellular structures. With only minor modifications to an existing fluorescence microscope, FPM can reveal the nanoscale architecture, orientational dynamics, conformational changes and interactions of fluorescently labeled molecules in their native cellular environments. Importantly, FPM excels at imaging systems that are challenging to study through traditional structural approaches, such as membranes, membrane proteins, cytoskeletal networks and large macromolecular complexes. In this Review, we discuss key discoveries enabled by FPM, compare and contrast the most common optical setups for FPM, and provide a theoretical and practical framework for researchers to apply this technique to their own research questions.

ExoSloNano: Multi-Modal Nanogold Tags for identification of Macromolecules in Live Cells & Cryo-Electron Tomograms.

In situ cryo-Electron Microscopy (cryo-EM) enables the direct interrogation of structure-function relationships by resolving macromolecular structures in their native cellular environment. Tremendous progress in sample preparation, imaging and data processing over the past decade has contributed to the identification and determination of large biomolecular complexes. However, the majority of proteins are of a size that still eludes identification in cellular cryo-EM data, and most proteins exist in low copy numbers. Therefore, novel tools are needed for cryo-EM to identify the vast majority of macromolecules across multiple size scales (from microns to nanometers). Here, we introduce and validate novel nanogold probes that enable the detection of specific proteins using cryo-ET (cryo-Electron Tomography) and resin-embedded correlated light and electron microscopy (CLEM). We demonstrate that these nanogold probes can be introduced into live cells, in a manner that preserves intact molecular networks and cell viability. We use this system to identify both cytoplasmic and nuclear proteins by room temperature EM, and resolve associated structures by cryo-ET. We further employ gold particles of different sizes to enable future multiplexed labeling and structural analysis. By providing high efficiency protein labeling in live cells and molecular specificity within cryo-ET tomograms, we establish a broadly enabling tool that significantly expands the proteome available to electron microscopy.

A cobalt coordination complex binds on a unique binding site between domain-I and domain-III of serum albumin

Serum albumin binding influences physiological effects and pharmacokinetics of drugs. Major binding sites in serum albumin are sudlow sites I and II, located on domains IIA and IIIA, respectively. However, groove between domain I and III as a binding site is not well studied. In this work, we studied the binding of a cobalt coordination complex (DCoP) with bovine serum albumin (BSA) using multi-spectroscopic methods and molecular docking. DCoP strongly interacts with BSA with association constant of ∼7 × 105M−1. The binding was found to significantly alter protein's conformation and unfolding pathway. Results of this study shows that DCoP binds at the groove formed between domains III and I. We conclude that the groove is a rare binding site for large molecules and coordination complexes. The knowledge of unique binding site of serum albumin enlightens the comprehensive role of protein as a carrier biomacromolecule.

Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes.

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently.

Learned liquid crystal microlens array for joint optimized deep optical architecture in identifying metameric materials.

Multispectral imaging holds great promise for the detection of metameric materials. However, traditional multispectral imaging systems are characterized by their large volume, complex structure, and high computational requirements, limiting their practical application. We propose a jointly optimized deep optical architecture that combines the liquid crystal (LC) microlens array (MLA) characteristics and a multi-level perceptual spectral reconstruction network (MLP-SRN). The core of the architecture is to integrate the physical properties of the LC-MLA into the MLP-SRN using point spread function (PSF) optical convolution kernels, decoupling the light-field characteristic information collected by the LC-MLA at different voltages. Experimental results demonstrate that the incorporation of the physical properties of the LC-MLA not only reduces the system size and computational complexity but demonstrates excellent performance in identifying a metameric material.

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and heart palpitations, and is therefore of particular interest. The large size and complexity of patterns in time series data have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection. It provides a taxonomy based on anomaly detection strategies and deep learning models. Aside from describing the basic anomaly detection techniques in each category, their advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. Finally, it summarises open issues in research and challenges faced while adopting deep anomaly detection models to time series data.

Evaluation of aerial thermography for measuring the thermal transmittance (U-value) of a building façade

In efforts to mitigate greenhouse gas emissions in the construction sector, policies promoting energy efficiency improvements in existing buildings are being implemented. Accurately assessing the impact of energy retrofit strategies requires precise determination of thermal envelope performance through energy audits. Aerial thermography, employing unmanned aerial vehicles (UAVs) equipped with infrared thermal cameras (IRT), is an underused method overcoming limitations in energy audits of large buildings or complexes. This approach offers an elevated perspective, providing an overview of the building’s thermal patterns, aiding in the identification of thermal envelope defects. By measuring temperature distribution, even in inaccessible envelope areas, the severity of identified defects can be estimated. Beyond detecting thermal bridges, air leaks, and moisture, aerial thermography is valid for estimating thermal transmittance (U-value) of enclosures. The aim of this research is to evaluate this method to calculate the U-value of the façade of an university building located in a Subtropical Mediterranean climate, during a typical full day in different seasons of the year. Concurrently with aerial thermography, the thermometric method (THM) was applied to compare results and refine the operational conditions during U-value measurement using different statistical indices and uncertainty analysis. The results demonstrated the validation of airborne thermography as a method for U-value calculation under specific meteorological and operational conditions, overcoming the common physical limitations of traditional building audit methods.

Evolutionary origins and innovations sculpting the mammalian PRPS enzyme complex.

The phosphoribosyl pyrophosphate synthetase (PRPS) enzyme conducts a chokepoint reaction connecting central carbon metabolism and nucleotide production pathways, making it essential for life 1,2 . Here, we show that the presence of multiple PRPS-encoding genes is a hallmark trait of eukaryotes, and we trace the evolutionary origins and define the individual functions of each of the five mammalian PRPS homologs - three isozymes (one testis-restricted) 3,4 and two non-enzymatic associated proteins (APs) 5,6 - which we demonstrate operate together as a large molecular weight complex capable of attaining a heterogeneous array of functional multimeric configurations. Employing a repertoire of isogenic fibroblast clones in all viable individual or combinatorial assembly states, we define preferential interactions between subunits, and we show that cells lacking PRPS2, PRPSAP1, and PRPSAP2 render PRPS1 into aberrant homo-oligomeric assemblies with diminished metabolic flux and impaired proliferative capacity. We demonstrate how numerous evolutionary innovations in the duplicated genes have created specialized roles for individual complex members and identify translational control mechanisms that enable fine-tuned regulation of PRPS assembly and activity, which provide clues into the positive and negative selective pressures that facilitate metabolic flexibility and tissue specialization in advanced lifeforms. Collectively, our study demonstrates how evolution has transformed a single PRPS gene into a multimeric complex endowed with functional and regulatory features that govern cellular biochemistry.

The genome of a giant clam zooxanthella (Cladocopium infistulum) offers few clues to adaptation as an extracellular symbiont with high thermotolerance

BackgroundCladocopium infistulum (Symbiodiniaceae) is a dinoflagellate specialized to live in symbiosis with western Pacific giant clams (Tridacnidae). Unlike coral-associated symbionts, which reside within the host cells, C. infistulum inhabits the extracellular spaces of the clam’s digestive diverticula. It is phylogenetically basal to a large species complex of stress-tolerant Cladocopium, many of which are associated with important reef-building corals in the genus Porites. This close phylogenetic relationship may explain why C. infistulum exhibits high thermotolerance relative to other tridacnid symbionts. Moreover, past analyses of microsatellite loci indicated that Cladocopium underwent whole-genome duplication prior to the adaptive radiations that led to its present diversity.ResultsA draft genome assembly of C. infistulum was produced using long- and short-read sequences to explore the genomic basis for adaptations underlying thermotolerance and extracellular symbiosis among dinoflagellates and to look for evidence of genome duplication. Comparison to three other Cladocopium genomes revealed no obvious over-representation of gene groups or families whose functions would be important for maintaining C. infistulum’s unique physiological and ecological properties. Preliminary analyses support the existence of partial or whole-genome duplication among Cladocopium, but additional high-quality genomes are required to substantiate these findings.ConclusionAlthough this investigation of Cladocopium infistulum revealed no patterns diagnostic of heat tolerance or extracellular symbiosis in terms of overrepresentation of gene functions or genes under selection, it provided a valuable genomic resource for comparative analyses. It also indicates that ecological divergence among Cladocopium species, and potentially among other dinoflagellates, is partially governed by mechanisms other than gene content. Thus, additional high-quality, multiomic data are needed to explore the molecular basis of key phenotypes among symbiotic microalgae.

Cracking the chaperone code through the computational microscope.

The heat shock protein 90 kDa (Hsp90) chaperone machinery plays a crucial role in maintaining cellular homeostasis. Beyond its traditional role in protein folding, Hsp90 is integral to key pathways influencing cellular function in health and disease. Hsp90 operates through the modular assembly of large multiprotein complexes, with their composition, stability, and localization adapting to the cell's needs. Its functional dynamics are finely tuned by ligand binding and post-translational modifications (PTMs). Here, we discuss how to disentangle the intricacies of the complex code that governs the crosstalk between dynamics, binding, PTMs, and the functions of the Hsp90 machinery using computer-based approaches. Specifically, we outline the contributions of computational and theoretical methods to the understanding of Hsp90 functions, ranging from providing atomic-level insights into its dynamics to clarifying the mechanisms of interactions with protein clients, cochaperones, and ligands. The knowledge generated in this framework can be actionable for the design and development of chemical tools and drugs targeting Hsp90 in specific disease-associated cellular contexts. Finally, we provide our perspective on how computation can be integrated into the study of the fine-tuning of functions in the highly complex Hsp90 landscape, complementing experimental methods for a comprehensive understanding of this important chaperone system.

Assessing the limitations of relief-based algorithms in detecting higher-order interactions

BackgroundEpistasis, the interaction between genetic loci where the effect of one locus is influenced by one or more other loci, plays a crucial role in the genetic architecture of complex traits. However, as the number of loci considered increases, the investigation of epistasis becomes exponentially more complex, making the selection of key features vital for effective downstream analyses. Relief-Based Algorithms (RBAs) are often employed for this purpose due to their reputation as “interaction-sensitive” algorithms and uniquely non-exhaustive approach. However, the limitations of RBAs in detecting interactions, particularly those involving multiple loci, have not been thoroughly defined. This study seeks to address this gap by evaluating the efficiency of RBAs in detecting higher-order epistatic interactions. Motivated by previous findings that suggest some RBAs may rank predictive features involved in higher-order epistasis negatively, we explore the potential of absolute value ranking of RBA feature weights as an alternative approach for capturing complex interactions. In this study, we assess the performance of ReliefF, MultiSURF, and MultiSURFstar on simulated genetic datasets that model various patterns of genotype-phenotype associations, including 2-way to 5-way genetic interactions, and compare their performance to two control methods: a random shuffle and mutual information.ResultsOur findings indicate that while RBAs effectively identify lower-order (2 to 3-way) interactions, their capability to detect higher-order interactions is significantly limited, primarily by large feature count but also by signal noise. Specifically, we observe that RBAs are successful in detecting fully penetrant 4-way XOR interactions using an absolute value ranking approach, but this is restricted to datasets with only 20 total features.ConclusionsThese results highlight the inherent limitations of current RBAs and underscore the need for the development of Relief-based approaches with enhanced detection capabilities for the investigation of epistasis, particularly in datasets with large feature counts and complex higher-order interactions.